Smarter Faster Better is a work of nonfiction. Nonetheless, some names and personal characteristics of individuals or events have been changed in order to disguise identities. Any resulting resemblance to persons living or dead is entirely coincidental and unintentional.

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Names: Duhigg, Charles, author.

Title: Smarter faster better: the secrets of productivity in life and business / Charles Duhigg.

Description: New York : Random House, [2016] | Includes bibliographical references and index.

Identifiers: LCCN 2015034214| ISBN 9780812993394 | ISBN 9780679645429 (ebook) Subjects:

LCSH: Mental efficiency. | Performance. | Motivation (Psychology) | Decision making. | Success.

Classification: LCC BF431 .D8185 2016 | DDC 158—dc23 LC record available at

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eBook ISBN 9780679645429

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CONTENTS

Cover

Title Page

Copyright

INTRODUCTION

1. MOTIVATION

Reimagining Boot Camp, Nursing Home Rebellions, and the Locus of Control

2. TEAMS

Psychological Safety at Google and Saturday Night Live

3. FOCUS

Cognitive Tunneling, Air France Flight 447, and the Power of Mental Models

4. GOAL SETTING

Smart Goals, Stretch Goals, and the Yom Kippur War

5. MANAGING OTHERS

Solving a Kidnapping with Lean and Agile Thinking and a Culture of Trust

6. DECISION MAKING

Forecasting the Future (and Winning at Poker) with Bayesian Psychology

7. INNOVATION

How Idea Brokers and Creative Desperation Saved Disney’s Frozen

8. ABSORBING DATA

Turning Information into Knowledge in Cincinnati’s Public Schools

APPENDIX:

A Reader’s Guide to Using These Ideas

Dedication

Acknowledgments

A Note on Sources

Notes

By Charles Duhigg

About the Author

INTRODUCTION

My introduction to the science of productivity began in the summer of 2011,

when I asked a friend of a friend for a favor.

At the time, I was finishing a book about the neurology and psychology of

habit formation. I was in the final, frantic stages of the writing process—a

flurry of phone calls, panicked rewrites, last-minute edits—and felt like I was

falling farther and farther behind. My wife, who worked full-time, had just

given birth to our second child. I was an investigative reporter at The New York Times and spent my days chasing stories and my nights rewriting book

pages. My life felt like a treadmill of to-do lists, emails requiring immediate

replies, rushed meetings, and subsequent apologies for being late.

Amid all this hustle and scurry—and under the guise of asking for a little

publishing advice—I sent a note to an author I admired, a friend of one of my

colleagues at the Times. The author’s name was Atul Gawande, and he

appeared to be a paragon of success. He was a forty-six-year-old staff writer

at a prestigious magazine, as well as a renowned surgeon at one of the

nation’s top hospitals. He was an associate professor at Harvard, an adviser to

the World Health Organization, and the founder of a nonprofit that sent

surgical supplies to medically underserved parts of the world. He had written

three books—all bestsellers—and was married with three children. In 2006,

he had been awarded a MacArthur “genius” grant—and had promptly given a

substantial portion of the $500,000 prize to charity.

There are some people who pretend at productivity, whose résumés appear

impressive until you realize their greatest talent is self marketing. Then there

are others, like Gawande, who seem to exist on a different plane of getting

things done. His articles were smart and engaging, and, by all accounts, he

was gifted in the operating room, committed to his patients, and a devoted

father. Whenever he was interviewed on television, he appeared relaxed and

thoughtful. His accomplishments in medicine, writing, and public health were important and real.

I emailed him to ask if he had some time to talk. I wanted to know how he

managed to be so productive. Mainly, what was his secret? And, if I learned

it, could I change my own life?

“Productivity,” of course, means different things in different settings. One

person might spend an hour exercising in the morning before dropping the

kids at school and consider the day a success. Another might opt to use that

time locked in her office, returning emails and calling a few clients, and feel

equally accomplished. A research scientist or artist may see productivity in

failed experiments or discarded canvases since each mistake, they hope, gets

them closer to discovery, while an engineer’s measure of productivity might

focus on making an assembly line ever faster. A productive weekend might

involve walking through the park with your kids, while a productive workday

involves rushing them to daycare and getting to the office as early as you can.

Productivity, put simply, is the name we give our attempts to figure out the

best uses of our energy, intellect, and time as we try to seize the most

meaningful rewards with the least wasted effort. It’s a process of learning

how to succeed with less stress and struggle. It’s about getting things done

without sacrificing everything we care about along the way.

By this definition, Atul Gawande seemed to have things pretty well figured

out.

A few days later, he responded to my email with his regrets. “I wish I

could help,” he wrote, “but I’m running flat out with my various

commitments.” Even he, it seemed, had limits. “I hope you’ll understand.”

Later that week, I mentioned this exchange to our mutual friend. I made it

clear I wasn’t offended—that, in fact, I admired Gawande’s focus. I imagined

his days were consumed with healing patients, teaching medical students,

writing articles, and advising the world’s largest health organization.

No, my friend told me, I had it wrong. That wasn’t it. Gawande was

particularly busy that week because he had bought tickets to a rock concert

with his kids. And then he was heading on a mini-vacation with his wife.

In fact, Gawande had suggested to our mutual friend that I should email

him again, later that month, when he would have more time in his schedule

for chatting.

At that moment, I realized two things:

First, I was clearly doing something wrong because I hadn’t taken a day

off in nine months; in fact, I was growing worried that, given a choice

between their father and the babysitter, my kids would pick the sitter.

Second, and more important, there were people out there who knew how to

be more productive. I just had to convince them to share their secrets with

me.

This book is the result of my investigations into how productivity works, and

my effort to understand why some people and companies are so much more

productive than everyone else.

Since I contacted Gawande four years ago, I’ve sought out neurologists,

businesspeople, government leaders, psychologists, and other productivity

experts. I’ve spoken to the filmmakers behind Disney’s Frozen, and learned

how they made one of the most successful movies in history under crushing

time pressures—and narrowly averted disaster—by fostering a certain kind of

creative tension within their ranks. I talked to data scientists at Google and writers from the early seasons of Saturday Night Live who said both

organizations were successful, in part, because they abided by a similar set of

unwritten rules regarding mutual support and risk taking. I interviewed FBI

agents who solved a kidnapping through agile management and a culture

influenced by an old auto plant in Fremont, California. I roamed the halls of

Cincinnati’s public schools and saw how an initiative to improve education

transformed students’ lives by, paradoxically, making information more

difficult to absorb.

As I spoke to people—poker players, airline pilots, military generals,

executives, cognitive scientists—a handful of key insights began to emerge. I

noticed that people kept mentioning the same concepts over and over. I came

to believe a small number of ideas were at the core of why some people and

companies get so much done.

This book, then, explores the eight ideas that seem most important in

expanding productivity. One chapter, for example, examines how a feeling of

control can generate motivation, and how the military turns directionless teenagers into marines by teaching them choices that are “biased toward

action.” Another chapter looks at why we can maintain focus by constructing

mental models—and how one group of pilots told themselves stories that

prevented 440 passengers from falling out of the sky.

This book’s chapters describe the correct way to set goals—by embracing

both big ambitions and small-bore objectives—and why Israel’s leaders

became so obsessed with the wrong aspirations in the run-up to the Yom

Kippur War. They explore the importance of making decisions by

envisioning the future as multiple possibilities rather than fixating on what

you hope will happen, and how a woman used that technique to win a

national poker championship. They describe how some Silicon Valley

companies became giants by building “commitment cultures” that supported

employees even when such commitment gets hard.

Connecting these eight ideas is a powerful underlying principle:

Productivity isn’t about working more or sweating harder. It’s not simply a

product of spending longer hours at your desk or making bigger sacrifices.

Rather, productivity is about making certain choices in certain ways. The

way we choose to see ourselves and frame daily decisions; the stories we tell

ourselves, and the easy goals we ignore; the sense of community we build

among teammates; the creative cultures we establish as leaders: These are the

things that separate the merely busy from the genuinely productive.

We now exist in a world where we can communicate with coworkers at

any hour, access vital documents over smartphones, learn any fact within

seconds, and have almost any product delivered to our doorstep within

twenty-four hours. Companies can design gadgets in California, collect

orders from customers in Barcelona, email blueprints to Shenzhen, and track

deliveries from anywhere on earth. Parents can auto-sync the family’s

schedules, pay bills online while lying in bed, and locate the kids’ phones one

minute after curfew. We are living through an economic and social revolution

that is as profound, in many ways, as the agrarian and industrial revolutions

of previous eras.

These advances in communications and technology are supposed to make

our lives easier. Instead, they often seem to fill our days with more work and

stress.

In part, that’s because we’ve been paying attention to the wrong innovations. We’ve been staring at the tools of productivity—the gadgets and

apps and complicated filing systems for keeping track of various to-do lists—

rather than the lessons those technologies are trying to teach us.

There are some people, however, who have figured out how to master this

changing world. There are some companies that have discovered how to find

advantages amid these rapid shifts.

We now know how productivity really functions. We know which choices

matter most and bring success within closer reach. We know how to set goals

that make the audacious achievable; how to reframe situations so that instead

of seeing problems, we notice hidden opportunities; how to open our minds

to new, creative connections; and how to learn faster by slowing down the

data that is speeding past us.

This is a book about how to recognize the choices that fuel true

productivity. It is a guide to the science, techniques, and opportunities that have changed lives. There are people who have learned how to succeed with

less effort. There are companies that create amazing things with less waste.

There are leaders who transform the people around them.

This is a book about how to become smarter, faster, and better at

everything you do.

MOTIVATION

Reimagining Boot Camp, Nursing Home Rebellions, and the Locus of

Control

The trip was intended as a celebration, a twenty-nine-day tour of South

America that would take Robert, who had just turned sixty, and his wife,

Viola, first to Brazil, then over the Andes into Bolivia and Peru. Their

itinerary included tours of Incan ruins, a boat trip on Lake Titicaca, the

occasional craft market, and a bit of birding.

That much relaxation, Robert had joked with friends before leaving,

seemed unsafe. He was already anticipating the fortune he would spend on

calls to his secretary. Over the previous half century Robert Philippe had built

a small gas station into an auto parts empire in rural Louisiana and had made

himself into a Bayou mogul through hard work, charisma, and hustle. In

addition to the auto-parts business, he also owned a chemical company, a

paper supplier, various swaths of land, and a real estate firm. And now here

he was, entering his seventh decade, and his wife had convinced him to spend

a month in a bunch of countries where, he suspected, it would be awfully

difficult to find a TV showing the LSU–Ole Miss game.

Robert liked to say there wasn’t a dirt road or back alley along the Gulf

Coast he hadn’t driven at least once to drum up business. As Philippe

Incorporated had grown, Robert had become famous for dragging big-city

businessmen from New Orleans and Atlanta out to ramshackle bars and

forbidding them from leaving until the ribs were picked clean and bottles

sucked dry. Then, while everyone nursed painful hangovers the next

morning, Robert would convince them to sign deals worth millions.

Bartenders always knew to fill his glass with club soda while serving the

bigwigs cocktails. Robert hadn’t touched booze in years.

He was a member of the Knights of Columbus and the chamber of

commerce, past president of the Louisiana Association of Wholesalers and the Greater Baton Rouge Port Commission, the chairman of his local bank,

and a loyal donor to whichever political party was more inclined to endorse

his business permits that day. “You never met a man who loved working so

much,” his daughter, Roxann, told me.

Robert and Viola had been looking forward to this South American trip.

But when they stepped off the plane in La Paz, midway through the

monthlong tour, Robert started acting oddly. He staggered through the airport

and had to sit down to catch his breath at the baggage claim. When a group of

children approached him to ask for coins, Robert threw change at their feet

and laughed. In the bus to the hotel, Robert started a loud, rambling

monologue about various countries he had visited and the relative

attractiveness of the women who lived there. Maybe it was the altitude. At

twelve thousand feet, La Paz is one of the highest cities in the world.

Once they were unpacked, Viola urged Robert to nap. He wasn’t

interested, he said. He wanted to go out. For the next hour, he marched

through town buying trinkets and exploding in a rage whenever locals didn’t

understand English. He eventually agreed to return to the hotel and fell

asleep, but woke repeatedly during the night to vomit. The next morning, he

said he felt faint but became angry when Viola suggested he rest. He spent

the third day in bed. On day four, Viola decided enough was enough and cut

the vacation short.

Back home in Louisiana, Robert seemed to improve. His disorientation

faded and he stopped saying strange things. His wife and children, however,

were still worried. Robert was lethargic and refused to leave the house unless

prodded. Viola had expected him to rush into the office upon their return, but

after four days he hadn’t so much as checked in with his secretary. When

Viola reminded him that deer hunting season was approaching and he’d need

to get a license, Robert said he thought he’d skip it this year. She phoned a

doctor. Soon, they were driving to the Ochsner Clinic in New Orleans.

The chief of neurology, Dr. Richard Strub, put Robert through a battery of

tests. Vital signs were normal. Blood work showed nothing unusual. No

indication of infection, diabetes, heart attack, or stroke. Robert demonstrated

understanding of that day’s newspaper and could clearly recall his childhood.

He could interpret a short story. The Revised Wechsler Adult Intelligence

Scale showed a normal IQ.

“Can you describe your business to me?” Dr. Strub asked.

Robert explained how his company was organized and the details of a few

contracts they had recently won.

“Your wife says you’re behaving differently,” Dr. Strub said.

“Yeah,” Robert replied. “I don’t seem to have as much get-up-and-go as I

used to.”

“It didn’t seem to bother him,” Dr. Strub later told me. “He told me about

the personality changes very matter of fact, like he was describing the

weather.”

Except for the sudden apathy, Dr. Strub couldn’t find evidence of illness or

injury. He suggested to Viola they wait a few weeks to see if Robert’s

disposition improved. When they returned a month later, however, there had

been no change. Robert wasn’t interested in seeing old friends, his wife said.

He didn’t read anymore. Previously, it had been infuriating to watch

television with him because he would flip from channel to channel, looking

for a more exciting show. Now, he just stared at the screen, indifferent to

what was on. She had finally convinced him to go into the office, but his

secretary said he spent hours at his desk gazing into space.

“Are you unhappy or depressed?” Dr. Strub asked.

“No,” Robert said. “I feel good.”

“Can you tell me how you spent yesterday?”

Robert described a day of watching television.

“You know, your wife tells me your employees are concerned because

they don’t see you around the office much,” said Dr. Strub.

“I guess I’m more interested in other things now,” Robert replied.

“Like what?”

“Oh, I don’t know,” Robert said, and then went silent and stared at the

wall.

Dr. Strub prescribed various medications—drugs to combat hormonal

imbalances and attention disorders—but none seemed to make a difference.

People suffering from depression will say they are unhappy and describe

hopeless thoughts. Robert, however, said he was satisfied with life. He

admitted his personality change was odd, but it didn’t upset him.

Dr. Strub administered an MRI, which allowed him to collect images from

inside Robert’s cranium. Deep inside his skull, near the center of Robert’s

head, he saw a small shadow, evidence that burst vessels had caused a tiny

amount of blood to pool temporarily inside a part of Robert’s brain known as

the striatum. Such injuries, in rare cases, can cause brain damage or mood

swings. But except for the listlessness, there was little in Robert’s behavior to

suggest that he was suffering any neurological disability.

A year later, Dr. Strub submitted an article to the Archives of Neurology.

Robert’s “behavior change was characterized by apathy and lack of

motivation,” he wrote. “He has given up his hobbies and fails to make timely

decisions in his work. He knows what actions are required in his business, yet

he procrastinates and leaves details unattended. Depression is not present.”

The cause of this passivity, Dr. Strub suggested, was the slight damage in his

brain, which had possibly been triggered by Bolivia’s altitude. Even that,

however, was uncertain. “It is possible that the hemorrhages are coincidental

and that the high altitude played no physiologic role.”

It was an interesting but ultimately inconclusive case, Dr. Strub wrote.

Over the next two decades, a handful of other studies appeared in medical

journals. There was the sixty-year-old professor who experienced a rapid

“decrease in interest.” He had been an expert in his field with a fierce work

ethic. Then, one day, he simply stopped. “I just lack spirit, energy,” he told

his physician. “I have no go. I must force myself to get up in the morning.”

There was a nineteen-year-old woman who had fallen briefly unconscious

after a carbon monoxide leak and then seemed to lose motivation for the most

basic tasks. She would sit in one position all day unless forced to move. Her

father learned he couldn’t leave her alone, as a neurologist wrote, when she

“was found by her parents with heavy sunburns on the beach at the very same

place where she laid down several hours before, under an umbrella: intense

inertia had prevented her from changing her position with that of the shadow

while the sun had turned around.”

There was a retired police officer who began waking up “late in the

morning, would not wash unless urged to do so, but meekly complied as soon as his wife asked him to. Then he would sit in his armchair, from which he

would not move.” There was a middle-aged man who was stung by a wasp

and, not long after, lost the desire to interact with his wife, children, and business associates.

In the late 1980s, a French neurologist in Marseille named Michel Habib

heard about a few of these cases, became intrigued, and started searching

archives and journals for similar stories. The studies he found were rare but

consistent: A relative would bring a patient in for an examination,

complaining of a sudden change in behavior and passivity. Doctors would

find nothing medically wrong. The patients scored normally when tested for

mental illness. They had moderate to high IQs and appeared physically

healthy. None of them said they felt depressed or complained about their

apathy.

Habib began contacting the physicians treating these patients and asked

them to collect MRIs. He then discovered another commonality: All the

apathetic individuals had tiny pinpricks of burst vessels in their striatum, the

same place where Robert had a small shadow inside his skull.

The striatum serves as a kind of central dispatch for the brain, relaying

commands from areas like the prefrontal cortex, where decisions are made, to

an older part of our neurology, the basal ganglia, where movement and

emotions emerge. Neurologists believe the striatum helps translate decisions

into action and plays an important role in regulating our moods. The damage

from the burst vessels inside the apathetic patients’ striata was small—too

small, some of Habib’s colleagues said, to explain their behavior changes.

Beyond those pinpricks, however, Habib could find nothing else to explain

why their motivation had disappeared.

Neurologists have long been interested in striatal injuries because the

striatum is involved in Parkinson’s disease. But whereas Parkinson’s often

causes tremors, a loss of physical control, and depression, the patients Habib

studied only seemed to lose their drive. “Parkinsonians have trouble initiating

movement,” Habib told me. “But the apathetic patients had no problems with

motion. It’s just that they had no desire to move.” The nineteen-year-old

woman who couldn’t be left alone at the beach, for example, was able to

clean her room, wash the dishes, fold the laundry, and follow recipes when

instructed to do so by her mother. However, if she wasn’t asked to help, she wouldn’t move all day. When her mother inquired what she wanted for

dinner, the woman said she had no preferences.

When examined by doctors, Habib wrote, the apathetic sixty-year-old

professor would “stay motionless and speechless during endless periods,

sitting in front of the examiner, waiting for the first question.” When asked to

describe his work, he could discuss complicated ideas and quote papers from

memory. Then he would lapse back into silence until another question was

posed.

None of the patients Habib studied responded to medications, and none

seemed to improve with counseling. “Patients demonstrate a more or less

total indifference to life events that would normally provoke an emotional

response, positive or negative,” Habib wrote.

“It was as if the part of their brain where motivation lives, where élan vital

is stored, had completely disappeared,” he told me. “There were no negative

thoughts, there were no positive thoughts. There were no thoughts at all.

They hadn’t become less intelligent or less aware of the world. Their old

personalities were still inside, but there was a total absence of drive or

momentum. Their motivation was completely gone.”

II.

The room where the experiment was conducted at the University of

Pittsburgh was painted a cheery yellow and contained an fMRI machine, a

computer monitor, and a smiling researcher who looked too young to have a

PhD. All participants in the study were welcomed into the room, asked to

remove their jewelry and any metal from their pockets, and then told to lie on

a plastic table that slid into the fMRI.

Once lying down, they could see a computer screen. The researcher

explained that a number between one and nine was going to appear on the

monitor. Before that number appeared, participants had to guess if it was

going to be higher or lower than five by pressing various buttons. There

would be multiple rounds of guessing, the researcher said. There was no skill

involved in this game, he explained. No abilities were being tested. And

though he didn’t mention this to the participants, the researcher thought this

was one of the most boring games in existence. In fact, he had explicitly

designed it that way.

The truth was, the researcher, Mauricio Delgado, didn’t care if participants

guessed right or wrong. Rather, he was interested in understanding which

parts of their brains became active as they played an intensely dull game. As

they made their guesses, the fMRI was recording the activity inside their

skulls. Delgado wanted to identify where the neurological sensations of

excitement and anticipation—where motivation—originated. Delgado told

participants they could quit whenever they wanted. Yet he knew, from prior

experience, that people would make guess after guess, sometimes for hours,

as they waited to see if they had guessed wrong or right.

Each participant lay inside the machine and watched the screen intently.

They hit buttons and made predictions. Some cheered when they won or

moaned when they lost. Delgado, monitoring the activity inside of their

heads, saw that people’s striata—that central dispatch—lit up with activity

whenever participants played, regardless of the outcome. This kind of striatal

activity, Delgado knew, was associated with emotional reactions—in

particular, with feelings of expectation and excitement.

As Delgado was finishing one session, a participant asked if he could

continue playing on his own, at home.

“I don’t think that’s possible,” Delgado told him, explaining that the game

only existed on his computer. Besides, he said, letting the man in on a secret,

the experiment was rigged. To make sure the game was consistent from

person to person, Delgado had programmed the computer so that everyone

won the first round, lost the second, won the third, lost the fourth, and so on,

in a predetermined pattern. The outcome had been determined ahead of time.

It was like betting on a two-headed quarter.

“That’s okay,” the man replied. “I don’t mind. I just like to play.”

“It was odd,” Delgado told me later. “There’s no reason he should have

wanted to continue playing once he knew it was rigged. I mean, where’s the

fun in a rigged game? Your choices have no impact. But it took me five

minutes to convince him he didn’t want to take the game home.”

For days afterward, Delgado kept thinking about that man. Why had this

game interested him so much? For that matter, why had it entertained so

many other participants? The experiment’s data had helped Delgado identify

which parts of people’s brains became active as they played a guessing game,

but the data didn’t explain why they were motivated to play in the first place.

So a few years later, Delgado set up another experiment. A new set of

participants was recruited. Like before, there was a guessing game. This time,

however, there was a key difference: Half the time, participants were allowed

to make their own guesses; the rest of the time, the computer guessed for

them.

As people began playing, Delgado watched the activity in their striata. This

time, when people were allowed to make their own choices, their brains lit up

just like in the previous experiment. They showed the neurological

equivalents of anticipation and excitement. But during those rounds when

participants didn’t have any control over their guesses, when the computer

made a choice for them, people’s striata went essentially silent. It was as if their brains became uninterested in the exercise. There was “robust activity in

the caudate nucleus only when subjects” were permitted to guess, Delgado

and his colleagues later wrote. “The anticipation of choice itself was

associated with increased activity in corticostriatal regions, particularly the ventral striatum, involved in affective and motivational processes.”

What’s more, when Delgado asked participants about their perceptions of

the game afterward, they said they enjoyed themselves much more when they

were in control of their choices. They cared whether they won or lost. When

the computer was in charge, they said, the experiment felt like an assignment.

They got bored and wanted it to end.

That didn’t make sense to Delgado. The odds of winning or losing were

exactly the same regardless of whether the participant or the computer was in

control. Allowing someone to make a guess, rather than waiting for a

computer to make a guess for them, shouldn’t have made any real difference

in the experience of the game. People’s neurological reactions should have been the same either way. But, somehow, allowing people to make choices

transformed the game. Instead of being a chore, the experiment became a

challenge. Participants were more motivated to play simply because they

believed they were in control.

III.

In recent decades, as the economy has shifted and large companies promising

lifelong employment have given way to freelance jobs and migratory careers,

understanding motivation has become increasingly important. In 1980, more

than 90 percent of the American workforce reported to a boss. Today more

than a third of working Americans are freelancers, contractors, or in

otherwise transitory positions. The workers who have succeeded in this new

economy are those who know how to decide for themselves how to spend

their time and allocate their energy. They understand how to set goals,

prioritize tasks, and make choices about which projects to pursue. People

who know how to self-motivate, according to studies, earn more money than

their peers, report higher levels of happiness, and say they are more satisfied

with their families, jobs, and lives.

Self-help books and leadership manuals often portray self-motivation as a

static feature of our personality or the outcome of a neurological calculus in

which we subconsciously compare efforts versus rewards. But scientists say

motivation is more complicated than that. Motivation is more like a skill,

akin to reading or writing, that can be learned and honed. Scientists have

found that people can get better at self-motivation if they practice the right way. The trick, researchers say, is realizing that a prerequisite to motivation

is believing we have authority over our actions and surroundings. To

motivate ourselves, we must feel like we are in control.

“The need for control is a biological imperative,” a group of Columbia

University psychologists wrote in the journal Trends in Cognitive Sciences in

2010. When people believe they are in control, they tend to work harder and

push themselves more. They are, on average, more confident and overcome

setbacks faster. People who believe they have authority over themselves often

live longer than their peers. This instinct for control is so central to how our

brains develop that infants, once they learn to feed themselves, will resist

adults’ attempts at control even if submission is more likely to get food into

their mouths.

One way to prove to ourselves that we are in control is by making decisions. “Each choice—no matter how small—reinforces the perception of

control and self-efficacy,” the Columbia researchers wrote. Even if making a

decision delivers no benefit, people still want the freedom to choose.

“Animals and humans demonstrate a preference for choice over non-choice,

even when that choice confers no additional reward,” Delgado noted in a

paper published in the journal Psychological Science in 2011.

From these insights, a theory of motivation has emerged: The first step in

creating drive is giving people opportunities to make choices that provide

them with a sense of autonomy and self-determination. In experiments,

people are more motivated to complete difficult tasks when those chores are

presented as decisions rather than commands. That’s one of the reasons why

your cable company asks all those questions when you sign up for service. If

they ask if you prefer a paperless bill to an itemized statement, or the ultra package versus the platinum lineup, or HBO to Showtime, you’re more likely

to be motivated to pay the bill each month. As long as we feel a sense of control, we’re more willing to play along.

“You know when you’re stuck in traffic on the freeway and you see an exit

approaching, and you want to take it even though you know it’ll probably

take longer to get home?” said Delgado. “That’s our brains getting excited by

the possibility of taking control. You won’t get home any faster, but it feels

better because you feel like you’re in charge.”

This is a useful lesson for anyone hoping to motivate themselves or others,

because it suggests an easy method for triggering the will to act: Find a

choice, almost any choice, that allows you to exert control. If you are

struggling to answer a tedious stream of emails, decide to reply to one from

the middle of your inbox. If you’re trying to start an assignment, write the conclusion first, or start by making the graphics, or do whatever’s most

interesting to you. To find the motivation to confront an unpleasant

employee, choose where the meeting is going to occur. To start the next sales

call, decide what question you’ll ask first.

Motivation is triggered by making choices that demonstrate to ourselves

that we are in control. The specific choice we make matters less than the

assertion of control. It’s this feeling of self-determination that gets us going.

That’s why Delgado’s participants were willing to play again and again when

they felt like they were in charge.

Which is not to say that motivation is, therefore, always easy. In fact,

sometimes simply making a choice isn’t enough. Occasionally, to really self-

motivate, we need something more.

IV.

After Eric Quintanilla signed his name to the form that officially made him a

U.S. Marine, the recruiter shook his hand, looked him in the eye, and said he

had made the right choice.

“It’s the only one I see for myself, sir,” Quintanilla replied. He had meant

the words to sound bold and confident, but his voice quavered when he spoke

and his hand was so sweaty that both of them wiped their palms on their

pants afterward.

Quintanilla was twenty-three years old. Five years earlier, he had

graduated from high school in a small town an hour south of Chicago. He had

thought about going away to college, but he wasn’t certain what to study,

wasn’t positive what he wanted to do afterward—wasn’t sure about much, to

be honest. So he enrolled at a local community college and got an associate’s

degree in general studies. He had hoped it would help him get a job at a

cellphone store in the mall. “I filled out, I don’t know, like ten applications,”

Quintanilla said. “But I never heard back from anyone.”

He found part-time work at a hobby supply shop and occasionally drove an

ice truck when the regular guy was sick or on vacation. At night, he played

World of Warcraft. This wasn’t how Quintanilla had envisioned his life. He

was ready for something better. He decided to propose to the girl he had been

dating since high school. The wedding was fantastic. Afterward, though, he

was still in the same place. And then his wife got pregnant. He tried the

cellphone stores once more and scored an interview. He rehearsed with his

wife the night before his appointment.

“Honey,” she told him, “you have to give them a reason to hire you. Just

tell them what you’re excited about.”

The next day, when the store manager asked him why he wanted to sell T-

Mobile phones, Quintanilla froze. “I don’t know,” he said. It was the truth.

He had no idea.

A few weeks later, Quintanilla went to a party and saw one of his former

classmates, freshly home from basic training and twenty pounds lighter, with bulging muscles and a newfound sense of confidence. He was telling jokes

and hitting on girls. Maybe, Quintanilla said to his wife the next morning, he

should consider the Marines. She didn’t like the idea, and neither did his

mom, but Quintanilla couldn’t think of anything else to do. He sat down one

night at the kitchen table, drew a line down the center of a piece of paper, wrote “Marine Corps” on the left side and tried to fill the right with other options. The only thing he could come up with was “Get promoted at the

hobby store.”

Five months later, he arrived at the San Diego Marine Corps Recruit Depot

in the middle of the night, shuffled into a room alongside eighty other young

men, had his head shaved, his blood type tested, his clothes replaced with

fatigues, and embarked on a new life.

The thirteen-week boot camp Quintanilla entered in 2010 was a relatively

new experiment in the Corps’ 235-year-old quest to manufacture the perfect

marine. For most of its history, the service’s training program had focused on

molding rowdy teenagers into disciplined troops. But fifteen years before

Quintanilla’s enlistment, a fifty-three-year-old general named Charles C.

Krulak had been promoted to commandant, the Marines’ top position. Krulak

believed basic training needed to change. “We were seeing much weaker

applicants,” he told me. “A lot of these kids didn’t just need discipline, they

needed a mental makeover. They’d never belonged to a sports team, they’d

never had a real job, they’d never done anything. They didn’t even have the

vocabulary for ambition. They’d followed instructions their whole life.”

This was a problem, because the Corps increasingly needed troops who

could make independent decisions. Marines—as they will happily tell you—

are different from soldiers and sailors. “We’re the first to arrive and the last

to leave,” Krulak said. “We need extreme self-starters.” In today’s world, that

means the Corps requires men and women capable of fighting in places such

as Somalia and Baghdad, where rules and tactics change unpredictably and

marines often have to decide—on their own and in real time—the best course

of action.

“I began spending time with psychologists and psychiatrists, trying to

figure out, how do we do a better job teaching these recruits to think for

themselves?” Krulak said. “We had great recruits coming in, but they didn’t

have any sense of direction or drive. All they knew was doing the bare minimum. It was like working with a bunch of wet socks. Marines can’t be

wet socks.”

Krulak began reviewing studies on how to teach self-motivation, and

became particularly intrigued by research, conducted by the Corps years

earlier, showing that the most successful marines were those with a strong

“internal locus of control”—a belief they could influence their destiny

through the choices they made.

Locus of control has been a major topic of study within psychology since

the 1950s. Researchers have found that people with an internal locus of

control tend to praise or blame themselves for success or failure, rather than

assigning responsibility to things outside their influence. A student with a

strong internal locus of control, for instance, will attribute good grades to

hard work, rather than natural smarts. A salesman with an internal locus of

control will blame a lost sale on his own lack of hustle, rather than bad

fortune.

“Internal locus of control has been linked with academic success, higher

self-motivation and social maturity, lower incidences of stress and

depression, and longer life span,” a team of psychologists wrote in the journal

Problems and Perspectives in Management in 2012. People with an internal

locus of control tend to earn more money, have more friends, stay married

longer, and report greater professional success and satisfaction.

In contrast, having an external locus of control—believing that your life is

primarily influenced by events outside your control—“is correlated with

higher levels of stress, [often] because an individual perceives the situation as

beyond his or her coping abilities,” the team of psychologists wrote.

Studies show that someone’s locus of control can be influenced through

training and feedback. One experiment conducted in 1998, for example,

presented 128 fifth graders with a series of difficult puzzles. Afterward, each

student was told they had scored very well. Half of them were also told, “You

must have worked hard at these problems.” Telling fifth graders they have

worked hard has been shown to activate their internal locus of control, because hard work is something we decide to do. Complimenting students for

hard work reinforces their belief that they have control over themselves and

their surroundings.

The other half of the students were also informed they had scored well, and then told, “You must be really smart at these problems.” Complimenting

students on their intelligence activates an external locus of control. Most fifth graders don’t believe they can choose how smart they are. In general, young

kids think that intelligence is an innate capacity, so telling young people they

are smart reinforces their belief that success or failure is based on factors outside of their control.

Then all the students were invited to work on three more puzzles of

varying difficulty.

The students who had been praised for their intelligence—who had been

primed to think in terms of things they could not influence—were much more

likely to focus on the easier puzzles during the second round of play, even

though they had been complimented for being smart. They were less

motivated to push themselves. They later said the experiment wasn’t much

fun.

In contrast, students who had been praised for their hard work—who were

encouraged to frame the experience in terms of self-determination—went to

the hard puzzles. They worked longer and scored better. They later said they

had a great time.

“Internal locus of control is a learned skill,” Carol Dweck, the Stanford

psychologist who helped conduct that study, told me. “Most of us learn it

early in life. But some people’s sense of self-determination gets suppressed

by how they grow up, or experiences they’ve had, and they forget how much

influence they can have on their own lives.

“That’s when training is helpful, because if you put people in situations

where they can practice feeling in control, where that internal locus of control is reawakened, then people can start building habits that make them

feel like they’re in charge of their own lives—and the more they feel that

way, the more they really are in control of themselves.”

For Krulak, studies like this seemed to hold the key to teaching recruits

self-motivation. If he could redesign basic training to force trainees to take control of their own choices, that impulse might become more automatic, he

hoped. “Today we call it teaching ‘a bias toward action,’ ” Krulak told me.

“The idea is that once recruits have taken control of a few situations, they start to learn how good it feels.

“We never tell anyone they’re a natural-born leader. ‘Natural born’ means it’s outside your control,” Krulak said. “Instead, we teach them that

leadership is learned, it’s the product of effort. We push recruits to experience

that thrill of taking control, of feeling the rush of being in charge. Once we

get them addicted to that, they’re hooked.”

For Quintanilla, this tutorial started as soon as he arrived. Initially, there were long days of forced marches, endless sit-ups and push-ups, and tedious

rifle drills. Instructors screamed at him constantly. (“We’ve got an image to

uphold,” Krulak told me.) But alongside those exercises, Quintanilla also

confronted a steady stream of situations that forced him to make decisions

and take control.

In his fourth week of training, for instance, Quintanilla’s platoon was told

to clean the mess hall. The recruits had no idea how. They didn’t know where

the cleaning supplies were located or how the industrial dishwasher worked.

Lunch had just ended and they weren’t sure if they were supposed to wrap

the leftovers or throw them away. Whenever someone approached a drill

instructor for advice, all he received was a scowl. So the platoon began

making choices. The potato salad got tossed, the leftover hamburgers went

into the fridge, and the dishwasher was loaded with so much detergent that

suds soon covered the floor. It took three and a half hours, including the time

spent mopping up the bubbles, for the platoon to finish cleaning the mess

hall. They mistakenly threw away edible food, accidentally turned off the ice

cream freezer, and somehow managed to misplace two dozen forks. When

they were done, however, their drill instructor approached the smallest,

shyest member of the platoon and said he had noticed how the recruit had

asserted himself when a decision was needed on where to put the ketchup. In

truth, it was pretty obvious where the ketchup should have gone. There was a

huge set of shelves containing nothing but ketchup bottles. But the shy recruit

beamed as he was praised.

“I hand out a number of compliments, and all of them are designed to be

unexpected,” said Sergeant Dennis Joy, a thoroughly intimidating drill

instructor who showed me around the Recruit Depot one day. “You’ll never

get rewarded for doing what’s easy for you. If you’re an athlete, I’ll never compliment you on a good run. Only the small guy gets congratulated for

running fast. Only the shy guy gets recognized for stepping into a leadership

role. We praise people for doing things that are hard. That’s how they learn to

believe they can do them.”

The centerpiece of Krulak’s redesigned basic training was the Crucible, a

grueling three-day challenge at the end of boot camp. Quintanilla was

terrified of the Crucible. He and his bunkmates whispered about it at night.

There were rumors and wild conjectures. Someone said a recruit had lost a

limb midway through the course the previous year. Quintanilla’s Crucible

began on a Tuesday morning when his platoon was woken at two A.M. and

told they should prepare themselves to march, crawl, and climb across fifty

miles of obstacle courses. Each person carried thirty pounds of gear. They

had only two meals apiece to last fifty-four hours. At most, they could hope

for just a few hours of sleep. Injuries were expected. Anyone who stopped

moving or lagged too far behind, they were told, would be dropped from the

Corps.

Midway through the Crucible, the recruits encountered a task called

Sergeant Timmerman’s Tank. “The enemy has chemically contaminated this

area,” a drill instructor shouted, pointing to a pit the size of a football field.

“You must cross it while wearing full gear and gas masks. If a recruit touches

the ground, you have failed and must start over. If you spend longer than

sixty minutes in the pit, you have failed and must start over. You must obey

your team leader. I repeat: You may not proceed without a direct verbal order

from the team leader. You must hear a command before you act, otherwise you have failed and must start over.”

Quintanilla’s team formed a circle and began using a technique they had

learned in basic training.

“What’s our objective?” one recruit said.

“To cross the pit,” someone replied.

“How do we use the boards?” another recruit said, pointing to planks with

ropes attached.

“We could lay them end to end,” someone answered. The team leader

issued a verbal order and the circle broke up to test this idea along the border

of the pit. They stood on one board while hauling the other forward. No one

could keep their balance. The circle reformed. “How do we use the ropes?” a

recruit asked.

“To lift the planks,” another said. He suggested standing on both boards

simultaneously and using the ropes to lift each piece in tandem, as if on skis.

Everyone put on their gas masks and stood on the boards with the leader at

the front. “Left!” he shouted as recruits pulled one of the planks slightly

forward. “Right!” They began shuffling across the pit. After ten minutes,

however, it was clear this wasn’t working. Some people were lifting too

quickly, others were pushing the boards too far. And because they were all

wearing gas masks, it was impossible to hear the leader’s commands. They

had already gone too far to turn around—but at this rate, crossing would take

hours. Recruits began yelling at each other to stop.

The leader ordered a pause. He turned to the man behind him. “Watch my

shoulders,” he yelled through his gas mask. The leader shrugged his left

shoulder, and then his right. By watching the rhythm of the leader, the recruit

behind him could coordinate how to lift the boards. The only problem with

this idea was that it violated one of the ground rules. Recruits had been told

they could not act until they heard a verbal command from their team leader.

But with their gas masks on, no one could really hear anything. However,

there was no other way to proceed. So the team leader began shrugging and

swinging his arms while screaming orders. No one caught on at first, so he

began yelling one of the songs they had learned on long marches. The recruit

behind him could make out enough of what he was singing to join in. His

neighbor did the same. Eventually, they were all singing and shrugging and

swinging in tandem. They crossed the field in twenty-eight minutes.

“Technically, we could send them back to start over because each person

didn’t hear a direct verbal command from the team leader,” a drill sergeant

later told me. “But that’s the point of the exercise: We know you can’t hear

anything with the gas masks on. The only way to get across the pit is to figure

out some workaround. We’re trying to teach them that you can’t just obey

orders. You have to take control and figure things out for yourself.”

Twenty-four hours and another dozen obstacles later, Quintanilla’s platoon

gathered at the base of the Crucible’s final challenge, a long, steep hill they

called the Grim Reaper. “You don’t have to help each other during the Reaper,” Krulak said. “I’ve seen that happen before. Recruits fall down, and

they don’t have buddies, so they get left behind.”

Quintanilla had been marching for two days by this point. He had slept less

than four hours. His face was numb and his hands were covered with blisters

and cuts from carrying water-filled drums across obstacles. “There were guys

throwing up at the Reaper,” he told me. “One person had his arm in a sling.”

As the group began walking up the mountain, recruits kept stumbling. They

were all so exhausted they moved as if in slow motion, hardly making any

progress. So they began linking up, arm in arm, to prevent one another from

sliding down the incline.

“Why are you doing this?” Quintanilla’s pack buddy wheezed at him,

lapsing into a call-and-response they had practiced on hikes. When things are

at their most miserable, their drill instructors had said, they should ask each

other questions that begin with “why.”

“To become a Marine and build a better life for my family,” Quintanilla

said.

His wife had given birth a week earlier to a daughter, Zoey. He had been

allowed to speak to her for a total of five minutes by telephone after the

delivery. It was his only contact with the outside world in almost two months.

If he finished the Crucible, he would see his wife and new child.

If you can link something hard to a choice you care about, it makes the

task easier, Quintanilla’s drill instructors had told him. That’s why they asked

each other questions starting with “why.” Make a chore into a meaningful

decision, and self-motivation will emerge.

The platoon summited the last peak as the sun crested, and staggered to a

clearing with a flagpole. Everyone went still. They were finally done. The Crucible was over. A drill instructor walked through their formation, pausing

before each man to place the service’s insignia, the Eagle, Globe, and

Anchor, in their hands. They were officially marines.

“You think boot camp is going to be all screaming and fighting,”

Quintanilla told me. “But it’s not. It’s not like that at all. It’s more about learning how to make yourself do things you thought you couldn’t do. It’s

really emotional, actually.”

Basic training, like the Marine Corps career itself, offers few material

rewards. A Marine’s starting salary is $17,616 a year. However, the Corps

has one of the highest career satisfaction rates. The training the Corps

provides to roughly forty thousand recruits each year has transformed the

lives of millions of people who, like Quintanilla, had no idea how to generate

the motivation and self-direction needed to take control of their lives. Since

Krulak’s reforms, the Corps’ retention of new recruits and the performance

scores of new marines have both increased by more than 20 percent. Surveys

indicate that the average recruit’s internal locus of control increases

significantly during basic training. Delgado’s experiments were a start to

understanding motivation. The Marines complement those insights by

helping us understand how to teach drive to people who aren’t practiced in

self-determination: If you give people an opportunity to feel a sense of

control and let them practice making choices, they can learn to exert

willpower. Once people know how to make self-directed choices into a habit,

motivation becomes more automatic.

Moreover, to teach ourselves to self-motivate more easily, we need to learn

to see our choices not just as expressions of control but also as affirmations

of our values and goals. That’s the reason recruits ask each other “why”—

because it shows them how to link small tasks to larger aspirations.

The significance of this insight can be seen in a series of studies conducted

in nursing homes in the 1990s. Researchers were studying why some seniors

thrived inside such facilities, while others experienced rapid physical and

mental declines. A critical difference, the researchers determined, was that

the seniors who flourished made choices that rebelled against the rigid

schedules, set menus, and strict rules that the nursing homes tried to force

upon them.

Some researchers referred to such residents as “subversives,” because so many of their decisions manifested as small rebellions against the status quo.

One group at a Santa Fe nursing home, for instance, started every meal by

trading food items among themselves in order to construct meals of their own

design rather than placidly accept what had been served to them. One resident

told a researcher that he always gave his cake away because, even though he

liked cake, he would “rather eat a second-class meal that I have chosen.”

A group of residents at a nursing home in Little Rock violated the

institution’s rules by moving furniture around to personalize their bedrooms.

Because wardrobes were attached to the walls, they used a crowbar—

appropriated from a tool closet—to wrench their dressers free. In response, an

administrator called a meeting and said there was no need to undertake

independent redecorations; if the residents needed help, the staff would

provide it. The residents informed the administrator that they didn’t want any

assistance, didn’t need permission, and intended to continue doing whatever

they damn well pleased.

These small acts of defiance were, in the grand scheme of things, relatively

minor. But they were psychologically powerful because the subversives saw

the rebellions as evidence that they were still in control of their own lives.

The subversives walked, on average, about twice as much as other nursing

home residents. They ate about a third more. They were better at complying

with doctors’ orders, taking their medications, visiting the gym, and

maintaining relationships with family and friends. These residents had

arrived at the nursing homes with just as many health problems as their peers,

but once inside, they lived longer, reported higher levels of happiness, and

were far more active and intellectually engaged.

“It’s the difference between making decisions that prove to yourself that

you’re still in charge of your life, versus falling into a mindset where you’re

just waiting to die,” said Rosalie Kane, a gerontologist at the University of Minnesota. “It doesn’t really matter if you eat cake or not. But if you refuse

to eat their cake, you’re demonstrating to yourself that you’re still in charge.”

The subversives thrived because they knew how to take control, the same

way that Quintanilla’s troop learned to subversively cross a pit during the

Crucible by deciding, on their own, how to interpret the rules.

The choices that are most powerful in generating motivation, in other

words, are decisions that do two things: They convince us we’re in control and they endow our actions with larger meaning. Choosing to climb a

mountain can become an articulation of love for a daughter. Deciding to stage

a nursing home insurrection can become proof that you’re still alive. An

internal locus of control emerges when we develop a mental habit of

transforming chores into meaningful choices, when we assert that we have authority over our lives.

Quintanilla finished boot camp in 2010 and served in the Corps for three

years. He then left. He was finally ready, he felt, for real life. He got another

job, but the lack of camaraderie among his colleagues was disappointing. No

one seemed motivated to excel. So in 2015, he reenlisted. “I missed that

constant reminder that I can do anything,” he told me. “I missed people

pushing me to choose a better me.”

V.

Viola Philippe, the wife of the onetime auto parts tycoon of Louisiana, was

something of an expert on motivation herself before she and Robert flew to

South America. She had been born with albinism—her body did not produce

the enzyme tyrosinase, critical in the production of melanin—and as a result,

her skin, hair, and eyes contained no pigment, and her eyesight was poor. She

was legally blind, and could read only by putting her face very close to a

page and using a magnifying glass. “You have never met a more determined

person, though,” her daughter, Roxann, told me. “She could do anything.”

When Viola was a girl, the school district had tried to put her into remedial

classes despite the fact that it was her eyes, not her brain, that had problems.

But she refused to leave the classroom where her friends sat. She stayed in

that room until administrators relented. After she graduated, she went to

Louisiana State University and told the school she expected them to provide

someone to read textbooks to her aloud. The school complied. During her

sophomore year, she met Robert, who soon dropped out to start washing and

greasing cars for a local Ford dealer. He encouraged her to quit school, as

well. She politely declined and got her degree. They were married in

December 1950, four months after she graduated.

They had six children in rapid succession, and while Robert built his

empire, Viola ran the household. There were morning meetings and charts

showing what each child had to accomplish each day. There were Friday

night check-ins, during which everyone laid out their goals for the coming

week. “They were like two peas in a pod, both totally driven,” said Roxann.

“Mom refused to let her disabilities stop her. I think that’s why it was so hard

for her when Dad changed.”

When Robert’s apathy took hold, Viola initially focused her energy on

caring for him. She hired nurses to help him exercise, and worked with his

brother to form a committee to oversee and then sell off Robert’s companies.

After a while, however, she ran out of things to do. She had married a bon vivant, a man so full of life that it was hard to go to the grocery store because

he constantly stopped to chat with everyone. Now Robert sat in a chair in front of the television all day. Viola was miserable. “He didn’t speak to me,”

she told a courtroom when the family sued for insurance money they felt

owed because of Robert’s neurological injuries. “He wasn’t—it didn’t seem

like he was interested in anything I did. You know, I would fix his meals and

I was more or less a caretaker. I guess you would call me a caretaker.”

For a few years, she felt sorry for herself. Then she became angry. Then

busy. If Robert wasn’t going to show any motivation to reclaim his life, then

she would force him to get moving again. She would make him engage. She

began by asking him ceaseless questions. When she made lunch, she would

pepper him with choices. Sandwich or soup? Lettuce or tomato? Ham or

turkey? What about mayonnaise? Ice water or juice? She didn’t really mean

anything by it at first. She was just frustrated and wanted to make him speak.

But then, after a few months of harassing him, Viola found that whenever

Robert was pressured into making decisions, he seemed to come out of his

shell a little bit. He would banter with her for a few moments, or tell her about a program he had been watching. One night, after she had forced him

to make a dozen choices about what he was going to eat and which table they

should sit at and what music to listen to, he began talking at length,

reminding her of a funny story from after they had gotten married, when they

had locked themselves out of the house in a rainstorm. He told the story in an

offhand way, and chuckled as he recalled trying to jimmy a window. It was

the first time Viola had heard him laugh in years. For a few minutes, it was

like the old Robert was back. Then he faced the TV and went silent again.

Viola continued her campaign, and over time, more and more of the old

Robert emerged. Viola congratulated, cajoled, and rewarded him whenever

he seemed, for a moment, like his former self. When he went back to Dr.

Strub, the neurologist in New Orleans, for his annual checkup seven years

after the trip to South America, the doctor could see the difference. “He was

saying hello to the nurses, and asking them about their kids,” Dr. Strub said.

“He would initiate conversations with me, ask about my hobbies. He had

opinions on the route they should drive to get home. It was stuff you

wouldn’t have noticed with anyone else, but with him, it was like someone

was turning on the lights again.”

As neurologists have studied how motivation functions within our brains,

they’ve become increasingly convinced that people like Robert don’t lose their drive because they’ve lost the capacity for self-motivation. Rather, their

apathy is due to an emotional dysfunction. One of the things Habib, the French researcher, noticed about all the people he studied was that they

shared an odd emotional detachment. One apathetic woman told him she had

hardly reacted when her father died. A man said he hadn’t felt the urge to hug

his wife or children since the passivity had taken hold. When Habib asked

patients if they felt sad about how much their lives had changed, they all said

no. They didn’t feel anything.

Neurologists have suggested that this emotional numbness is why some

people feel no motivation. Among Habib’s patients, the injuries in their

striata prevented them from feeling the sense of reward that comes from

taking control. Their motivation went dormant because they had forgotten

how good it feels to make a choice. In other situations, it’s that people have

never learned what it feels like to be self-determined, because they have

grown up in a neighborhood that seems to offer so few choices or they have

forgotten the rewards of autonomy since they’ve moved into a nursing home.

This theory suggests how we can help ourselves and others strengthen our

internal locus of control. We should reward initiative, congratulate people for

self-motivation, celebrate when an infant wants to feed herself. We should

applaud a child who shows defiant, self-righteous stubbornness and reward a

student who finds a way to get things done by working around the rules.

This is easier in theory, of course, than practice. We all applaud self-

motivation until a toddler won’t put on his shoes, an aged parent is ripping a

dresser out of the wall, or a teenager ignores the rules. But that’s how an internal locus of control becomes stronger. That’s how our mind learns and

remembers how good it feels to be in control. And unless we practice self-determination and give ourselves emotional rewards for subversive

assertiveness, our capacity for self-motivation can fade.

What’s more, we need to prove to ourselves that our choices are

meaningful. When we start a new task, or confront an unpleasant chore, we

should take a moment to ask ourselves “why.” Why are we forcing ourselves

to climb up this hill? Why are we pushing ourselves to walk away from the

television? Why is it so important to return that email or deal with a coworker

whose requests seem so unimportant?

Once we start asking why, those small tasks become pieces of a larger constellation of meaningful projects, goals, and values. We start to recognize

how small chores can have outsized emotional rewards, because they prove

to ourselves that we are making meaningful choices, that we are genuinely in

control of our own lives. That’s when self-motivation flourishes: when we

realize that replying to an email or helping a coworker, on its own, might be

relatively unimportant. But it is part of a bigger project that we believe in, that we want to achieve, that we have chosen to do. Self-motivation, in other

words, is a choice we make because it is part of something bigger and more

emotionally rewarding than the immediate task that needs doing.

In 2010, twenty-two years after her South American vacation with Robert,

Viola was diagnosed with ovarian cancer. It took two years for the disease to

consume her. At every step, Robert was there, helping her out of bed in the

morning and reminding her to take her medications at night. He asked her

questions to distract her from the pain and fed her when she became feeble.

When Viola finally passed, Robert sat by her empty bed for days. His

children, worried he was slipping back into apathy, suggested another visit

with the neurologist in New Orleans. Perhaps the doctor would recommend

something to forestall his listlessness from returning.

No, Robert replied. It wasn’t apathy keeping him indoors. He just needed

some time to reflect on sixty-two years of marriage. Viola had helped Robert

build a life—and then, when everything had slipped from his grasp, she

helped him rebuild it again. He just wanted to honor that by pausing for a few

days, he told his kids. A week later, he left the house and came over for

brunch. Afterward, he babysat his grandchildren. Robert passed away twenty-

four months later, in 2014. He was active, his obituary noted, until the end.

TEAMS

Psychological Safety at Google and Saturday Night Live

Julia Rozovsky was twenty-five years old and uncertain what to do with her

life when she decided it was time for a change. She was a Tufts graduate with

a bachelor’s degree in math and economics who had previously worked at a

consulting firm, which she found unfulfilling. Then she had become a

researcher for two professors at Harvard, which was fun but not a long-term

career.

Maybe, she thought, she belonged in a big corporation. Or perhaps she

ought to become an academic. Or maybe she should join a tech start-up. It

was all very confusing to her. So she picked the option that meant she didn’t

have to decide: She applied to business schools, and was accepted to start at

the Yale School of Management in 2010.

She showed up in New Haven ready to bond with her classmates and, like

all new students, was assigned to a study group. This group, she figured,

would be an important part of her education. They would become close

friends and learn together, debate important issues, and discover, with each

other’s help, who they were meant to be.

Study groups are a rite of passage at most MBA programs, a way for

students to practice working in teams. At Yale, “each study group shares the

same class schedule and collaborates on each group assignment,” one of the

school’s websites explained. “Study groups have been carefully constructed

to bring together students with diverse backgrounds, both professionally and

culturally.” Each day during lunch or after dinner, Julia and the four other

members of her study group would gather to discuss homework and compare

spreadsheets, strategize for upcoming exams, and trade lecture notes. Truth

be told, her group wasn’t all that diverse. Two of them had been management

consultants, like Julia. Another had worked at a start-up. They were all smart

and curious and outgoing. Their similarities, she hoped, would make it easy for them to bond. “There are lots of people who say some of their best

business school friends come from their study groups,” said Julia. “But it

wasn’t like that for me.”

Almost from the start, study group felt like a daily dose of stress. “I never

felt completely relaxed,” she told me. “I always felt like I had to prove

myself.” Dynamics quickly emerged that put her on edge. Everyone wanted

to show they were leaders, and so when teachers issued study group

assignments, there were subtle tussles over who was in charge. “People

would try to show authority by speaking louder, or talking over each other,”

Julia said. When it came to divvying up tasks for projects, one group member

would sometimes preemptively assign roles, and then the others would

critique those assignments, and then someone else would claim authority over

some part of the project, and then everyone else would rush to grab their own

piece. “Maybe it was my own insecurities, but I always felt like I had to be

careful not to make mistakes around them,” said Julia. “People were critical

of each other, but they would play it off like they were making a joke, and so

the group was kind of passive-aggressive.

“I was looking forward to making friends with my group,” she said. “It

really bummed me out that we didn’t gel.”

So Julia started looking for other groups to join, other ways to connect

with classmates. One person mentioned that some students were putting

together a team to participate in “case competitions,” in which business

school students proposed innovative solutions to real-world business

problems. Teams would receive a case study, spend a few weeks writing a

business plan, and then submit it to high-profile executives and professors

who picked the winner. Companies sponsored these contests and there were

cash prizes as well as, sometimes, jobs that came out of the competitions.

Julia signed up.

Yale hosted about a dozen different case competition teams. The one Julia

joined included a former army officer, a think tank researcher, the director of

a health education nonprofit, and a refugee program manager. Unlike her

study group, everyone was from different backgrounds. From the start,

though, they all clicked. Each time a new case arrived, the team would gather

in the library and dive into action, spending hours brainstorming options,

assigning research duties, and divvying up writing assignments. Then they would meet again and again and again. “One of the best cases we did was

about Yale itself,” Julia said. “There had always been a student-run snack

store, but the university was taking over food sales, and so the business

school sponsored a contest to overhaul the shop.

“We met every night for a week. I thought we should fill the shop with nap

pods, and someone else said it should become a game room, and there was

also some kind of clothing swap idea. We had lots of crazy ideas.” No one

ever shot down a suggestion, not even the nap pods. Julia’s study group, as

part of their class assignments, had also engaged in a fair amount of

brainstorming, “but if I had ever mentioned something like a nap pod,

somebody would have rolled their eyes and come up with fifteen reasons why

it was a dumb idea. And it was a dumb idea. But my case team loved it. We

always loved each other’s dumb ideas. We spent an hour figuring out how

nap pods could make money by selling accessories like earplugs.”

Eventually, Julia’s case team settled on the idea of converting the student

shop into a micro-gym with a handful of exercise classes and a few workout

machines. They spent weeks researching pricing models and contacting

equipment manufacturers. They won the competition and the micro-gym

exists today. That same year, Julia’s case team spent another month studying

ways for a chain of eco-friendly convenience stores to expand into North

Carolina. “We must have analyzed two dozen plans,” she said. “A lot of them

turned out not to make any sense.” When the team traveled to Portland,

Oregon, to present their final suggestion—a slow-growth approach that

emphasized the chain’s healthy food options—they placed first in the nation.

Julia’s study group dissolved sometime in her second semester after one

person, and then another, and then everyone stopped showing up. The case

competition team grew as new students asked if they could join. The core

group of five teammates, including Julia, remained involved the entire time

they were at Yale. Today, these people are some of her closest friends. They

attend one another’s weddings and visit each other when traveling. They call

each other for career advice and pass along job leads.

It always struck Julia as odd that those two teams felt so different. Her study group felt stressful because everyone was always jousting for

leadership and critiquing each other’s ideas. Her case competition team felt

exciting because everyone was so supportive and enthusiastic. Both groups,

however, were composed of basically the same kinds of people. They were

all bright, and everyone was friendly outside of the team settings. There was

no reason why the dynamic inside Julia’s study group needed to become so

competitive, while the culture of the case team was so easygoing.

“I couldn’t figure out why things had turned out so different,” Julia told

me. “It didn’t seem like it had to happen that way.”

After graduation, Julia went to work at Google and joined its People

Analytics group, which was tasked with studying nearly every aspect of how

employees spent their time. What she was supposed to do with her life, it

turned out, was use data to figure out why people behave in certain ways.

For six years running, Google had been ranked by Fortune as one of

America’s top workplaces. The company’s executives believed that was

because, even as it had grown to fifty-three thousand employees, Google had

devoted enormous resources to studying workers’ happiness and productivity.

The People Analytics group, part of Google’s human resources division,

helped examine if employees were satisfied with their bosses and coworkers,

whether they felt overworked, intellectually challenged, and fairly paid,

whether their work-life balance was actually balancing out, as well as

hundreds of other variables. The division helped with hiring and firing

decisions, and its analysts provided insights into who should be promoted and

who, perhaps, had risen too fast. In the years before Julia joined the group, People Analytics had determined that Google needed to interview a job

applicant only four times to predict, with 86 percent confidence, if they

would be a good hire. The division had successfully pushed to increase paid

maternity leave from twelve to eighteen weeks because computer models

indicated that would reduce the frequency of new mothers quitting by 50

percent. At the most basic level, the division’s goal was to make life at

Google a little bit better and a lot more productive. With enough data, People

Analytics believed, almost any behavioral puzzle could be solved.

People Analytics’ biggest undertaking in recent years had been a study—

code-named Project Oxygen before it was revealed—that examined why

some managers were more effective than others. Ultimately, researchers had

identified eight critical management skills.*1 “Oxygen was a huge success for us,” said Abeer Dubey, a People Analytics manager. “It helped clarify what

differentiated good managers from everyone else and how we could help

people improve.” The project was so useful, in fact, that at about the same

time Julia was hired, Google began another massive effort, this one code-

named Project Aristotle.

Dubey and his colleagues had noticed that many Google employees, in

company surveys, had consistently mentioned the importance of their teams.

“Googlers would say things like ‘I have a great manager, but my team has

never clicked’ or ‘My manager isn’t fantastic, but the team is so strong it

doesn’t matter,’ ” said Dubey. “And that was kind of eye opening, because

Project Oxygen had looked at leadership, but it hadn’t focused on how teams

function, or if there’s an optimal mix of different kinds of people or

backgrounds.” Dubey and his colleagues wanted to figure out how to build

the perfect team. Julia became one of the effort’s researchers.

The project started with a sweeping review of academic literature. Some

scientists had found that teams functioned best when they contained a

concentration of people with similar levels of extroversion and introversion,

while others had found that a balance of personalities was key. There were

studies about the importance of teammates having similar tastes and hobbies,

and others lauding diversity within groups. Some research suggested that

teams needed people who like to collaborate; others said groups were more

successful when individuals had healthy rivalries. The literature, in other

words, was all over the place.

So Project Aristotle spent more than 150 hours asking Google employees

what they thought made a team effective. “We learned that teams are

somewhat in the eye of the beholder,” said Dubey. “One group might appear

like it’s working really well from the outside, but, inside, everyone is

miserable.” Eventually, they established criteria for measuring teams’

effectiveness based on external factors, such as whether a group hit their sales

targets, as well as internal variables, such as how productive team members

felt. Then the Aristotle group began measuring everything they could.

Researchers examined how often teammates socialized outside of work and

how members divided up tasks. They drew complicated diagrams to show

teams’ overlapping memberships, and then compared those against statistics

of which groups had exceeded their department’s goals. They studied how long teams stuck together and if gender balance had an impact on

effectiveness.

No matter how they arranged the data, though, it was almost impossible to

find patterns—or any evidence that a team’s composition was correlated with

its success. “We looked at 180 teams from all over the company,” said

Dubey. “We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference. The

‘who’ part of the equation didn’t seem to matter.”

Some productive Google teams, for instance, were composed of friends

who played sports together outside of work. Others were made up of people

who were basically strangers away from the conference room. Some groups

preferred strong managers. Others wanted a flatter structure. Most

confounding of all, sometimes two teams would have nearly identical

compositions, with overlapping memberships, but radically different levels of

effectiveness. “At Google, we’re good at finding patterns,” said Dubey.

“There weren’t strong patterns here.”

So Project Aristotle turned to a different approach. There was a second

body of academic research that focused on what are known as “group

norms.” “Any group, over time, develops collective norms about appropriate

behavior,” a team of psychologists had written in the Sociology of Sport

Journal. Norms are the traditions, behavioral standards, and unwritten rules that govern how we function. When a team comes to an unspoken consensus

that avoiding disagreement is more valuable than debate, that’s a norm

asserting itself. If a team develops a culture that encourages differences of

opinion and spurns groupthink, that’s another norm holding sway. Team

members might behave certain ways as individuals—they may chafe against

authority or prefer working independently—but often, inside a group, there’s

a set of norms that override those preferences and encourage deference to the

team.

The Project Aristotle researchers went back to their data and analyzed it

again, this time looking for norms. They found that some teams consistently

allowed people to interrupt one another. Others enforced taking

conversational turns. Some teams celebrated birthdays and began each

meeting with a few minutes of informal chitchat. Others got right to business.

There were teams that contained extroverts who hewed to the group’s sedate norms whenever they assembled, and others where introverts came out of

their shells as soon as meetings began.

And some norms, the data indicated, consistently correlated with high team

effectiveness. One engineer, for instance, told the researchers that his team

leader “is direct and straightforward, which creates a safe space for you to

take risks….She also takes the time to ask how we are, figure out how she

can help you and support you.” That was one of the most effective groups

inside Google.

Alternately, another engineer told the researchers that his “team leader has

poor emotional control. He panics over small issues and keeps trying to grab

control. I would hate to be driving with him in the passenger seat, because he

would keep trying to grab the steering wheel and crash the car.” That team

did not perform well.

Most of all, though, employees talked about how various teams felt. “And

that made a lot of sense to me, maybe because of my experiences at Yale,”

Julia said. “I’d been on some teams that left me feeling totally exhausted and

others where I got so much energy from the group.”

There is strong evidence that group norms play a critical role in shaping

the emotional experience of participating in a team. Research by

psychologists from Yale, Harvard, Berkeley, the University of Oregon, and

elsewhere indicate that norms determine whether we feel safe or threatened,

enervated or excited, and motivated or discouraged by our teammates. Julia’s

study group at Yale, for instance, felt draining because the norms—the

tussles over leadership, the pressure to constantly demonstrate expertise, the

tendency to critique—had put her on guard. In contrast, the norms of her case

competition team—enthusiasm for one another’s ideas, withholding

criticisms, encouraging people to take a leadership role or hang back as they

wanted—allowed everyone to be friendly and unconstrained. Coordination

was easy.

Group norms, the researchers on Project Aristotle concluded, were the

answer to improving Google’s teams. “The data finally started making

sense,” said Dubey. “We had to manage the how of teams, not the who.”

The question, however, was which norms mattered most. Google’s

research had identified dozens of norms that seemed important—and,

sometimes, the norms of one effective team contradicted the norms of another, equally successful group. Was it better to let everyone speak as

much as they wanted, or should strong leaders end meandering debates? Was

it more effective for people to openly disagree with one another, or should

conflicts be downplayed? Which norms were most crucial?

II.

In 1991, a first-year PhD student named Amy Edmondson began visiting

hospital wards, intending to show that good teamwork and good medicine

went hand in hand. But the data kept saying she was wrong.

Edmondson was studying organizational behavior at Harvard. A professor

had asked her to help with a study of medical mistakes, and so Edmondson,

on the prowl for a dissertation topic, started visiting recovery rooms, talking

to nurses, and paging through error reports from two Boston hospitals. In one

cardiac ward, she discovered that a nurse had accidentally given a patient an

IV of lidocaine, an anesthetic, rather than heparin, a blood thinner. In an

orthopedic ward, a patient was given amphetamines rather than aspirin. “You

would be shocked at how many mistakes occur every day,” Edmondson told

me. “Not because of incompetence, but because hospitals are really

complicated places and there’s usually a large team—as many as two dozen

nurses and techs and doctors—who might be involved in each patient’s care.

That’s a lot of opportunities for something to slip through the cracks.”

Some parts of the hospitals Edmondson visited seemed more accident

prone than others. The orthopedic ward, for instance, reported an average of

one error every three weeks; the cardiac ward, on the other hand, reported a

mistake almost every other day. Edmondson also found that the various

departments had very different cultures. In the cardiac ward nurses were

chatty and informal; they gossiped in the hallways and had pictures of their

kids on the walls. In orthopedics, people were more sedate. Nurse managers

wore business suits rather than scrubs and asked everyone to keep the public

areas free of personal items and clutter. Perhaps, Edmondson thought, she

could study the various teams’ cultures and see if they correlated with error

rates.

She and a colleague created a survey to measure team cohesion on various

wards. She asked nurses to describe how frequently their team leader set clear

goals and whether teammates discussed conflicts openly or avoided tense

conversations. She measured the satisfaction, happiness, and self-motivation

of different groups and hired a research assistant to observe the wards for two months.

“I figured it would be pretty straightforward,” Edmondson told me. “The

units with the strongest sense of teamwork would have the lowest error

rates.” Except, when she tabulated her data, Edmondson found exactly the

opposite. The wards with the strongest team cohesion had far more errors.

She checked the data again. It didn’t make any sense. Why would strong

teams make more mistakes?

Confused, Edmondson decided to look at these nurses’ responses, question

by question, alongside the error rates to see if any explanations emerged.

Edmondson had included one survey question that inquired specifically about

the personal risks associated with making errors. She asked people to agree or

disagree with the statement: “If you make a mistake in this unit, it is held against you.” Once she compared the data from that question with error

incidence, she realized what was going on. It wasn’t that wards with strong

teams were making more mistakes. Rather, it was that nurses who belonged

to strong teams felt more comfortable reporting their mistakes. The data indicated that one particular norm—whether people were punished for

missteps—influenced if they were honest after they screwed up.

Some leaders “have established a climate of openness that facilitates

discussion of error, which is likely to be an important influence on detected

error rates,” Edmondson wrote in The Journal of Applied Behavioral Science

in 1996. What particularly surprised her, however, was how complicated

things got the closer she looked: it wasn’t simply that strong teams

encouraged open communication and weak teams discouraged it. In fact,

while some strong teams emboldened people to admit their mistakes, other,

equally strong teams made it hard for nurses to speak up. What made the

difference wasn’t team cohesion—rather, it was the culture each team

established. In one ward with a strong team, for instance, nurses were

overseen by “a hands-on manager who actively invites questions and

concerns….In an interview, the nurse manager explains that a ‘certain level

of error will occur’ so a ‘nonpunitive environment’ is essential to deal with this error productively,” Edmondson wrote. “There is an unspoken rule here

to help each other and check each other,” a nurse told Edmondson’s assistant.

“People feel more willing to admit to errors here, because the nurse manager

goes to bat for you.”

In another ward with a team that, at first glance, seemed equally strong, a

nurse said that when she admitted hurting a patient while drawing blood, the

nurse manager “made her feel like she was on trial.” Another said doctors

“bite your head off if you make a mistake.” Yet measurements of group

cohesion on this ward were still very high. A nurse told the research assistant

that the ward “prides itself on being clean, neat and having an appearance of

professionalism.” The nurse manager for the ward dressed in business suits

and when she delivered criticism, she considerately offered her critiques

behind closed doors. The staff said they appreciated the manager’s

professionalism, were proud of their department, and felt a strong sense of

unity. To Edmondson, the team seemed like they genuinely liked and

respected one another. But they also admitted that the unit’s culture

sometimes made it hard to confess making a mistake.

It wasn’t the strength of the team that determined how many errors were

reported—rather, it was one specific norm.

When Edmondson started working on her dissertation, she visited

technology companies and factory floors, and asked people about the

unwritten rules that shaped how their teammates behaved. “People would say

things like, ‘This is one of the best teams I’ve ever been on, because I don’t

have to wear a work face here,’ or ‘We aren’t afraid to share crazy ideas,’ ”

Edmondson told me. On those teams, norms of enthusiasm and support had

taken hold and everyone felt empowered to voice opinions and take risks.

“And other teams would tell me, ‘My group is really dedicated to each other

and so I try not to go outside my department without checking with my

supervisor first’ or ‘We’re all in this together, so I don’t like to bring up an

idea unless I know it will work.’ ” Within those teams, a norm of loyalty held

sway—and it undermined people’s willingness to make suggestions or take

chances.

Both enthusiasm and loyalty are admirable norms. It wasn’t clear to

managers that they would have such different impacts on people’s behaviors.

And yet they did. In that setting, enthusiastic norms made teams better.

Loyalty norms made them less effective. “Managers never intend to create

unhealthy norms,” Edmondson said. “Sometimes, though, they make choices

that seem logical, like encouraging people to flesh out their ideas before

presenting them, that ultimately undermine a team’s ability to work together.”

As her research continued, Edmondson found a handful of good norms that

seemed to be consistently associated with higher productivity. On the best

teams, for instance, leaders encouraged people to speak up; teammates felt

like they could expose their vulnerabilities to one another; people said they

could suggest ideas without fear of retribution; the culture discouraged

people from making harsh judgments. As Edmondson’s list of good norms

grew, she began to notice that everything shared a common attribute: They

were all behaviors that created a sense of togetherness while also encouraging

people to take a chance.

“We call it ‘psychological safety,’ ” she said. Psychological safety is a

“shared belief, held by members of a team, that the group is a safe place for

taking risks.” It is “a sense of confidence that the team will not embarrass, reject, or punish someone for speaking up,” Edmondson wrote in a 1999

paper. “It describes a team climate characterized by interpersonal trust and

mutual respect in which people are comfortable being themselves.”

Julia and her Google colleagues found Edmondson’s papers as they were

researching norms. The idea of psychological safety, they felt, captured

everything their data indicated was important to Google’s teams. The norms

that Google’s surveys said were most effective—allowing others to fail

without repercussions, respecting divergent opinions, feeling free to question

others’ choices but also trusting that people aren’t trying to undermine you—

were all aspects of feeling psychologically safe at work. “It was clear to us that this idea of psychological safety was pointing to which norms were most

important,” said Julia. “But it wasn’t clear how to teach those inside Google.

People here are really busy. We needed clear guidelines for creating

psychological safety without losing the capacity for dissent and debate that’s

critical to how Google functions.” In other words, how do you convince

people to feel safe while also encouraging them to be willing to disagree?

“For a long time, that was the million-dollar question,” Edmondson told

me. “We knew it was important for teammates to be open with each other.

We knew it was important for people to feel like they can speak up if

something’s wrong. But those are also the behaviors that can set people at

odds. We didn’t know why some groups could clash and still have

psychological safety while others would hit a period of conflict and

everything would fall apart.”

III.

On the first day of auditions for the television show that became known as

Saturday Night Live, the actors showed up, one after another, hour after hour,

until it felt like it would never stop. There were two women who played

midwestern housewives preparing for the annual meteorological disaster

(“Can I borrow your centerpiece for the tornado this year?”) and a singer with

an original composition named “I Am Dog” lampooning the women’s

liberation anthem “I Am Woman.” A roller-skating impressionist and an

obscure musician named Meat Loaf took the stage around lunchtime. The

actor Morgan Freeman and the comic Larry David were on the call sheet, as

were four jugglers and five mimes. To the exhausted observers watching the

auditions, it felt as if every vaudeville act and stand-up comedian between

Boston and Washington, D.C., had shown up.

Which is the way the show’s thirty-year-old creator, Lorne Michaels,

wanted it. Over the previous nine months, Michaels had traveled from

Bangor to San Diego, watching hundreds of comedy club shows. He talked to

writers from television and radio programs and every magazine with a humor

page. His goal, he later said, was to see “every single funny person in North

America.”

By noon on the second day of auditions, tryouts were running late when a

man burst through the doors, leapt onto the stage, and demanded the

producers’ attention. He had a trim mustache and wore a three-piece suit. He

carried a folded umbrella and an attaché. “I’ve been waiting out there for

three hours and I’m not going to wait anymore!” he shouted. “I’m going to

miss my plane!” He marched across the stage. “That’s it! You’ve had your

chance! Good day!” Then he stormed out.

“What the hell was that?” one producer asked.

“Oh, that was just Danny Aykroyd,” said Michaels. They had known each

other in Toronto, where Aykroyd was a student in Michaels’s improv class.

“He’s probably going to do the show,” Michaels said.

Over the next month, as Michaels chose the rest of the cast, the same thing

happened again and again: Instead of picking from among the hundreds of people he auditioned, Michaels hired comedians he already knew or who had

been recommended by friends. Michaels knew Aykroyd from Canada, and

Aykroyd, in turn, was enthusiastic about a guy named John Belushi he had

met in Chicago. Belushi initially said he’d never appear on television because

it was a crass medium, but he recommended a castmate from the National

Lampoon Show named Gilda Radner (who Michaels, it turned out, had

already hired; they knew each other from Godspell). The National Lampoon

Show was affiliated with National Lampoon magazine, which was founded by the writer Michael O’Donoghue, who lived with another comedy writer

named Anne Beatts.

All of these people created the first season of Saturday Night Live. Howard

Shore, the show’s music director, had gone to summer camp with Michaels.

Neil Levy, the show’s talent coordinator, was Michaels’s cousin. Michaels

had met Chevy Chase while standing in a line in Hollywood to see Monty

Python and the Holy Grail. Tom Schiller, another writer, knew Michaels

because they had gone to Joshua Tree to eat hallucinogenic mushrooms

together, and Schiller’s father, a Hollywood writer, had taken Michaels under

his wing early in the young man’s career.

The original cast and writers of Saturday Night Live hailed largely from Canada, Chicago, and Los Angeles and all moved to New York in 1975.

“Manhattan was a show business wasteland then,” said Marilyn Suzanne

Miller, a writer whom Michaels hired after they collaborated on a Lily

Tomlin special in L.A. “It was like Lorne had deposited us on Mars.”

When most of the staff got to New York, they didn’t know anyone except

one another. Many considered themselves anticapitalist or antiwar activists—

or, at least, they were fond of the recreational drugs these activists enjoyed—

and now they were riding elevators with a bunch of suits at 30 Rockefeller

Center, where the show’s studio was being built. “We were all like twenty-

one or twenty-two years old. We didn’t have any money, or any clue what we

were doing, so we spent all of our time trying to make each other laugh,”

Schiller told me. “We’d eat every meal together. We’d go to the same bars

each night. We were terrified that if we separated, one of us might get lost and never be heard from again.”

In subsequent years, as Saturday Night Live became one of the most

popular and longest-running programs in television history, a kind of mythology emerged. “In the early days of SNL,” the journalist Malcolm

Gladwell wrote in 2002, “everyone knew everyone and everyone was always

in everyone else’s business, and that fact goes a long way toward explaining

the extraordinary chemistry among the show’s cast.” There are books filled

with stories of John Belushi breaking into castmates’ apartments to make

spaghetti late at night, or setting their guest bedrooms on fire with carelessly

handled joints, or writers gluing one another’s furniture to the ceilings, or

prank calling one another’s offices, or ordering thirty pizzas to the news

division and then dressing up like security guards so they could infiltrate the

lower floors, steal the pizza, and leave the journalists with the bill. There are

flowcharts detailing who from SNL slept with whom. (They tend to get

complicated, because Michaels was married to writer Rosie Shuster, who

eventually ended up with Dan Aykroyd, who had dated Gilda Radner, who

everyone suspected was in love with writer Alan Zweibel, who later wrote a

book explaining they were in love, but nothing ever happened and, besides, Radner later married a member of the SNL band. “It was the 1970s,” Miller

told me. “Sex was what you did.”)

Saturday Night Live has been held up as a model of great team dynamics.

It is cited in college textbooks as an example of what groups can achieve

when the right conditions are in place and a team intensely bonds.

The group that created Saturday Night Live came together so successfully,

this theory goes, because a communal culture replaced individual needs.

There were shared experiences (“We were all the kids who didn’t get to sit at

the popular table in high school,” Beatts told me); common social networks

(“Lorne was a cult leader,” said writer Bruce McCall. “As long as you had a

Moonie-like devotion to the group, you were fine.”); and group needs

trumped individual egos (“I don’t mean this in a bad way, but we were

Guyana on the seventeenth floor,” said Zweibel. “It was a stalag.”).

But this theory becomes considerably more complicated when you speak

to the people on the original Saturday Night Live team. It’s true those writers

and actors spent enormous amounts of time together and developed a strong

sense of unity—but not because of forced intimacy or shared history or

because they particularly liked each other. In fact, the group norms at

Saturday Night Live created as many tensions as strengths. “There was a

tremendous amount of competitiveness and infighting,” said Beatts. “We were so young, and no one knew how to control themselves. We fought all

the time.”

One night in the writers’ room, Beatts made a joke that they were lucky

Hitler had killed six million Jews because, otherwise, no one would have

found an apartment in New York City. “Marilyn Miller didn’t speak to me

for two weeks,” she said. “Marilyn was completely uptight about jokes about

Hitler. I think she hated me at that point. We would glare at each other for hours.” There were jealousies and rivalries, battles for Michaels’s affection,

competition for airtime. “You wanted your sketch to go on, which meant

someone else’s would have to get cut,” said Beatts. “If you were succeeding,

someone else was failing.”

Even the closest relationships, such as between Alan Zweibel and Gilda

Radner, were fraught. “Gilda and I came up with this character, Roseanne

Roseannadanna, and on Friday I would go into the office and stay up all night

writing the script, like eight or nine pages,” said Zweibel. “Then Gilda would

arrive midmorning, totally refreshed, and take a red pen and start crossing

shit out, like she was some kind of schoolmarm, and I would get pissed. So I

would go back to my office and rework everything, and she would do it

again. By the time the show went on, we usually weren’t speaking to each

other. I once stopped writing sketches for her for three weeks. I purposely

saved my best stuff for other people.”

Furthermore, it’s not entirely true that members of the SNL team enjoyed

spending time together. Garrett Morris, the show’s only black actor, felt like

an outcast and planned to quit as soon as he had enough money. Jane Curtin

would escape to her home and husband as soon as the show was done for the

week. People would form allegiances, and then get into fights, and then form

counter-allegiances. “Everyone was in these cliques that were constantly

shifting,” said Bruce McCall, who came aboard as a writer for the show’s

second season. “It was a pretty dismal place.”

In some ways it’s remarkable the Saturday Night Live team gelled at all.

Michaels, it turned out, had chosen everyone precisely because of their

disparate tastes. Zweibel was a specialist in borscht belt one-liners. Michael

O’Donoghue wrote dark, bitter satires about such topics as the assassination

of JFK. (When a distraught secretary told O’Donoghue that Elvis had died,

he replied, “Smart career move.”) Tom Schiller aspired to direct art films.

And everyone could become scathing critics when their sensibilities clashed.

“Great, Garrett,” O’Donoghue once said when he read a script the actor had

spent weeks writing. Then he dropped it into a trash can. “Real good.”

“Comedy writers carry a lot of anger,” said Schiller. “We were vicious to

each other. If you thought something was funny and no one else did, it could

be brutal.”

So why, given all the tensions and infighting, did the Saturday Night Live

creators become such an effective, productive team? The answer isn’t that

they spent so much time together, or that the show’s norms put the needs of

the group above individual egos.

Rather, the SNL team clicked because, surprisingly, they all felt safe

enough around one another to keep pitching new jokes and ideas. The writers

and actors worked amid norms that made everyone feel like they could take

risks and be honest with one another, even as they were shooting down ideas,

undermining one another, and competing for airtime.

“You know that saying, ‘There’s no I in TEAM’?” Michaels told me. “My

goal was the opposite of that. All I wanted were a bunch of I’s. I wanted everyone to hear each other, but no one to disappear into the group.”

That’s how psychological safety emerged.

Imagine you have been invited to join one of two teams.

Team A is composed of eight men and two women, all of whom are

exceptionally smart and successful. When you watch a video of them

working together, you see articulate professionals who take turns speaking

and are polite and courteous. At some point, when a question arises, one

person—clearly an expert on the topic—speaks at length while everyone else

listens. No one interrupts. When another person veers off topic, a colleague

gently reminds him of the agenda and steers the conversation back on track.

The team is efficient. The meeting ends exactly when scheduled.

Team B is different. It’s evenly divided among men and women, some of

whom are successful executives, while others are middle managers with little

in the way of professional achievements. On a video, you see teammates

jumping in and out of a discussion haphazardly. Some ramble at length;

others are curt. They interrupt one another so much, it’s sometimes hard to

follow the conversation. When a team member abruptly changes the topic or

loses sight of their point, the rest of the group follows him off the agenda. At

the end of the meeting, the meeting doesn’t actually end: Everyone sits

around and gossips.

Which group would you rather join?

Before you decide, imagine you are given one additional piece of

information. When both teams first formed, each member was asked to

complete what’s known as the “Reading the Mind in the Eyes” test. They

were each shown thirty-six photos of people’s eyes and asked to choose

which word, among four offered, best described the emotion that person was

feeling.*2

This test, you are told, measures people’s empathy. The members of Team

A picked the right emotion, on average, 49 percent of the time. Team B: 58

percent.

Does that change your mind?

In 2008, a group of psychologists from Carnegie Mellon and MIT

wondered if they could figure out which kinds of teams were clearly superior.

“As research, management, and many other kinds of tasks are increasingly

accomplished by groups—both those working face-to-face and ‘virtually’—it

is becoming even more important to understand the determinants of group

performance,” the researchers wrote in the journal Science in 2010. “Over the

last century, psychologists made significant progress in defining and

systematically measuring intelligence in individuals. We have used the

statistical approach they developed for individual intelligence to

systematically measure the intelligence of groups.”

Put differently, the researchers wanted to know if there is a collective

intelligence that emerges within a team that is distinct from the smarts of any

single member.

To accomplish this, the researchers recruited 699 people, divided them into

152 teams, and gave each group a series of assignments that required

different kinds of cooperation. Most teams began by spending ten minutes

brainstorming possible uses for a brick and received a point for each unique

idea. Then they were asked to plan a shopping trip as if they were housemates

sharing a single car: Each teammate was given a different list of groceries to

buy and a map showing prices at various stores. The only way to maximize

the team’s score was for each person to sacrifice one item they really wanted

in exchange for something that pleased the entire group. Then the teams were

told to arrive at a ruling on a disciplinary case in which a college basketball

player allegedly bribed his teacher. Some teammates represented the interests

of the faculty; others were stand-ins for the athletics department. Points were

awarded for reaching a verdict that maximized each group’s concerns.

Each of these tasks required full team participation; each demanded

different kinds of collaboration. As the researchers observed groups going

about the tasks, they saw various dynamics emerge. Some teams came up

with dozens of clever uses for the brick, arrived at a verdict that made

everyone happy, and easily divvied up the shopping trip. Others kept

describing the same uses for the brick in different words; came to verdicts

that left some participants feeling alienated; and managed to buy only ice

cream and Froot Loops because no one was willing to compromise. What

was interesting was that teams that did well on one assignment also seemed to do well on the others. Conversely, teams that failed at one thing seemed to

fail at everything.

Some might have hypothesized that the “good teams” were successful

because their members were smarter—that group intelligence might be

nothing more than the intelligence of the individuals making up the team. But

the researchers had tested participants’ IQs beforehand and found that

individual intelligence didn’t correlate with team performance. Putting ten

smart people in a room didn’t mean they solved problems more intelligently

—in fact, those smart people were often outperformed by groups consisting

of people who had scored lower on intellect tests, but who still seemed

smarter as a group.

Others might have argued that the good teams had more decisive leaders.

But the research showed that wasn’t right, either.

The researchers eventually concluded that the good teams had succeeded

not because of innate qualities of team members, but because of how they

treated one another. Put differently, the most successful teams had norms that

caused everyone to mesh particularly well.

“We find converging evidence of a general collective intelligence factor

that explains a group’s performance on a wide variety of tasks,” the

researchers wrote in their Science article. “This kind of collective intelligence is a property of the group itself, not just the individuals in it.” It was the norms, not the people, that made teams so smart. The right norms could raise

the collective intelligence of mediocre thinkers. The wrong norms could

hobble a group made up of people who, on their own, were all exceptionally

bright.

But when the researchers reviewed videos of the good teams’ interactions,

they noticed that not all norms looked alike. “It was striking how different

some of them behaved,” said Anita Woolley, the study’s lead author. “Some

teams had a bunch of smart people who figured out how to break up work

evenly. Other groups had pretty average members but came up with ways to

take advantage of everyone’s relative strengths. Some groups had one strong

leader. Others were more fluid, and everyone took a leadership role.”

There were, however, two behaviors that all the good teams shared.

First, all the members of the good teams spoke in roughly the same proportion, a phenomenon the researchers referred to as “equality in

distribution of conversational turn-taking.” In some teams, for instance,

everyone spoke during each task. In other groups, conversation ebbed from

assignment to assignment—but by the end of the day, everyone had spoken

roughly the same amount.

“As long as everyone got a chance to talk, the team did well,” said

Woolley. “But if only one person or a small group spoke all the time, the

collective intelligence declined. The conversations didn’t need to be equal

every minute, but in aggregate, they had to balance out.”

Second, the good teams tested as having “high average social

sensitivity”—a fancy way of saying that the groups were skilled at intuiting

how members felt based on their tone of voice, how people held themselves,

and the expressions on their faces.

One of the easiest ways to gauge social sensitivity is to show someone

photos of people’s eyes and ask them to describe what that person is thinking

or feeling—the empathy test described previously. This is a “test of how well

the participant can put themselves into the mind of the other person, and

‘tune in’ to their mental state,” wrote the creator of the “Reading the Mind in

the Eyes” test, Simon Baron-Cohen of the University of Cambridge. While

men, on average, correctly guess the emotion of the person in the photo only

52 percent of the time, women typically guess right 61 percent.

People on the good teams in Woolley’s experiment scored above average

on the “Reading the Mind in the Eyes” test. They seemed to know when

someone was feeling upset or left out. They spent time asking one another

what they were thinking about. The good teams also contained more women.

Coming back to the question of which team to join, if you are given a

choice between the serious-minded, professional Team A, or the free-

flowing, more informal Team B, you should opt for Team B. Team A is

smart and filled with effective colleagues. As individuals, they will all be

successful. But as a team, they still tend to act like individuals. There’s little

to suggest that, as a group, they become collectively intelligent, because there’s little evidence that everyone has an equal voice and that members are

sensitive to teammates’ emotions and needs.

In contrast, Team B is messier. People speak over one another, they go on

tangents, they socialize instead of remaining focused on the agenda.

Everyone speaks as much as they need to, though. They feel equally heard

and are attuned to one another’s body language and expressions. They try to

anticipate how one another will react. Team B may not contain as many

individual stars, but when that group unites, the sum is much greater than any

of its parts.

If you ask the original Saturday Night Live team why the show was such a success, they’ll talk about Lorne Michaels. There’s something about his

leadership, they’ll say, that made everything come together. He had an ability

to make everyone feel heard, to make even the most self-centered actors and

writers pay attention to each other. His eye for talent is nearly unrivaled in entertainment over the last forty years.

You’ll also find people who say that Michaels is aloof, socially awkward,

proud, and jealous, and that when he decides to fire someone, he’ll cut them

completely adrift. You might not want Michaels as a friend. But as the leader

of Saturday Night Live, what he’s created is extraordinary: one of the longest-

running shows in history, built on the talent of egomaniacal comedians who,

twenty times a year for four decades, have put their craziness aside just long

enough to make a live television program with only a week’s preparation.

Michaels himself, still the show’s executive producer, says the reason why

Saturday Night Live has succeeded is because he works hard to force people

to become a team. The secret to making that happen, he says, is giving

everyone a voice and finding people willing to be sensitive enough to listen

to one another.

“Lorne was deliberate about making sure everyone got a chance to pitch

their ideas,” the writer Marilyn Miller told me. “He would say, ‘Do we have

pieces for the girls this week?’ ‘Who hasn’t been on in a while?’ ”

“He has this kind of psychic ability to draw in everyone,” said Alan

Zweibel. “I honestly believe that’s why the show has existed for forty years.

At the top of each script, there’s a list of the initials of everyone who worked

on that sketch and Lorne has always said he’s happiest the more initials he

sees.”

Michaels is almost ostentatious in his demonstrations of social sensitivity

—and he expects the cast and writers to mimic him. During the early years of

the show, he was the one who appeared with a soothing word when an

exhausted writer was crying in his office. He has been known to interrupt a

rehearsal or table read and quietly take an actor aside to ask if they need to

talk about something going on in their personal life. Once, when the writer

Michael O’Donoghue was inordinately proud of an obscene commercial

parody, Michaels ordered it read at eighteen different rehearsals—even

though everyone knew the network’s censors would never let it on the air.

“I remember walking up to Lorne once and saying, ‘Okay, here’s my idea,

it’s a bunch of girls at their first slumber party and they are telling each other

how sex works.’ And Lorne said, ‘Write it up,’ just like that, no questions

asked. Then he took an index card and put it on the board for the next show.”

That sketch—which appeared on Saturday Night Live on May 8, 1976—

became one of the show’s most famous pieces. “I was on top of the world,”

said Miller. “He’s got this social ESP. Sometimes he knows exactly what will

make you feel like the most important person on earth.”

Many of the original actors and writers on Saturday Night Live weren’t particularly easy to get along with. They freely admit that, even today, they

are combative and gossipy and sometimes downright mean. But when they

worked together, they were careful with one another’s feelings. Michael

O’Donoghue might have dropped Garrett Morris’s script into a trash can, but

he made a point, afterward, to tell Morris he was joking, and when Morris

suggested an idea about a depressing children’s story, O’Donoghue came up

with “The Little Train That Died.” (“I know I can! I know I can! Heart

attack! Heart attack! Oh, my God, the pain!”) The SNL team avoided picking

fights with one another. (“When I made that Hitler joke, Marilyn wouldn’t

speak to me,” Beatts told me. “But that’s the point. She didn’t speak. She

didn’t escalate it into a whole big thing.”) People might have criticized one another’s ideas, but they were careful about how far they let their critiques go. They disagreed and clashed, but everyone still had a voice at each table

read, and despite the sniping and competition, they were oddly protective of

one another. “Everyone liked everyone else, or at least worked hard to

pretend like they liked everyone,” said Don Novello, a writer on the show in

the 1970s and ’80s and the actor who played Father Guido Sarducci. “We

genuinely trusted each other, as crazy as that sounds.”

For psychological safety to emerge among a group, teammates don’t have

to be friends. They do, however, need to be socially sensitive and ensure

everyone feels heard. “The best tactic for establishing psychological safety is

demonstration by a team leader,” as Amy Edmondson, who is now a

professor at Harvard Business School, told me. “It seems like fairly minor

stuff, but when the leader goes out of their way to make someone feel

listened to, or starts a meeting by saying ‘I might miss something, so I need

all of you to watch for my mistakes,’ or says ‘Jim, you haven’t spoken in a

while, what do you think?,’ that makes a huge difference.”

In Edmondson’s hospital studies, the teams with the highest levels of

psychological safety were also the ones with leaders most likely to model

listening and social sensitivity. They invited people to speak up. They talked

about their own emotions. They didn’t interrupt other people. When someone

was concerned or upset, they showed the group that it was okay to intervene.

They tried to anticipate how people would react and then worked to

accommodate those reactions. This is how teams encourage people to

disagree while still being honest with one another and occasionally clashing.

This is how psychological safety emerges: by giving everyone an equal voice

and encouraging social sensitivity among teammates.

Michaels himself says the job of modeling norms is his most important

duty. “Everyone who comes through this show is different, and I have to

show each of them that I’m treating them different, and show everyone else

I’m treating them different, if we want to draw the unique brilliance out of everyone,” Michaels told me.

“SNL only works when we have different writing and performing styles all

bumping into and meshing with each other,” he said. “That’s my job: To

protect people’s distinct voices, but also to get them to work together. I want

to preserve whatever made each person special before they came to the show,

but also help everyone be sensitive enough to make the rough edges fit.

That’s the only way we can do a new show every week without everyone

wanting to kill each other as soon as we’re done.”

IV.

By the summer of 2015, the Google researchers working on Project Aristotle

had been collecting surveys, conducting interviews, running regressions, and

analyzing statistics for two years. They had scrutinized tens of thousands of

pieces of data and had written dozens of software programs to analyze trends.

Finally, they were ready to reveal their conclusions to the company’s

employees.

They scheduled a meeting at the headquarters in Mountain View.

Thousands of employees showed up, and many more watched via video

stream. Laszlo Bock, the head of the People Operations department at

Google, walked onto the stage and thanked everyone for coming. “The

biggest thing you should take away from this work is that how teams work matters, in a lot of ways, more than who is on them,” he said.

He had spoken to me before he went onstage. “There’s a myth we all carry

inside our head,” Bock said. “We think we need superstars. But that’s not

what our research found. You can take a team of average performers, and if

you teach them to interact the right way, they’ll do things no superstar could

ever accomplish. And there’s other myths, like sales teams should be run

differently than engineering teams, or the best teams need to achieve

consensus around everything, or high-performing teams need a high volume

of work to stay engaged, or teams need to be physically located together.

“But now we can say those aren’t right. The data shows there’s a

universality to how good teams succeed. It’s important that everyone on a

team feels like they have a voice, but whether they actually get to vote on things or make decisions turns out not to matter much. Neither does the

volume of work or physical co-location. What matters is having a voice and

social sensitivity.”

Onstage, Bock brought up a series of slides. “What matters are five key

norms,” he told the audience.

Teams need to believe that their work is important.

Teams need to feel their work is personally meaningful.

Teams need clear goals and defined roles.

Team members need to know they can depend on one another.

But, most important, teams need psychological safety.

To create psychological safety, Bock said, team leaders needed to model

the right behaviors. There were Google-designed checklists they could use:

Leaders should not interrupt teammates during conversations, because that

will establish an interrupting norm. They should demonstrate they are

listening by summarizing what people say after they said it. They should

admit what they don’t know. They shouldn’t end a meeting until all team

members have spoken at least once. They should encourage people who are

upset to express their frustrations, and encourage teammates to respond in

nonjudgmental ways. They should call out intergroup conflicts and resolve

them through open discussion.

There were dozens of tactics on the checklist. All of them, however, came

back to two general principles: Teams succeed when everyone feels like they

can speak up and when members show they are sensitive to how one another

feels.

“There are lots of small things a leader can do,” Abeer Dubey told me. “In

meetings, does the leader cut people off by saying ‘Let me ask a question

there,’ or does she wait until someone is done speaking? How does the leader

act when someone’s upset? These things are so subtle, but they can have a

huge impact. Every team is different, and it’s not uncommon in a company

like Google for engineers or salespeople to be taught to fight for what they believe in. But you need the right norms to make arguments productive rather

than destructive. Otherwise, a team never becomes stronger.”

For three months, Project Aristotle traveled from division to division

explaining their findings and coaching team leaders. Google’s top executives

released tools that any team could use to evaluate if members felt

psychologically safe and worksheets to help leaders and teammates improve

their scores.

“I come from a quantitative background. If I’m going to believe

something, you need to give me data to back it up,” said Sagnik Nandy, who

as chief of Google Analytics Engineering heads one of the company’s biggest

teams. “So seeing this data has been a game changer for me. Engineers love

debugging software because we know we can get 10 percent more efficiency

by just making a few tweaks. But we never focus on debugging human

interactions. We put great people together and hope it will work, and

sometimes it does and sometimes it doesn’t, and most of the time we don’t

know why. Aristotle let us debug our people. It’s totally changed how I run

meetings. I’m so much more conscious of how I model listening now, or

whether I interrupt, or how I encourage everyone to speak.”

The project has had an impact on the Aristotle team, as well. “A couple of

months ago, we were in a meeting where I made a mistake,” Julia Rozovsky

told me. “Not a huge mistake, but an embarrassing one, and afterward, I sent

out a note explaining what had gone wrong, why it had happened, and what

we were doing to resolve it. Right afterward, I got an email back from a team

member that just said, ‘Ouch.’

“It was like a punch to the gut. I was already upset about making this

mistake, and this note totally played on my insecurities. But because of all the

work we’ve done, I pinged the person back and said, ‘Nothing like a good

Ouch to destroy psychological safety in the morning!’ And he wrote back and

said, ‘I’m just testing your resilience.’ That could have been the wrong thing

to say to someone else, but he knew it was exactly what I needed to hear.

With one thirty-second interaction, we diffused the tension.

“It’s funny to do a project on team effectiveness while working on a team,

because we get to test everything we’re learning as we go along. What I’ve

realized is that as long as everyone feels like they can talk and we’re really

demonstrating that we want to hear each other, you feel like everyone’s got

your back.”

Over the last two decades, the American workplace has become much more team focused. The average worker today might belong to a sales team,

as well as a group of unit managers, a special team planning future products,

and the team overseeing the holiday party. Executives belong to groups that

oversee compensation and strategy and hiring and firing and approving HR

policies and figuring out how to cut costs. These teams might meet every day

in person or correspond via email or telecommute from all over the world.

Teams are important. Within companies and conglomerates, government

agencies and schools, teams are now the fundamental unit of self-

organization.

And the unwritten rules that make teams succeed or fail, it turns out, are

the same from place to place. The way investment bankers coordinate their

efforts might seem different from how orthopedic nurses divvy up tasks. And

the specific norms, in those different settings, will likely vary. But one thing

will remain true if those teams work well: In both places, the groups will feel

a sense of psychological safety. They will succeed because teammates feel

they can trust each other, and that honest discussion can occur without fear of

retribution. Their members will have roughly equal voices. Teammates will

show they are sensitive to one another’s emotions and needs.

In general, the route to establishing psychological safety begins with the

team’s leader. So if you are leading a team—be it a group of coworkers or a

sports team, a church gathering, or your family dinner table—think about

what message your choices send. Are you encouraging equality in speaking,

or rewarding the loudest people? Are you modeling listening? Are you

demonstrating a sensitivity to what people think and feel, or are you letting decisive leadership be an excuse for not paying as close attention as you

should?

There are always good reasons for choosing behaviors that undermine

psychological safety. It is often more efficient to cut off debate, to make a quick decision, to listen to whoever knows the most and ask others to hold

their tongues. But a team will become an amplification of its internal culture,

for better or worse. Study after study shows that while psychological safety

might be less efficient in the short run, it’s more productive over time.

If motivation comes from giving individuals a greater sense of control,

then psychological safety is the caveat we must remember when individuals

come together in a group. Establishing control requires more than just seizing self-determination. Being a subversive works, unless you’re leading a team.

When people come together in a group, sometimes we need to give control

to others. That’s ultimately what team norms are: individuals willingly giving

a measure of control to their teammates. But that works only when people

feel like they can trust one another. It only succeeds when we feel

psychologically safe.

As a team leader, then, it’s important to give people control. Some team

leaders at Google make checkmarks next to people’s names each time they

speak, and won’t end a meeting until those checks are all roughly equivalent.

And as a team member, we share control by demonstrating that we are

genuinely listening—by repeating what someone just said, by responding to

their comments, by showing we care by reacting when someone seems upset

or flustered, rather than acting as if nothing is wrong. When we defer to

others’ judgment, when we vocally treat others’ concerns as our own, we

give control to the group and psychological safety takes hold.

“The thing I love most is when I see a sketch performed and the actors are

really killing it onstage, and the sketch’s writers are high-fiving each other by

the monitor, and whoever is waiting in the wings is laughing, and there’s

another team already figuring how to make the characters funnier next time,”

Lorne Michaels told me.

“When I see the entire team drawing some kind of inspiration from the

same thing, I know everything is working,” he said. “At that moment, the

whole team is rooting for each other, and each person feels like the star.”

*1 Project Oxygen found that a good manager (1) is a good coach; (2) empowers and does not micromanage; (3) expresses interest and concern in subordinates’ success and well-being; (4) is results oriented; (5) listens and shares information; (6) helps with career development; (7) has a clear vision and strategy; (8) has key technical skills.

*2 The correct answers for these photos can be found in the notes on this page.

FOCUS

Cognitive Tunneling, Air France Flight 447, and the Power of Mental Models

When they finally found the wreckage, it was clear that few of the victims

had realized disaster was near even as it struck. There was no evidence of

passengers’ last-minute buckling of seatbelts or frenzied raising of food trays.

Oxygen masks were firmly encased in ceiling panels. A submarine probing

the wreckage at the bottom of the Atlantic Ocean found a whole row of seats

upright in the sand, as if waiting to fly again.

It had taken almost two years to find the plane’s data recorders and

everyone hoped that once they were retrieved, the cause of the accident

would become clear at last. Initially, however, the recorders offered few

clues. None of the plane’s computers had malfunctioned, according to the

data. There was no indication of mechanical failure or electrical glitch. It

wasn’t until investigators listened to the cockpit voice recordings that they

began to understand. This Airbus—one of the largest and most sophisticated

aircraft ever built, a plane designed to be an error-proof model of automation

—was at the bottom of the ocean not because of a defect in machinery, but

because of a failure of attention.

Twenty-three months earlier, on May 31, 2009, the night sky was clear as Air

France Flight 447 pulled away from the gate in Rio de Janeiro with 228

people on board, bound for Paris. In the cabin were honeymooners and a

former conductor for the Washington National Opera, a well-known arms

control activist, and an eleven-year-old boy headed to boarding school. One

of the plane’s pilots had brought his wife to Rio so they could enjoy a three-

day layover at the Copacabana Beach. Now she was in the back of the

massive aircraft, while he and two colleagues were in the cockpit, flying them

home.

As the plane began its ascent, there were a few radioed exchanges with air

traffic control, the standard chatter that accompanies any takeoff. Four

minutes after lifting from the runway, the pilot in the right seat—the junior position—activated the autopilot. For the next ten and a half hours, if all went

according to plan, the plane would essentially fly itself.

Just two decades earlier, flying from Rio to Paris had been a much more

taxing affair. Prior to the 1990s and advances in cockpit automation, pilots

were responsible for calculating dozens of variables during a flight, including

airspeed, fuel consumption, direction, and optimal cruising altitude, all while

monitoring weather disturbances, discussions with air traffic control, and the

plane’s position in the sky. Such trips were so demanding that pilots often

rotated responsibilities. They all knew the risks if vigilance waned. In 1987, a

pilot in Detroit had become so overwhelmed during takeoff that he had

forgotten to set the wing flaps. One hundred and fifty-four people died when

the plane crashed after takeoff. Fifteen years before that, pilots flying near Miami had become fixated on a faulty landing gear light and had failed to

notice that they were gradually descending. One hundred and one people

were killed when the craft slammed into the Everglades. Before automated

aviation systems were invented, it wasn’t unheard of for more than a

thousand people to die each year in airplane accidents, often because pilots’

attention spans were stretched too thin, or due to other human errors.

The plane flying from Rio to Paris, however, had been designed to

eliminate such mistakes by vastly reducing the number of decisions a pilot

had to make. The Airbus A330 was so advanced that its computers could

automatically intervene when problems arose, identify solutions, and then tell

pilots, via on-screen instructions, where to direct their focus as they

responded to computerized prompts. In optimal conditions, a human might

fly for only about eight minutes per trip, during takeoff and landing. Planes like the A330 had fundamentally changed piloting from a proactive to a

reactive profession. As a result, flying was easier. Accident rates went down,

and airlines’ productivity soared because more customers could travel with

less crew. A transoceanic flight had once required as many as six pilots. By

the time of Flight 447, thanks to automation, Air France needed only two

people in the cockpit at any given time.

Four hours into the trip, midway between Brazil and Senegal, the plane crossed the equator. Most of the passengers would have been asleep. There

were clouds from a tropical storm in the distance. The two men in the cockpit

remarked on static electricity dancing across the windows, a phenomenon

known as St. Elmo’s fire. “I’m dimming the lighting a bit to see outside, eh?”

said Pierre-Cedric Bonin, the pilot whose wife was in the passenger cabin.

“Yes, yes,” the captain replied. There was a third aviator in a small hold

behind the cockpit, taking a nap. The captain summoned the third man to

switch places, and then left the two junior pilots at the controls so he could sleep. The plane was flying smoothly on full autopilot at thirty-two thousand

feet.

Twenty minutes later there was a small bump from turbulence. “It might be

a good idea to tell the passengers to buckle up,” Bonin informed a stewardess

over the intercom. As the air surrounding the cockpit cooled, three metal

cylinders jutting from the craft’s body—the pitot tubes, which measure

airspeed by detecting the force of air flowing into them—became clogged

with ice crystals. For almost a hundred years, aviators have complained

about, and safely accommodated, ice in pitot tubes. Most pilots know that if

their airspeed measurement plunges unexpectedly, it’s likely because of

clogged pitot tubes. When the pitot tubes on Flight 447 froze over, the

plane’s computers lost airspeed information and the auto-flight system turned

off, as it was programmed to do.

A warning alarm sounded.

“I have the controls,” Bonin said calmly.

“Okay,” his colleague replied.

At this point, if the aviators had done nothing at all, the plane would have

continued flying safely and the pitot tubes would have eventually thawed.

But Bonin, perhaps shaken out of a reverie by the alarm and wanting to offset

the loss of the autopilot, pulled back a bit on the command stick, causing the

plane’s nose to nudge upward and the aircraft to gain altitude. Within one

minute, it had ascended by three thousand feet.

With Flight 447’s nose now pointed slightly upward, the plane’s

aerodynamics began to change. The atmosphere at that height was thin, and

the ascent had disrupted the smooth flow of air over the plane’s wings. The

craft’s “lift”—the basic force of physics that pulls airplanes into the sky

because there is less pressure above a wing than below it—began

deteriorating. In extreme conditions, this can cause an aerodynamic stall, a

dangerous situation in which a plane starts falling, even as its engines strain

with thrust and the nose points skyward. A stall is easy to overcome in its early stages. Simply lowering the nose so air begins flowing smoothly over

the wings prevents a stall from emerging. But if a plane’s nose remains

upward, a stall will become worse and worse until the airplane drops like a

stone in a well.

As Flight 447 rose through the thin atmosphere, a loud chime erupted in

the cockpit and a recorded voice began warning, “Stall! Stall! Stall! Stall!,”

indicating that the plane’s nose was pointed too high.

“What’s this?” the copilot said.

“There’s no good…uh…no good speed indication?” Bonin responded. The

pitot tubes were still clogged with ice and so the display did not show any airspeed.

“Pay attention to your speed,” the copilot said.

“Okay, okay, I’m descending,” Bonin replied.

“It says we’re going up,” the copilot said, “so descend.”

“Okay,” said Bonin.

But Bonin didn’t descend. If he had leveled the plane, the craft would have

flown on safely. Instead, he continued pulling back on the stick slightly,

pushing the airplane’s nose further into the sky.

Automation has today penetrated nearly every aspect of our lives. Most of us

now drive cars equipped with computers that automatically engage the brakes

and reduce transmission power when we hit a patch of rain or ice, often so

subtly we never notice the vehicle has anticipated our tendency to

overcorrect. We work in offices where customers are routed to departments

via computerized phone systems, emails are automatically sent when we’re

away from our desks, and bank accounts are instantaneously hedged against

currency fluctuations. We communicate with smartphones that finish our

words. Even without technology’s help, all humans rely on cognitive

automations, known as “heuristics,” that allow us to multitask. That’s why

we can email the babysitter while chatting with our spouse and

simultaneously watching the kids. Mental automation lets us choose, almost

subconsciously, what to pay attention to and what to ignore.

Automations have made factories safer, offices more efficient, cars less

accident-prone, and economies more stable. By one measure, there have been

more gains in personal and professional productivity in the past fifty years

than in the previous two centuries combined, much of it made possible by

automation.

But as automation becomes more common, the risks that our attention

spans will fail have risen. Studies from Yale, UCLA, Harvard, Berkeley,

NASA, the National Institutes of Health, and elsewhere show errors are

particularly likely when people are forced to toggle between automaticity and

focus, and are unusually dangerous as automatic systems infiltrate airplanes,

cars, and other environments where a misstep can be tragic. In the age of

automation, knowing how to manage your focus is more critical than ever

before.

Take, for instance, Bonin’s mindset when he was forced to take control of

Flight 447. It is unclear why he continued guiding the plane upward after

agreeing with his copilot that they should descend. Maybe he hoped to climb

above the storm clouds on the horizon. Perhaps it was an unintentional

reaction to the sudden alarm. We will never know why he didn’t return the

controls to neutral once the stall warning sounded. There is significant

evidence, however, that Bonin was in the grip of what’s known as “cognitive

tunneling”—a mental glitch that sometimes occurs when our brains are

forced to transition abruptly from relaxed automation to panicked attention.

“You can think about your brain’s attention span like a spotlight that can

go wide and diffused, or tight and focused,” said David Strayer, a cognitive

psychologist at the University of Utah. Our attention span is guided by our

intentions. We choose, in most situations, whether to focus the spotlight or let

it be relaxed. But when we allow automated systems, such as computers or

autopilots, to pay attention for us, our brains dim that spotlight and allow it to swing wherever it wants. This is, in part, an effort by our brains to conserve

energy. The ability to relax in this manner gives us huge advantages: It helps

us subconsciously control stress levels and makes it easier to brainstorm, it

means we don’t have to constantly monitor our environment, and it helps us

get ready for big cognitive tasks. Our brains automatically seek out

opportunities to disconnect and unwind.

“But then, bam! , some kind of emergency happens—or you get an

unexpected email, or someone asks you an important question in a meeting—

and suddenly the spotlight in your head has to ramp up all of a sudden and, at

first, it doesn’t know where to shine,” said Strayer. “So the brain’s instinct is

to force it as bright as possible on the most obvious stimuli, whatever’s right

in front of you, even if that’s not the best choice. That’s when cognitive

tunneling happens.”

Cognitive tunneling can cause people to become overly focused on

whatever is directly in front of their eyes or become preoccupied with

immediate tasks. It’s what keeps someone glued to their smartphone as the

kids wail or pedestrians swerve around them on the sidewalk. It’s what

causes drivers to slam on their brakes when they see a red light ahead. We

can learn techniques to get better at toggling between relaxation and

concentration, but they require practice and a desire to remain engaged.

However, once in a cognitive tunnel, we lose our ability to direct our focus.

Instead, we latch on to the easiest and most obvious stimulus, often at the

cost of common sense.

As the pitot tubes iced over and the alarms blared, Bonin entered a cognitive

tunnel. His attention had been relaxed for the past four hours. Now, amid

flashing lights and ringing bells, his attention searched for a focal point. The

most obvious one was the video monitor right in front of his eyes.

The cockpit of an Airbus A330 is a minimalist masterpiece, an

environment designed to be distraction free, with just a few screens alongside

a modest number of gauges and controls. One of the most prominent screens,

directly in each pilot’s line of sight, is the primary flight display. There is a

broad line running across the horizontal center of a screen that indicates the

division between sky and land. Floating atop this line is the small icon of an

aircraft. If a plane rolls to either side while flying, the icon goes off-kilter and

pilots know their wings are no longer parallel to the ground.

PRIMARY FLIGHT DISPLAY

When Bonin heard the alarm and looked at his instrument panel, he saw

the primary flight display. The icon of the plane on that screen had rolled

slightly to the right. Normally, this would not have been a concern. Planes

roll in small increments throughout a trip and are easily righted. But now,

with the autopilot disengaged and the sudden pressure to focus, the spotlight

inside Bonin’s head shined on that off-kilter icon. Bonin, data records

indicate, became focused on getting the wings of that icon level with the

middle of his screen. And then, perhaps because he was fixated on correcting

the roll, he failed to notice that he was still pulling back on the control stick,

lifting the plane’s nose.

As Bonin pulled back on his stick, the front of the aircraft rose higher.

Then, another instance of cognitive tunneling occurred—this time, inside the

head of Bonin’s copilot. The man in the left-hand seat was named David

Robert, and he was officially the “monitoring pilot.” His job was to keep

watch over Bonin and intervene if the “flying pilot” became overwhelmed. In

a worst-case scenario, Robert could take control of the craft. But now, with

alarms sounding, Robert did what’s most natural in such a situation: He

became focused on the most obvious stimuli. There was a screen next to him

spewing text as the plane’s computer provided updates and instructions.

Robert turned his eyes away from Bonin and began staring at the scrolling

type, reading the messages aloud. “Stabilize,” Robert said. “Go back down.”

Focused on the screen as he was, Robert didn’t see that Bonin was pulling

back on his stick and didn’t register that the flying pilot was raising the craft

higher even as he agreed they needed to descend. There is no evidence that

Robert looked at his gauges. Instead, he frantically scrolled through a series of messages automatically generated by the plane’s computer. Even if those

prompts had been helpful, nothing indicates that Bonin, locked on the

airplane icon in front of him, heard anything his colleague said.

The plane rose through thirty-five thousand feet, drawing dangerously

close to its maximum height. The nose of the airplane was now pitched at

twelve degrees.

The copilot finally looked away from his screen. “We’re climbing,

according to this,” he told Bonin, referring to the instrument panel. “Go back

down!” he shouted.

“Okay,” Bonin replied.

Bonin pushed his stick forward, forcing the plane’s nose to dip slightly. As

a result, the force of gravity against the pilots lessened by a third, giving them

a brief sense of weightlessness. “Gently!” his colleague snapped. Then

Bonin, perhaps overwhelmed by the combination of the alarms, the

weightlessness, and his copilot’s chastisement, jerked his hand backward,

arresting the descent of the plane’s nose. The craft remained at a six-degree

upward pitch. Another loud warning chime came from the cockpit’s speakers,

and a few seconds later the aircraft began to shake, what’s known as

buffeting, the result of rough air moving across the wings in the early stages

of a serious aerodynamic stall.

“We’re in, ahhh, yeah, we’re in a climb, I think?” Bonin said.

For the next ten seconds, neither man spoke. The plane rose above its

maximum recommended altitude of 37,500 feet. To stay aloft, Flight 447 had

to descend. If Bonin would simply lower the nose, all would be fine.

Then, as the pilots focused on their screens, the ice crystals clogging the

pitot tubes cleared and the plane’s computer began receiving accurate

airspeed information once again. From this moment onward, all the craft’s

sensors functioned correctly throughout the flight. The computer began

spitting out instructions, telling the pilots how to overcome the stall. Their instrument panels were showing them everything they needed to know to

right the plane, but they had no idea where to look. Even as helpful

information arrived, Bonin and Robert had no clue as to where to focus.

The stall warning blared again. A piercing, high-pitched noise called the

“cricket,” designed to be impossible for pilots to ignore, began to sound.

“Damn it!” the copilot yelled. He had already paged the captain. “Where is

he?…Above all, try to touch the lateral controls as little as possible,” he told

Bonin.

“Okay,” Bonin replied. “I’m in TO/GA, right?”

It is at this moment, investigators later concluded, that the lives of all 228

people on board Flight 447 were condemned. “TO/GA” is an acronym for

“takeoff, go around,” a setting that aviators use to abort a landing, or “go around” the runway. TO/GA pushes a plane’s thrust to maximum while the

pilot raises the nose. There is a sequence of moves associated with TO/GA

that all aviators practice, hundreds of times, in preparation for a certain kind

of emergency. At low altitudes, TO/GA makes a lot of sense. The air is thick

near the earth’s surface, and so increasing thrust and raising the nose makes a

plane go faster and higher, allowing a pilot to abort a landing safely.

But at thirty-eight thousand feet, the air is so thin that TO/GA doesn’t

work. A plane can’t draw additional thrust at that height, and raising the nose

simply increases the severity of a stall. At that altitude, the only correct

choice is lowering the nose. In his startled panic, however, Bonin made a second mistake, a mental misstep that is a cousin to cognitive tunneling: He

sought to aim the spotlight in his head onto something familiar. Bonin fell

back on a reaction he had practiced repeatedly, a sequence of moves he had

learned to associate with emergencies. He fell into what psychologists call

“reactive thinking.”

Reactive thinking is at the core of how we allocate our attention, and in

many settings, it’s a tremendous asset. Athletes, for example, practice certain

moves again and again so that, during a game, they can think reactively and

execute plays faster than their opponents can respond. Reactive thinking is

how we build habits, and it’s why to-do lists and calendar alerts are so

helpful: Rather than needing to decide what to do next, we can take

advantage of our reactive instincts and automatically proceed. Reactive

thinking, in a sense, outsources the choices and control that, in other settings,

create motivation.

But the downside of reactive thinking is that habits and reactions can

become so automatic they overpower our judgment. Once our motivation is

outsourced, we simply react. One study conducted by Strayer, the

psychologist, in 2009 looked at how drivers’ behaviors changed when cars

were equipped with features such as cruise control and automatic braking

systems that allowed people to pay less attention to road conditions.

“These technologies are supposed to make driving safer, and many times,

they do,” said Strayer. “But it also makes reactive thinking easier, and so

when the unexpected startles you, when the car skids or you have to brake

suddenly, you’ll react with practiced, habitual responses, like stomping on the

pedal or twisting the wheel too far. Instead of thinking, you react, and if it’s

not the correct response, bad things happen.”

Inside the cockpit, as the alarms sounded and the cricket chirped, the pilots were silent. Robert, the copilot, perhaps lost in his own thoughts, didn’t reply

to Bonin’s question—“I’m in TO/GA, right?”—but instead tried once again

to beckon the captain, who was still resting in the hold. If Bonin had paused

to consider the basic facts—he was in thin air, a stall alarm was sounding, the

plane couldn’t safely go higher—he would have immediately realized he

needed to lower the airplane’s nose. Instead, he relied on behaviors he had

practiced hundreds of times and pulled back on the stick. The plane’s nose

increased to a terrifying eighteen-degree pitch as Bonin pushed the throttle

open. The plane moved higher, touched the top of an arc, and then started

dropping, its nose still pointed upward and the engines at full thrust. The

cockpit began shaking as the buffeting grew more pronounced. The plane

was falling fast.

“What the hell is happening?” the copilot asked. “Do you understand

what’s happening, or not?”

“I don’t have control of the plane anymore!” Bonin shouted. “I don’t have

control of the plane at all!”

In the cabin, passengers probably had little idea anything was wrong.

There were no alarms they could hear. The buffeting likely felt like normal

turbulence. Neither pilot ever made an announcement of any kind.

The captain finally entered the cockpit.

“What the hell are you doing?” he asked.

“I don’t know what’s happening,” Robert said.

“We’re losing control of the airplane!” Bonin shouted.

“We lost control of the airplane and we don’t understand at all,” Robert

said. “We’ve tried everything.”

Flight 447 was now sinking at a rate of ten thousand feet per minute. The

captain, standing behind the pilots and perhaps overwhelmed by what he saw,

uttered a curse word and then remained silent for forty-one seconds.

“I have a problem,” Bonin said, the panic audible in his voice. “I have no

more displays.” This was not correct. The displays—the screens on his

instrument panel—were providing accurate information and were clearly

visible. But Bonin was too overwhelmed to focus.

“I have the impression we’re going crazily fast,” Bonin said. The plane, in

fact, at this point was moving far too slowly. “What do you think?” Bonin

asked as he reached for the lever that would raise the speed-brakes on the

wing, slowing the plane even more.

“No!” shouted the copilot. “Above all, don’t extend the brakes!”

“Okay,” Bonin said.

“What should we do?” the copilot asked the captain. “What do you see?”

“I don’t know,” the captain said. “It’s descending.”

Over the next thirty-five seconds, as the pilots shouted questions, the plane

dropped another nine thousand feet.

“Am I going down now?” Bonin asked. The instruments in front of him

could have easily answered that question.

“You’re going down down down,” the copilot said.

“I’ve been at full back stick for a while,” Bonin said.

“No, no!” the captain shouted. The plane was now less than ten thousand

feet above the Atlantic Ocean. “Don’t climb!”

“Give me the controls!” the copilot said. “The controls! To me!”

“Go ahead,” Bonin says, finally releasing the stick. “You have the controls.

We’re still in TO/GA, right?”

As the copilot took over, the plane fell another six thousand feet closer to

the sea.

“Watch out, you’re pitching up there,” the captain said.

“I’m pitching up?” the copilot replied.

“You’re pitching up,” the captain said.

“Well, we need to!” Bonin said. “We’re at four thousand feet!”

By now, the only way the craft could pick up enough speed was to lower

its nose into a dive and let more air flow over the wings. But with such a small distance between the plane and the ocean’s surface, there was no room

to maneuver. A ground proximity warning began blaring, “SINK RATE!

PULL UP!” The cockpit was filled with constant noise.

“You’re pitching up,” the captain told the copilot.

“Let’s go!” Bonin replied. “Pull up! Pull up! Pull up!”

The men stopped speaking for a moment.

“This can’t be true,” said Bonin. The ocean was visible through the

cockpit’s windows. If the pilots had craned their necks, they could have made

out individual waves.

“But what’s happening?” Bonin asked.

Two seconds later, the plane plunged into the sea.

II.

In the late 1980s, a group of psychologists at a consulting firm named Klein

Associates began exploring why some people seem to stay calm and focused

amid chaotic environments while others become overwhelmed. Klein

Associates’ business was helping companies analyze how they make

decisions. A variety of clients wanted to know why some employees made

such good choices amid stress and time pressures, while other workers

became distracted. More important, they wanted to know if they could train

people to get better at paying attention to the right things.

The Klein Associates team began by interviewing professionals who

worked in extreme settings, such as firefighters, military commanders, and

emergency rescue personnel. Many of those conversations, however, proved

frustrating. Firefighters could look at a burning staircase and sense if it would

hold their weight, they knew which parts of a building needed constant

attention and how to stay attuned to warning signs, but they struggled to

explain how they did it. Soldiers could tell you which parts of a battlefield were more likely to be harboring enemies and where to focus for signs of

ambush. But when asked to explain their decisions, they chalked it up to

intuition.

So the team moved on to other settings. One researcher, Beth Crandall,

visited neonatal intensive care units, or NICUs, around Dayton, near where

she lived. A NICU, like all critical care settings, is a mix of chaos and

banality set against a backdrop of constantly beeping machines and chiming

warnings. Many of the babies inside a NICU are on their way to full health;

they might have arrived prematurely or suffered minor injuries during birth,

but they are not seriously ill. Others, though, are unwell and need constant

monitoring. What makes things particularly hard for NICU nurses, however,

is that it is not always clear which babies are sick and which are healthy.

Seemingly okay preemies can become unwell quickly; sick infants can

recover unexpectedly. So nurses are constantly making choices about where

to focus their attention: the squalling baby or the quiet one? The new lab

results or the worried parents who say something seems wrong? What’s more, these choices occur amid a constant stream of data from machines—

heart monitors and automatic thermometers, blood pressure systems and

pulse oximeters—that are ready to sound alarms the moment anything

changes. Such innovations have made patients safer and have remarkably

improved NICUs’ productivity, because fewer nurses are now needed to

oversee greater numbers of children. But they have also made NICUs more

complex. Crandall wanted to understand how nurses made decisions about

which babies needed their attention, and why some of them were better at

focusing on what mattered most.

Crandall interviewed nurses who were calm in the face of emergencies and

others who seemed on the brink of collapse. Most interesting were the

handful of nurses who seemed particularly gifted at noticing when a baby

was in trouble. They could predict an infant’s decline or recovery based on

small warning signs that almost everyone else overlooked. Often, the clues

these nurses relied upon to spot problems were so subtle that they themselves

had trouble later recalling what had prompted them to act. “It was like they

could see things no one else did,” Crandall told me. “They seemed to think

differently.”

One of Crandall’s first interviews was with a talented nurse named

Darlene, who described a shift from a few years earlier. Darlene had been

walking past an incubator when she happened to glance at the baby inside.

All of the machines hooked up to the child showed that her vitals were within

normal ranges. There was another RN keeping watch over the baby, and she

was observing the infant attentively, unconcerned by what she saw. But to

Darlene, something seemed wrong. The baby’s skin was slightly mottled

instead of uniformly pink. The child’s belly seemed a bit distended. Blood

had recently been drawn from a pinprick in her heel and the Band-Aid

showed a blot of crimson, rather than a small dot.

None of that was particularly unusual or troubling. The nurse tending to

the child said she was eating and sleeping well. Her heartbeat was strong. But

something about all those small things occurring together caught Darlene’s

attention. She opened the incubator and examined the infant. The newborn

was conscious and awake. She grimaced slightly at Darlene’s touch but

didn’t cry. There was nothing specific that she could point to, but this baby

simply didn’t look like Darlene expected her to.

Darlene found the attending physician and said they needed to start the

child on intravenous antibiotics. All they had to go on was Darlene’s

intuition, but the doctor, deferring to her judgment, ordered the medication

and a series of tests. When the labs came back, they showed that the baby

was in the early stages of sepsis, a potentially fatal whole-body inflammation

caused by a severe infection. The condition was moving so fast that, had they

waited any longer, the newborn would have likely died. Instead, she

recovered fully.

“It fascinated me that Darlene and this other nurse had seen the same

warning signs, they had all the same information, but only Darlene detected

the problem,” Crandall said. “To the other nurse, the mottled skin and the

bloody Band-Aid were data points, nothing big enough to trigger an alarm.

But Darlene put everything together. She saw a whole picture.” When

Crandall asked Darlene to explain how she knew the baby was sick, Darlene

said it was a hunch. As Crandall asked more questions, however, another

explanation emerged. Darlene explained that she carried around a picture in

her mind of what a healthy baby ought to look like—and the infant in the crib, when she glanced at her, hadn’t matched that image. So the spotlight

inside Darlene’s head went to the child’s skin, the blot of blood on her heel,

and the distended belly. It focused on those unexpected details and triggered

Darlene’s sense of alarm. The other nurse, in contrast, didn’t have a strong

picture in her head of what she expected to see, and so her spotlight focused

on the most obvious details: The baby was eating. Her heartbeat was strong.

She wasn’t crying. The other nurse was distracted by the information that was

easiest to grasp.

People like Darlene who are particularly good at managing their attention

tend to share certain characteristics. One is a propensity to create pictures in

their minds of what they expect to see. These people tell themselves stories

about what’s going on as it occurs. They narrate their own experiences within

their heads. They are more likely to answer questions with anecdotes rather

than simple responses. They say when they daydream, they’re often

imagining future conversations. They visualize their days with more

specificity than the rest of us do.

Psychologists have a phrase for this kind of habitual forecasting: “creating

mental models.” Understanding how people build mental models has become

one of the most important topics in cognitive psychology. All people rely on

mental models to some degree. We all tell ourselves stories about how the

world works, whether we realize we’re doing it or not.

But some of us build more robust models than others. We envision the

conversations we’re going to have with more specificity, and imagine what

we are going to do later that day in greater detail. As a result, we’re better at

choosing where to focus and what to ignore. The secret of people like

Darlene is that they are in the habit of telling themselves stories all the time.

They engage in constant forecasting. They daydream about the future and

then, when life clashes with their imagination, their attention gets snagged.

That helps explain why Darlene noticed the sick baby. She was in the habit of

imagining what the babies in her unit ought to look like. Then, when she

glanced over and the bloody Band-Aid, distended belly, and mottled skin

didn’t match the image in her mind, the spotlight in her head swung toward

the child’s bassinet.

Cognitive tunneling and reactive thinking occur when our mental

spotlights go from dim to bright in a split second. But if we are constantly telling ourselves stories and creating mental pictures, that beam never fully

powers down. It’s always jumping around inside our heads. And, as a result,

when it has to flare to life in the real world, we’re not blinded by its glare.

When the Air France Flight 447 investigators began parsing cockpit audio

recordings, they found compelling evidence that none of the pilots had strong

mental models during their flight.

“What’s this?” the copilot asked when the first stall warning sounded.

“There’s no good speed indication?…We’re in…we’re in a climb?” Bonin

responded.

The pilots kept asking each other questions as the plane’s crisis deepened

because they didn’t have mental models to help them process new

information as it arrived. The more they learned, the more confused they

became. This explains why Bonin was so prone to cognitive tunneling. He

hadn’t been telling himself a story as the plane flew along, and so when the

unexpected occurred, he wasn’t sure which details to pay attention to. “I have

the impression we’re going crazily fast,” he said as the plane began to slow

and fall. “What do you think?”

And when Bonin did finally latch on to a mental model—“I’m in TO/GA,

right?”—he didn’t look for any facts that challenged that model. “I’m

climbing, okay, so we’re going down,” he said two minutes before the plane

crashed, seemingly oblivious to the contradiction of his words. “Okay, we’re

in TO/GA,” he added. “How come we’re continuing to go right down?”

“This can’t be true,” he said seconds before the plane hit the water. Then

there are his last words, which make all the sense in the world once you

realize Bonin was still grasping for useful mental models as the plane hurtled

toward the waves:

“But what’s happening?”

This problem isn’t unique to the aviators of Flight 447, of course. It

happens all the time, within offices and on freeways, as we’re working on our

smartphones and multitasking from the couch. “This mess of a situation is

one hundred percent our own fault,” said Stephen Casner, a research

psychologist at NASA who has studied dozens of accidents like Air France

Flight 447. “We started with a creative, flexible, problem-solving human and

a mostly dumb computer that’s good at rote, repetitive tasks like monitoring.

So we let the dumb computer fly and the novel-writing, scientific-theorizing,

jet-flying humans sit in front of the computer like potted plants watching for

blinking lights. It’s always been difficult to learn how to focus. It’s even

harder now.”

A decade after Beth Crandall interviewed the NICU nurses, two economists

and a sociologist from MIT decided to study how, exactly, the most

productive people build mental models. To do that, they convinced a

midsized recruiting firm to give them access to their profit-and-loss data,

employees’ appointment calendars, and the 125,000 email messages the

firm’s executives had sent over the previous ten months.

The first thing the researchers noticed, as they began crawling through all

that data, was that the firm’s most productive workers, its superstars, shared a

number of traits. The first was they tended to work on only five projects at once—a healthy load, but not extraordinary. There were other employees

who handled ten or twelve projects at a time. But those employees had a

lower profit rate than the superstars, who were more careful about how they

invested their time.

The economists figured the superstars were pickier because they were

seeking out assignments that were similar to previous work they had done.

Conventional wisdom holds that productivity rises when people do the same

kind of tasks over and over. Repetition makes us faster and more efficient

because we don’t have to learn fresh skills with each new assignment. But as

the economists looked more closely, they found the opposite: The superstars

weren’t choosing tasks that leveraged existing skills. Instead, they were

signing up for projects that required them to seek out new colleagues and

demanded new abilities. That’s why the superstars worked on only five

projects at a time: Meeting new people and learning new skills takes a lot of

additional hours.

Something else the superstars had in common is they were

disproportionately drawn to assignments that were in their early stages. This

was surprising, because joining a project in its infancy is risky. New ideas

often fail, no matter how smart or well executed. The safest bet is signing on

to a project that is well under way.

However, the beginning of a project is also more information rich. By

joining fledgling initiatives, the superstars were cc’d on emails they wouldn’t

have otherwise seen. They learned which junior executives were smart and

picked up new ideas from their younger colleagues. They were exposed to

emerging markets and the lessons of the digital economy earlier than other

executives. What’s more, the superstars could later claim ownership of an

innovation simply by being in the room when it was born, rather than fighting

paternity battles once it was deemed a success.

Finally, the superstars also shared a particular behavior, almost an

intellectual and conversational tic: They loved to generate theories—lots and

lots of theories, about all kinds of topics, such as why certain accounts were

succeeding or failing, or why some clients were happy or disgruntled, or how

different management styles influenced various employees. They were

somewhat obsessive, in fact, about trying to explain the world to themselves

and their colleagues as they went about their days.

The superstars were constantly telling stories about what they had seen and

heard. They were, in other words, much more prone to generate mental

models. They were more likely to throw out ideas during meetings, or ask

colleagues to help them imagine how future conversations might unfold, or

envision how a pitch should go. They came up with concepts for new

products and practiced how they would sell them. They told anecdotes about

past conversations and dreamed up far-fetched expansion plans. They were

building mental models at a near constant rate.

“A lot of these people will come up with explanation after explanation

about what they just saw,” said Marshall Van Alstyne, one of the MIT

researchers. “They’ll reconstruct a conversation right in front of you,

analyzing it piece by piece. And then they’ll ask you to challenge them on

their take. They’re constantly trying to figure out how information fits

together.”

The MIT researchers eventually calculated that getting cc’d on those early

information-rich emails and hashing out those mental models earned the

superstars an extra $10,000 a year, on average, in bonuses. The superstars

took on only five projects at once—but they outperformed their colleagues

because they had more productive methods of thinking.

Researchers have found similar results in dozens of other studies. People

who know how to manage their attention and who habitually build robust

mental models tend to earn more money and get better grades. Moreover,

experiments show that anyone can learn to habitually construct mental

models. By developing a habit of telling ourselves stories about what’s going

on around us, we learn to sharpen where our attention goes. These

storytelling moments can be as small as trying to envision a coming meeting

while driving to work—forcing yourself to imagine how the meeting will

start, what points you will raise if the boss asks for comments, what

objections your coworkers are likely to bring up—or they can be as big as a

nurse telling herself stories about what infants ought to look like as she walks

through a NICU.

If you want to make yourself more sensitive to the small details in your

work, cultivate a habit of imagining, as specifically as possible, what you

expect to see and do when you get to your desk. Then you’ll be prone to

notice the tiny ways in which real life deviates from the narrative inside your head. If you want to become better at listening to your children, tell yourself

stories about what they said to you at dinnertime last night. Narrate your life,

as you are living it, and you’ll encode those experiences deeper in your brain.

If you need to improve your focus and learn to avoid distractions, take a

moment to visualize, with as much detail as possible, what you are about to

do. It is easier to know what’s ahead when there’s a well-rounded script

inside your head.

Companies say such tactics are important in all kinds of settings, including

if you’re applying for a job or deciding whom to hire. The candidates who

tell stories are the ones every firm wants. “We look for people who describe

their experiences as some kind of a narrative,” Andy Billings, a vice

president at the video game giant Electronic Arts, told me. “It’s a tip-off that

someone has an instinct for connecting the dots and understanding how the

world works at a deeper level. That’s who everyone tries to get.”

III.

One year after Air France Flight 447 disappeared into the ocean, another

Airbus—this one part of Qantas Airways—taxied onto a runway in

Singapore, requested permission to begin the eight-hour flight to Sydney, and

lifted into the bright morning sky.

The Qantas plane flying that day had the same auto-flight systems as the

Air France airplane that had crashed into the sea. But the pilots were very

different. Even before Captain Richard Champion de Crespigny stepped on

board Qantas Flight 32, he was drilling his crew in the mental models he

expected them to use.

“I want us to envision the first thing we’ll do if there’s a problem,” he told

his copilots as they rode in a van from the Fairmont hotel to Singapore

Changi Airport. “Imagine there’s an engine failure. Where’s the first place

you’ll look?” The pilots took turns describing where they would turn their

eyes. De Crespigny conducted this same conversation prior to every flight.

His copilots knew to expect it. He quizzed them on what screens they would

stare at during an emergency, where their hands would go if an alarm

sounded, whether they would turn their heads to the left or stare straight

ahead. “The reality of a modern aircraft is that it’s a quarter million sensors

and computers that sometimes can’t tell the difference between garbage and

good sense,” de Crespigny later told me. He’s a brusque Australian, a cross

between Crocodile Dundee and General Patton. “That’s why we have human

pilots. It’s our job to think about what might happen, instead of what is.”

After the crew’s visualization session, de Crespigny laid down some rules.

“Everyone has a responsibility to tell me if you disagree with my decisions or

think I’m missing anything.”

“Mark,” he said, gesturing to a copilot, “if you see everyone looking down,

I want you to look up. If we’re all looking up, you look down. We’ll all

probably make at least one mistake this flight. You’re each responsible for

catching them.”

Four hundred and forty passengers were preparing to board the plane when

the pilots entered the cockpit. De Crespigny, like all Qantas aviators, was required to undergo a yearly review of his flying skills, and so, on that day,

there were two extra pilots in the cockpit, observers drawn from the airline’s

most experienced ranks. The review wasn’t perfunctory. If de Crespigny

stumbled, it could trigger his early retirement.

As the pilots took their seats, one of the observers sat near the center of the

cockpit, where standard operating procedure usually positioned the second

officer. De Crespigny frowned. He had expected the observer to sit off to the

side, out of the way. He had a picture in his mind of how his cockpit ought to

be arranged.

De Crespigny faced the evaluator. “Where do you intend to sit?” he asked.

“In this seat between you and Matt,” the observer said.

“I’ve got a problem with that,” de Crespigny said. “You’re inhibiting my

crew.”

The cockpit went silent. This kind of confrontation was not supposed to

happen between a captain and the observers.

“Rich, I can’t see you if I sit in Mark’s seat,” the observer said. “How can I

check you?”

“That’s your problem,” de Crespigny replied. “I want my crew together

and I want Mark in your seat.”

“Richard, you’re being unreasonable,” the second observer said.

“I have a flight to command and I want my crew operating properly,” said

de Crespigny.

“Look, Richard,” replied the evaluator, “if it helps, I promise I’ll be the

second officer if I have to be.”

De Crespigny paused. He wanted to show his crew they could question his

decisions. He wanted them to know he was paying close attention to what

they had to say and was sensitive to what they thought. Just as teams at

Google and Saturday Night Live need to be able to critique one another without fear of punishment, de Crespigny wanted his crew to see that he

encouraged them to disagree.

“Fantastic,” de Crespigny said to the evaluator. (“Once he said he would

be the second officer, it fit into the plan I had in my mind,” de Crespigny later told me.) Inside the cockpit, de Crespigny turned back to the controls

and began moving Qantas Flight 32 away from the gate.

The plane sped down the runway and lifted into the air. At 2,000 feet, de

Crespigny activated the plane’s autopilot. The sky was cloudless, the

conditions perfect.

At 7,400 feet, as de Crespigny was about to order the first officer to switch

off the cabin’s seatbelt sign, he heard a boom. It was probably just a surge of

high-pressure air moving through the engine, he thought. Then there was

another, even louder crash, followed by what sounded like thousands of

marbles being thrown against the hull.

A red alarm flashed on de Crespigny’s instrument panel and a siren blared

in the cockpit. Investigators would later determine that an oil fire inside one

of the left jets had caused a massive turbine disk to detach from the drive shaft, shear into three pieces, and shoot outward, shattering the engine. Two

of the larger fragments from that explosion punched holes in the left wing,

one of them large enough for a man to fit through. Hundreds of smaller

shards, exploding like a cluster bomb, cut through electrical wires, fuel hoses,

a fuel tank, and hydraulic pumps. The underside of the wing looked as though

it had been machine-gunned.

Long strips of metal were bending off the left wing and whipping in the

air. The plane began to shake. De Crespigny reached over to decrease the

aircraft’s speed, the standard reaction for an emergency of this kind, but

when he pushed a button, the auto-thrust didn’t respond. Alarms started

popping up on his computer display. Engine two was on fire. Engine three

was damaged. There was no data at all for engines one and four. The fuel

pumps were failing. The hydraulics, pneumatics, and electrical systems were

almost inoperative. Fuel was leaking from the left wing in a wide fan. The

damage would later be described as one of the worst midair mechanical

disasters in modern aviation.

De Crespigny radioed Singapore air traffic control. “QF32, engine two

appears failed,” he said. “Heading 150, maintaining 7,400 feet, we’ll keep

you informed and will get back to you in five minutes.”

Less than ten seconds had passed since the first boom. De Crespigny cut

power to the left wing and began anti-fire protocols. The plane stopped

vibrating for a moment. Inside the cockpit, alarms were blaring. The pilots

were quiet.

In the cabin, panicked passengers rushed to their windows and pointed at

the screens embedded in their seats, which, unfortunately, were broadcasting

the view of the damaged wing from a camera mounted in the tail.

The men in the cockpit began responding to prompts from the plane’s

computers, speaking to one another in short, efficient sentences. De

Crespigny looked at his display and saw that twenty-one of the plane’s

twenty-two major systems were damaged or completely disabled. The

functioning engines were rapidly deteriorating and the left wing was losing

the hydraulics that made steering possible. Within minutes, the plane had

become capable of only the smallest changes in thrust and the tiniest

navigational adjustments. No one was certain how long it would stay in the

air.

One of the copilots looked up from his controls. “I think we should turn

back,” he said. Turning the airplane around in order to head back to the

airport was risky. But at their current heading, they were getting farther away

from the runway with each second.

De Crespigny told the control tower they would return. He began turning

the plane in a long, slow arc. “Request climb to ten thousand feet,” de

Crespigny radioed to air traffic control.

“No!” his copilots shouted.

They quickly explained their concerns: Climbing higher might strain the

engines. The change in altitude could cause fuel to leak faster. They wanted

to stay low and keep the plane flat.

De Crespigny had flown more than fifteen thousand hours as a pilot and

had practiced disaster scenarios like this in dozens of simulators. He had

envisioned moments like this hundreds of times. He had a picture in his mind

of how to react, and it involved getting higher so he would have more

options. Every instinct told him to gain altitude. But each mental model has

gaps. It was his crew’s job to find them.

“Qantas 32,” de Crespigny radioed. “Disregard the climb to 10,000 feet.

We will maintain 7,400 feet.”

For the next twenty minutes, the men in the cockpit dealt with an increasing

number of alarms and emergencies. The plane’s computer displayed step-by-

step solutions to each problem, but as the issues cascaded, the instructions

became so overwhelming that no one was certain how to prioritize or where

to focus. De Crespigny felt himself getting drawn into a cognitive tunnel.

One computer checklist told the pilots to transfer fuel between the wings in

order to balance the plane’s weight. “Stop!” de Crespigny shouted as a

copilot reached to comply with the screen’s command. “Should we be

transferring fuel out of the good right wing into the leaking left wing?” A

decade earlier, a flight in Toronto had nearly crashed after the crew had

inadvertently dumped their fuel by transferring it into a leaky engine. The

pilots agreed to ignore the order.

De Crespigny slumped in his chair. He was trying to visualize the damage,

trying to keep track of his dwindling options, trying to construct a mental

picture of the plane as he learned more and more about what was wrong.

Throughout this crisis, de Crespigny and the other pilots had been building

mental models of the Airbus inside their heads. Everywhere they looked,

however, they saw a new alarm, another system failing, more blinking lights.

De Crespigny took a breath, removed his hand from the controls, and placed

them in his lap.

“Let’s keep this simple,” he said to his copilots. “We can’t transfer fuel, we

can’t jettison it. The trim tank fuel is stuck in the tail and the transfer tanks

are useless.

“So forget the pumps, forget the other eight tanks, forget the total fuel

quantity gauge. We need to stop focusing on what’s wrong and start paying

attention to what’s still working.”

On cue, one of the copilots began ticking off things that were still

operational: Two of eight hydraulic pumps still functioned. The left wing had

no electricity, but the right wing had some power. The wheels were intact and

the copilots believed de Crespigny could pump the brakes at least once before

they failed.

The first airplane de Crespigny had ever flown was a Cessna, one of the

single-engine, nearly noncomputerized planes that hobbyists loved. A Cessna

is a toy compared to an Airbus, of course, but every plane, at its core, has the

same components: a fuel system, flight controls, brakes, landing gear. What

if, de Crespigny thought to himself, I imagine this plane as a Cessna? What

would I do then?

“That moment is really the turning point,” Barbara Burian, a research

psychologist at NASA who has studied Qantas Flight 32, told me. “When de

Crespigny decided to take control of the mental model he was applying to the

situation, rather than react to the computer, it shifted his mindset. Now, he’s

deciding where to direct his focus instead of relying on instructions.

“Most of the time, when information overload occurs, we’re not aware it’s

happening—and that’s why it’s so dangerous,” Burian said. “So really good

pilots push themselves to do a lot of ‘what if’ exercises before an event,

running through scenarios in their heads. That way, when an emergency

happens, they have models they can use.”

This shift in mindset —What if I imagine this plane as a Cessna?—is what

never occurred, tragically, inside the cockpit of Air France Flight 447. The

French pilots never reached for a new mental model to explain what was

going on. But when the mental model of the Airbus inside de Crespigny’s

head started coming apart under the weight of all the new emergencies, he

decided to replace it with something new. He began imagining the plane as a

Cessna, which allowed him to figure out where he should turn his attention

and what he could ignore.

De Crespigny asked one of his copilots to calculate how much runway they

would need. Inside his head, de Crespigny was envisioning the landing of an

oversized Cessna. “Picturing it that way helped me simplify things,” he told

me. “I had a picture in my head that contained the basics, and that’s all I needed to land the plane.”

If de Crespigny hit everything just right, the copilot said, the plane would

require 3,900 meters of asphalt. The longest runway at Singapore Changi was

4,000 meters. If they overshot, the craft would buckle as its wheels hit the grassy fields and sand dunes.

“Let’s do this,” de Crespigny said.

The plane began descending toward Singapore Changi airport. At two

thousand feet, de Crespigny looked up from his panel and saw the runway. At

one thousand feet, an alarm inside the cockpit began screaming “SPEED!

SPEED! SPEED!” The plane was at risk of stalling. De Crespigny’s eyes

flicked between the runway and his speed indicators. He could see the

Cessna’s wings in his mind. He delicately nudged the throttle, increasing the speed slightly, and the alarm stopped. He brought the nose up a touch

because that’s what the picture in his mind told him to do.

“Confirm the fire services on standby,” a copilot radioed the control tower.

“Affirm, we have the emergency services on standby,” a voice replied.

The plane was descending at fourteen feet per second. The maximum

certified speed the undercarriage could absorb was only twelve feet per

second. But there were no other options now.

“FIFTY,” a computerized voice said. “FORTY.” De Crespigny pulled back

slightly on his stick. “THIRTY…TWENTY.” A metallic voice erupted:

“STALL! STALL! STALL!” The Cessna in de Crespigny’s mind was still

sailing toward the runway, ready to land as he had hundreds of times before.

It wasn’t stalling. He ignored the alarm. The rear wheels of the Airbus

touched the ground and de Crespigny pushed his stick forward, forcing the

front wheels onto the tarmac. The brakes would work only once, so de

Crespigny pushed the pedal as far as it would go and held it down. The first

thousand meters of the runway blurred past. At the two-thousand-meter mark,

de Crespigny thought they might be slowing. The end of the runway was

rushing toward them through the windshield, grass and sand dunes growing

bigger the closer they got. As the plane neared the end of the runway, the

metal began to groan. The wheels left long skid marks on the asphalt. Then

the plane slowed, shuddered, and came to a stop with one hundred meters to

spare.

Investigators would later deem Qantas Flight 32 the most damaged Airbus

A380 ever to land safely. Multiple pilots would try to re-create de

Crespigny’s recovery in simulators and would fail every time.

When Qantas Flight 32 finally came to a rest, the lead flight attendant

activated the plane’s announcement system.

“Ladies and gentlemen,” he said, “welcome to Singapore. The local time is

five minutes to midday on Thursday 4 November, and I think you’ll agree

that was one of the nicest landings we have experienced for a while.” De

Crespigny returned home a hero. Today, Qantas Flight 32 is taught in flight

schools and psychology classrooms as a case study of how to maintain focus

during an emergency. It is cited as one of the prime examples of how mental

models can put even the most dire situations within our control.

Mental models help us by providing a scaffold for the torrent of

information that constantly surrounds us. Models help us choose where to

direct our attention, so we can make decisions, rather than just react. The Air

France pilots didn’t have strong mental models, and so when tragedy struck,

they didn’t know where to focus. De Crespigny and his copilots, in contrast,

were telling themselves stories—and testing and revising them—even before

they stepped onto the plane, and so they were prepared when disaster

occurred.

We may not recognize how situations within our own lives are similar to

what happens within an airplane cockpit. But think, for a moment, about the

pressures you face each day. If you are in a meeting and the CEO suddenly

asks you for an opinion, your mind is likely to snap from passive listening to

active involvement—and if you’re not careful, a cognitive tunnel might

prompt you to say something you regret. If you are juggling multiple

conversations and tasks at once and an important email arrives, reactive

thinking can cause you to type a reply before you’ve really thought out what

you want to say.

So what’s the solution? If you want to do a better job of paying attention to

what really matters, of not getting overwhelmed and distracted by the

constant flow of emails and conversations and interruptions that are part of

every day, of knowing where to focus and what to ignore, get into the habit of

telling yourself stories. Narrate your life as it’s occurring, and then when

your boss suddenly asks a question or an urgent note arrives and you have

only minutes to reply, the spotlight inside your head will be ready to shine the

right way.

To become genuinely productive, we must take control of our attention; we

must build mental models that put us firmly in charge. When you’re driving

to work, force yourself to envision your day. While you’re sitting in a

meeting or at lunch, describe to yourself what you’re seeing and what it

means. Find other people to hear your theories and challenge them. Get in a

pattern of forcing yourself to anticipate what’s next. If you are a parent,

anticipate what your children will say at the dinner table. Then you’ll notice

what goes unmentioned or if there’s a stray comment that you should see as a

warning sign.

“You can’t delegate thinking,” de Crespigny told me. “Computers fail, checklists fail, everything can fail. But people can’t. We have to make

decisions, and that includes deciding what deserves our attention. The key is

forcing yourself to think. As long as you’re thinking, you’re halfway home.”

GOAL SETTING

Smart Goals, Stretch Goals, and the Yom Kippur War

In October 1972, one of Israel’s brightest generals, the forty-four-year-old Eli

Zeira, was promoted to oversee the Directorate of Military Intelligence, the

agency responsible for warning the country’s leaders if its enemies were

about to attack.

Zeira’s appointment came half a decade after the 1967 Six-Day War, in

which Israel, in a stunning preemptive strike, had captured the Sinai

Peninsula, the Golan Heights, and other territory from Egypt, Syria, and

Jordan. That war had demonstrated Israel’s military superiority, more than

doubled the amount of territory the country controlled, and had humiliated

the nation’s enemies. But it also instilled a deep anxiety among Israeli

citizens that the country’s antagonists would eventually seek revenge.

Those fears were legitimate. Since the Six-Day War had ended, generals in

Egypt and Syria had repeatedly threatened to reclaim their lost territory, and

Arab leaders, in fiery speeches, had vowed to push the Jewish state into the

sea. As Israel’s enemies became increasingly bellicose, the nation’s

lawmakers sought to calm public worries by asking the military to provide

regular forecasts on the likelihood of attack.

However, the assessments provided by the Directorate of Military

Intelligence were often contradictory and inconclusive, a mishmash of

opinions predicting various levels of risk. Analysts sent conflicting memos

and flip-flopped week to week. Some weeks, lawmakers were warned to be

on alert, and then nothing would happen. Policy makers were called to

meetings and told that a risk might be materializing, but no one could say for

sure. Army units were ordered to ready their defenses, and then those orders

would be countermanded with no explanation why.

As a result, Israeli politicians and the public became increasingly frustrated. Army reservists constituted 80 percent of the Israeli Defense

Forces’ ground troops. There was a constant nervousness that hundreds of

thousands of citizens would be required, at a moment’s notice, to abandon

their families and rush to the borders. People wanted to know if the risk of another war was real and, if so, how much forewarning they would get.

Eli Zeira was appointed to head the Directorate of Military Intelligence, in

part, to address those uncertainties. He was a former paratrooper known for

his sophistication and political savvy. He had risen quickly through Israel’s

military establishment, even spending a few years as an assistant to Moshe

Dayan, the hero of the Six-Day War. When Zeira took over the Directorate,

he told the Israeli parliament that his job was simple: to provide decision

makers with an “estimate as clear and as sharp as possible.” His chief goal,

he said, was to make sure alarms were raised only when the risks of war were

real.

His method for achieving this clarity was ordering his military analysts to

use a strict formula in assessing Arab intentions. He had helped develop these

criteria, which became known among intelligence officials as “the concept.”

Zeira argued that during the Six-Day War, Israel’s superior airpower, arsenal

of long-range missiles, and battlefield dominance had so thoroughly

embarrassed their enemies that no country would attack again unless they had

an air force powerful enough to protect ground troops from Israeli jets, and

Scud missiles capable of hitting Tel Aviv. Without those two conditions

being met, Zeira said, the threats of Arab leaders were nothing more than

words.

Six months after Zeira assumed his post, the nation had an opportunity to

test his concept. In the spring of 1973, large numbers of Egyptian troops

began amassing along the Suez Canal, which was the border between Egypt

and the Israeli-controlled Sinai Peninsula. Israel’s spies warned that Egypt

planned to invade in mid-May.

On April 18, Israel’s prime minister, Golda Meir, gathered her top advisers

in a closed-door meeting. The military chief of staff and the head of the

Mossad both said an Egyptian attack was a real possibility and the nation

needed to prepare.

Meir turned to Zeira for his assessment. He disagreed with his colleagues,

he said. Egypt still didn’t have a powerful air force and possessed no missiles

capable of reaching Tel Aviv. Egypt’s leaders were merely rattling sabers to

impress their countrymen. The odds of an invasion, he determined, were

“very low.”

Meir ultimately sided with her chief of staff and the Mossad. She ordered

the military to make defensive preparations and, over the next month, the

army readied itself for war. Soldiers built walls, outposts, and batteries along

the hundred-mile-long bank of the Suez Canal. In the Golan Heights, which

bordered Syria, platoons launched practice shells and tanks rehearsed battle

formations. Millions of dollars were spent and thousands of soldiers were

prevented from taking leave. But the attack never materialized. Meir’s

government, chagrined at their overreaction, soon reversed their public

declarations. In July of that year, Moshe Dayan, then Israel’s defense

minister, told Time magazine that it was unlikely a war would occur within the next decade. Zeira emerged from the affair, in the words of the historian

Abraham Rabinovich, “with his reputation, and his self-confidence, greatly

enhanced.

“With alarm bells going off all around him and the nation’s fate at stake, he had coolly maintained throughout the crisis that the probability of war was

not only low, but ‘very low,’ ” Rabinovich wrote. “It was [his] task, he would

say, to keep the national blood pressure down and not sound alarms

unnecessarily. Otherwise, the reserves would be mobilized every couple of

months with devastating effect on the economy and on morale.”

By the summer of 1973, Zeira had established himself as one of Israel’s

most influential leaders. He had assumed his new job with the goal of

reducing needless anxiety, and had demonstrated that a disciplined approach

could prevent wasteful second-guessing. The nation had wanted relief from

the constant worries of an impending attack, and Zeira had provided it. His

ascent to even more powerful positions seemed preordained.

II.

Imagine you have been asked to complete a questionnaire. Your assignment

is to rate how strongly you agree or disagree with forty-two statements,

including:

I believe orderliness and organization are among the most

important characteristics.

I find that establishing a consistent routine enables me to enjoy

life more.

I like to have friends who are unpredictable.

I prefer interacting with people whose opinions are very different

from my own.

My personal space is usually messy and disorganized.

It’s annoying to listen to someone who cannot seem to make up

his or her mind.

A team of researchers at the University of Maryland first published this

test in 1994, and since then it has become a staple of personality exams. At

first glance, the questions seem designed to measure someone’s preference

for personal organization and their comfort with alternate viewpoints. And, in

fact, researchers have found that this exam helps identify people who are

more decisive and self-assured, and that those traits are correlated with

general success in life. Determined and focused people tend to work harder

and get tasks done more promptly. They stay married longer and have deeper

networks of friends. They often have higher-paying jobs.

But this questionnaire is not intended to test personal organization. Rather,

it’s designed to measure a personality trait known as “the need for cognitive

closure,” which psychologists define as “the desire for a confident judgment

on an issue, any confident judgment, as compared to confusion and

ambiguity.” Most people respond to this exam—which is called “the need for

closure scale”—by demonstrating a preference for a mix of order and chaos

in their lives. They say they prize orderliness but admit to having messy desks. They say they are annoyed by indecision but also have unreliable

friends. However, some people—about 20 percent of test takers, and many of

the most accomplished people who have completed the exam—show a

higher-than-average preference for personal organization, decisiveness, and

predictability. They tend to disdain flighty friends and ambiguous situations.

These people have a high emotional need for cognitive closure.

The need for cognitive closure, in many settings, can be a great strength.

People who have a strong urge for closure are more likely to be self-

disciplined and seen as leaders by their peers. An instinct to make a judgment

and then stick with it forestalls needless second-guessing and prolonged

debate. The best chess players typically display a high need for closure,

which helps them focus on a specific problem during stressful moments

rather than obsessing over past mistakes. All of us crave closure to some

degree, and that’s good, because a basic level of personal organization is a

prerequisite for success. What’s more, making a decision and moving on to

the next question feels productive. It feels like progress.

But there are risks associated with a high need for closure. When people

begin craving the emotional satisfaction that comes from making a decision

—when they require a sensation of being productive in order to stay calm—

they are more likely to make hasty decisions and less likely to reconsider an

unwise choice. The “need for closure introduces a bias into the judgmental

process,” a team of researchers wrote in Political Psychology in 2003. A high

need for closure has been shown to trigger close-mindedness, authoritarian

impulses, and a preference for conflict over cooperation. Individuals with a

high need for closure “may display considerable cognitive impatience or

impulsivity: They may ‘leap’ to judgment on the basis of inconclusive

evidence and exhibit rigidity of thought and reluctance to entertain views

different from their own,” the authors of the need for closure scale, Arie

Kruglanski and Donna Webster, wrote in 1996.

Put differently, an instinct for decisiveness is great—until it’s not. When

people rush toward decisions simply because it makes them feel like they are

getting something done, missteps are more likely to occur.

Researchers describe the need for closure as having multiple components.

There is the need to “seize” a goal, as well as a separate urge to “freeze” on

an objective once it has been selected. Decisive people have an instinct to

“seize” on a choice when it meets a minimum threshold of acceptability. This

is a useful impulse, because it helps us commit to projects rather than

endlessly debating questions or second-guessing ourselves into a state of

paralysis.

However, if our urge for closure is too strong, we “freeze” on our goals and yearn to grab that feeling of productivity at the expense of common

sense. “Individuals with a high need for cognitive closure may deny,

reinterpret or suppress information inconsistent with the preconceptions on

which they are ‘frozen,’ ” the Political Psychology researchers wrote. When

we’re overly focused on feeling productive, we become blind to details that

should give us pause.

It feels good to achieve closure. Sometimes, though, we become unwilling

to sacrifice that sensation even when it’s clear we’re making a mistake.

On October 1, 1973, six months after Zeira predicted that the odds of war

were “very low”—and five days before Yom Kippur, the holiest day in the

Jewish calendar—a young Israeli intelligence officer named Binyamin

Siman-Tov sent his commanders in Tel Aviv a warning: He was receiving

reports from the Sinai that large numbers of Egyptian convoys were arriving

at night. Egypt’s military was digging up minefields they had installed along

the border, making it easier for them to move material across the canal. There

were stockpiles of boats and bridge-making supplies on the Egyptian side of

the border. It was the largest buildup of equipment that soldiers on the front

lines had seen.

Zeira had received a number of reports like this in the preceding week, but

they didn’t cause him much concern. Remember the concept, he counseled

his lieutenants: Egypt still didn’t have enough planes or missiles to defeat

Israel. And besides, Zeira had other things to focus on, most notably the

cultural transformation he was pushing through the Directorate of Military

Intelligence. In the midst of remaking the military’s approach to threat

analysis, Zeira was also ridding his agency of its propensity for endless

debate. Henceforth, he had declared, intelligence officers would be evaluated

on the clarity of their recommendations. Both Zeira and his chief lieutenant

“lacked the patience for long and open discussions and regarded them as

‘bullshit,’ ” the historians Uri Bar-Joseph and Abraham Rabinovich wrote.

Zeira would “humiliate officers who, in his opinion, came unprepared for

meetings. At least once he was heard to say that those officers who estimated

in spring 1973 that a war was likely should not expect promotion.” Though

internal debates were tolerated to a point, “once an estimate was formulated

everyone was committed to it and no one was allowed to express a different

estimate outside the organization.”

The Directorate had to lead by example, Zeira declared. He had been

appointed to provide answers, not prolong debates. When one of Zeira’s

subordinates, concerned about the latest reports of Egyptian troop

movements, asked to mobilize a handful of reservists to help analyze what

was going on, he received a phone call. “Yoel, listen well,” Zeira told the

memo writer. “It is intelligence’s job to safeguard the nation’s nerves, not to

drive the public crazy.” The request was denied.

On October 2 and 3, 1973, sightings of Egyptian troops increased. Then

came word of activity on the border with Syria. Alarmed, the prime minister

called another meeting. Zeira’s division, once again, counseled that there was

no reason to be concerned: Egypt and Syria had weak air forces; they had no

missiles capable of hitting Tel Aviv. This time, the military experts who had

disagreed with Zeira six months earlier followed his lead. “I don’t see a

concrete danger in the near future,” one general told the prime minister. Meir

was troubled before the meeting, she later recounted in her memoirs, but the

intelligence estimate eased her mind. She had chosen the right officials to

bring the nation much-needed relief.

Seventy-two hours after Binyamin Siman-Tov submitted his report,

Israel’s intelligence analysts learned that the Soviet Union had started an

emergency airlift of Soviet advisers and their families out of Syria and Egypt.

Intercepted telephone calls among Russian families revealed they had been

ordered to hurry to the airport. Aerial photographs showed more tanks,

artillery, and air-defense guns massing along the Suez Canal and in the

Syrian-controlled portions of the Golan Heights.

On the morning of Friday, October 5, four days after Siman-Tov’s report, a

group of Israel’s top military commanders, including Zeira, gathered in the

office of Defense Minister Moshe Dayan. The hero of the Six-Day War was

upset. The Egyptians had positioned 1,100 pieces of artillery along the Suez, and air reconnaissance showed massive numbers of troops. “You people

don’t take the Arabs seriously enough,” Dayan said. The chief of staff of the

Israeli Defense Forces agreed. Earlier that morning he had ordered the army

to its highest alert since 1967.

But Zeira had another explanation for the troop movements: The Egyptians

were preparing their defenses in case Israel launched an invasion of its own.

There were no new fighter jets in Egypt, he said. No Scud missiles. Arab

leaders knew striking at Israel would be suicide. “I don’t see either the

Egyptians or the Syrians attacking,” Zeira said.

Afterward, the meeting moved into the prime minister’s office. She asked

for an update. The military’s chief of staff, aware that mobilizing Israel’s

reservists on the holiest Jewish holiday would draw fierce criticism, said, “I

still think that they’re not going to attack, but we have no hard information.”

Then Zeira spoke. Concerns about the Egyptians and Syrians attacking, he

said, were “absolutely unreasonable.” He even had a logical reason for the

evacuation of Soviet advisers. “Maybe the Russians think the Arabs are going

to attack because they don’t understand them well,” he said, but the Israelis

knew their neighbors better than that. Later that day, when Israeli generals

briefed the prime minister’s cabinet, Zeira reiterated that he believed there

was a “low probability” of war. What they were seeing were defensive

preparations or a military exercise, Zeira argued. Arab leaders weren’t

irrational.

Having seized on an answer—that Egypt and Syria knew they couldn’t

win, and therefore wouldn’t attack—Zeira was frozen, unwilling to

reconsider the question. His goal of disciplined decision making had been

satisfied.

The next morning was the first full day of Yom Kippur.

Before daybreak, the head of the Mossad telephoned his colleagues to say

that a well-connected source had told him Egypt would invade by nightfall.

The message was delivered to the prime minister as well as Dayan and the

military’s chief of staff. They all rushed to their offices as the sun rose. The

odds of war, they believed, had just shifted.

As Yom Kippur prayers began, Israel’s streets were quiet. Families were

gathered in homes and synagogues. Shortly after ten o’clock, a full six days after enemy forces had started massing along Israeli borders, the military

finally issued a partial call-up of reserves. Inside houses of worship, rabbis read hastily delivered lists with the names of people who needed to report for

duty. By then, Egypt and Syria had been moving tanks and artillery into

offensive range for weeks, but this was the first public hint that trouble might

be near. At that moment, there were more than 150,000 enemy soldiers along

Israel’s borders, ready to attack from two directions, and another half million

soldiers waiting to follow the initial waves. Egypt and Syria had been

coordinating their invasion plans for months. When confidential documents

from that period were released decades later, they revealed that Egypt s

president had assumed Israel knew what he was doing. How else could they

interpret all the men and matériel being moved to the border?

Meir called an emergency meeting of her cabinet for noon. “She was pale

and her eyes were downcast,” The Times of Israel wrote in a reconstruction

of that day. “Her hair, normally neatly combed and pulled back, was

disheveled and she looked like she had not shut her eyes all night….She

began with a detailed report of events over the past few days the Arab

deployment on the borders that had suddenly taken on ominous color, the

hasty evacuation of the families of Soviet advisers from Egypt and Syria, the

air photos, the insistence by military intelligence that there would be no war

despite mounting evidence to the contrary.” Meir announced her conclusion:

An invasion of Israel was likely to occur, maybe as soon as within the next

six hours.

“The ministers were stunned,” The Times of Israel reported. “They had not

been made privy to the Arab buildup. Furthermore, they had been told for

years that even in a worst-case situation, military intelligence would provide

at least a 48-hour warning to call up the reserves before war broke out.” Now

they were being informed that a two-front war would occur in less than six

hours. The reserves were only partially mobilized—and because of the

holiday, it was unclear how quickly troops would be able to get to the front.

The attack came even sooner than Meir expected. Two hours after the

cabinet meeting started, the first of ten thousand Egyptian shells began falling

on the Sinai; at four P.M., twenty-three thousand Egyptian soldiers crossed the

Suez in the first wave of attack. By the end of the day, enemy forces were two miles into Israeli territory. They had killed five hundred Israeli soldiers

and were rapidly advancing toward the Israeli towns of Yamit and Avshalom,

as well as an Israeli air force base. Meanwhile, on the other side of the

country, Syria struck simultaneously, attacking the Golan Heights with

soldiers, planes, and tanks.

Over the next twenty-four hours, Egypt and Syria pushed deeper into the

Sinai and the Golan as Israel scrambled to respond. Over a hundred thousand

enemy troops were inside Israel’s territory. It took three days to halt the

Egyptian advance, and two days to organize a counterstrike against Syria.

Eventually, Israel’s superior firepower asserted itself. Israeli soldiers drove

the Syrian army back toward the border, forcing the retreating army to leave

behind 1,000 of its 1,500 tanks. A few days later, Israeli Defense Forces

began shelling the outskirts of Damascus.

Then Egyptian president Anwar Sadat, hoping to take more territory in the

Sinai, launched a risky offensive to capture two strategic passes deep inside the peninsula. The gamble failed. Israeli forces pushed the Egyptians

backward. On October 15, nine days after Egypt invaded, Israel crossed the

Suez Canal and began taking Egyptian land. Within the week, Egypt’s Third

Army, located along the banks of the Suez, was encircled, cut off by the

Israelis from supplies and reinforcements. The Second Army, in the north,

was almost completely surrounded as well. In the face of defeat, President

Sadat demanded a cease-fire, and American and Soviet leaders pressured

Israel to agree. Fighting stopped in late October, and the war formally ended

on January 18, 1974. Israel had repelled the invasion, but at a huge cost.

More than ten thousand Israelis were killed or wounded. As many as thirty

thousand Egyptians and Syrians are estimated to have died.

“Something of ours was destroyed on Yom Kippur last year,” an Israeli

newspaper wrote on the first anniversary of the war. “The state was saved,

true, but our faith was fractured, our trust damaged, our hearts deeply

gouged, and an entire generation was nearly lost.”

“Even a quarter century later the Yom Kippur war remains the most

traumatic phase in Israel’s history,” the historian P. R. Kumaraswamy wrote.

Today, the psychological scars of the invasion are still profound.

Zeira had set out with a goal of alleviating public anxiety, and the

government had followed his lead. But in their eagerness to provide confident

answers, to make decisive judgments and avoid ambiguity, those leaders had

almost cost Israel its life.

III.

Fifteen years later and half a world away, General Electric, one of the largest

companies on earth, was thinking about very different kinds of goals when

executives contacted an organizational psychologist from the University of

Southern California and asked him for help figuring out why some factories

had gone awry.

It was the late 1980s and GE was the second most valuable company in

America, just behind Exxon. GE manufactured everything from lightbulbs to

jet engines, refrigerators to railway cars, and through its ownership of NBC,

was in millions of homes with iconic shows such as Cheers, The Cosby Show,

and L.A. Law. The company employed over 220,000 people, more than many

U.S. cities had residents. One of the reasons GE was so successful, its

executives boasted, was that it was so good at choosing goals.

In the 1940s, GE had formalized a corporate goal-setting system that

would eventually become a model around the world. By the 1960s, every GE

employee was required to write out their objectives for the year in a letter to

their manager. “Simply put,” historians at Harvard Business School wrote in

2011, “the manager’s letter required a job holder to write a letter to his or her

superior indicating what the goals for the next time frame were, how the

goals would be met, and what standards were to be expected. When the

superior accepted this letter—usually after editing and discussion—it became

the work ‘contract.’ ”

By the 1980s, this system had evolved into a system of so-called SMART

goals that every division and manager were expected to describe each quarter.

These objectives had to be specific, measurable, achievable, realistic, and

based on a timeline. In other words, they had to be provably within reach and

described in a way that suggested a concrete plan.

If a goal didn’t meet the SMART criteria, a manager had to resubmit a

memo detailing their aims, again and again, until it was approved by upper

management. “It was about getting concrete,” said William Conaty, who

retired as GE’s head of human resources in 2007. “Your manager was always

saying, what’s the specifics? What’s the timeline? Prove to me this is

realistic. The system worked because by the time we were done, you knew

pretty clearly how things were going to unfold.”

The SMART mindset spread throughout GE’s culture. There were

SMART charts to help midlevel managers describe monthly goals and

SMART worksheets to turn personal objectives into action plans. And the

company’s belief that SMART goals would work was rooted in good science.

In the 1970s, a pair of university psychologists named Edwin Locke and

Gary Latham had helped develop the SMART criteria through experiments scrutinizing the best way to set goals. In one experiment Latham conducted in

1975, researchers approached forty-five of the most experienced and

productive typists at a large corporation and measured how fast they

produced text. The typists knew they were among the best in the company,

but they had never measured how quickly they typed. The researchers found

that, on average, each worker produced ninety-five lines of typewritten

output per hour.

Then the researchers gave each typist a specific goal based on their

previous performance—such as ninety-eight lines per hour—and showed the

typists a system for easily measuring their hourly output. The researchers also

had a conversation with each typist to make sure the goal was realistic—and

to adjust it if necessary—and they discussed what changes were required to

make the objective achievable. They came up with a timeline for each person.

The conversation didn’t take long—say, fifteen minutes per person—but

afterward each typist knew exactly what to do and how to measure success.

Each of them, put differently, had a SMART goal.

Some of the researchers’ colleagues said they didn’t believe this would

have an impact on the typists’ performance. All the typists were professionals

with years of experience. A fifteen-minute conversation should not make

much of a difference to someone who has been typing eight hours a day for

two decades.

But one week later, when the researchers measured typing speeds again,

they found that the workers, on average, were completing 103 lines per hour.

Another week later: 112 lines. Most of the typists had blown past the goals

they had set. The researchers worried the workers were just trying to impress

them, so they came back again, three months later, and quietly measured

everyone’s performance once more. They were typing just as fast, and some

had gotten even faster.

“Some 400 laboratory and field studies [show] that specific, high goals

lead to a higher level of task performance than do easy goals or vague,

abstract goals such as the exhortation to ‘do one’s best,’ ” Locke and Latham

wrote in 2006 in a review of goal-setting studies. In particular, objectives like

SMART goals often unlock a potential that people don’t even realize they

possess. The reason, in part, is because goal-setting processes like the

SMART system force people to translate vague aspirations into concrete plans. The process of making a goal specific and proving it is achievable

involves figuring out the steps it requires—or shifting that goal slightly, if your initial aims turn out to be unrealistic. Coming up with a timeline and a

way to measure success forces a discipline onto the process that good

intentions can’t match.

“Making yourself break a goal into its SMART components is the

difference between hoping something comes true and figuring out how to do

it,” Latham told me.

GE’s chief executive Jack Welch had long claimed that his insistence on

SMART goals was one of the reasons the company’s stock had more than

tripled in eight years. But forcing people to detail their goals with such

specificity didn’t mean every part of the company ran smoothly. Some

divisions, despite setting SMART goals, never seemed to excel—or they

would flip-flop from profits to losses, or seem to be growing and then

suddenly fall apart. In the late 1980s, executives became particularly

concerned about two divisions—a nuclear equipment manufacturer in North

Carolina and a jet engine plant in Massachusetts—that had once been among

the company’s top performers but were now limping along.

Executives initially suspected those divisions simply needed better-defined

objectives, so factory managers were asked to prepare memo after memo

describing increasingly specific goals. Their responses were detailed, precise,

and realistic. They met every SMART criterion.

And yet, profits still fell.

So a group of GE’s internal consultants visited the nuclear factory in

Wilmington, North Carolina. They asked employees to walk them through

their weekly, monthly, and quarterly goals. One plant executive explained

that his SMART objective was to prevent antinuclear protesters from

harassing workers as they entered the plant, because he felt it eroded morale.

He had come up with a SMART plan to build a fence. The goal was specific

and reasonable (the fence would be fifty feet long and nine feet high), it had a

timeline (it would be done by February), and it was achievable (they had a

contractor ready to go).

Next, the consultants went to the jet engine factory in Lynn,

Massachusetts, and interviewed, among others, an administrative assistant

who told them her SMART goal was ordering the factory’s office supplies.

She showed them a SMART chart with specific aims (“order staplers, pens

and desk calendars”) that were measurable (“by June”), as well as achievable,

realistic, and had a timeline (“Place order on February 1. Request update on

March 15.”).

Many of the SMART goals the consultants found inside the factories were

just as detailed—and just as trivial. Workers spent hours making sure their

objectives satisfied every SMART criterion, but spent much less time making

sure the goals were worth pursuing in the first place. The nuclear factory’s

security guards had written extensive memos on the goal of theft prevention

and had come up with a plan that “basically consisted of searching

everyone’s bags every time they entered or exited the plant, which caused

huge delays,” said Brian Butler, one of the consultants. “It might have

stopped thefts, but it also destroyed the factory’s productivity because

everyone started leaving earlier each day so they could get home at a decent

hour.” Even the plants’ senior executives, the consultants found, had fallen

prey to an obsession with achievable but inconsequential goals, and were

focused on unimportant short-term objectives rather than more ambitious

plans.

When the consultants asked employees how they felt about GE’s emphasis

on SMART goals, they expected to hear complaints about the onerous

bureaucracy. They anticipated people would say they wanted to think bigger,

but were hamstrung by the incessant SMART demands. Instead, employees

said they loved the SMART system. The administrative assistant who ordered

office supplies said fulfilling those goals gave her a real sense of

accomplishment. Sometimes, she said, she would write a SMART memo for

a task she had already completed and then put it into her “Done” folder. It made her feel so good.

Researchers who have studied SMART goals and other structured methods

of choosing objectives say this isn’t unusual. Such systems, though useful,

can sometimes trigger our need for closure in counterproductive ways. Aims

such as SMART goals “can cause [a] person to have tunnel vision, to focus

more on expanding effort to get immediate results,” Locke and Latham wrote

in 1990. Experiments have shown that people with SMART goals are more

likely to seize on the easiest tasks, to become obsessed with finishing

projects, and to freeze on priorities once a goal has been set. “You get into this mindset where crossing things off your to-do list becomes more

important than asking yourself if you’re doing the right things,” said Latham.

GE’s executives weren’t sure how to help the nuclear and jet engine

factories. So in 1989, they asked a professor named Steve Kerr, the dean of

faculty at the University of Southern California business school, for help.

Kerr was an expert in the psychology of goal setting, and he began by

interviewing employees inside the nuclear factory. “A lot of these people

were really demoralized,” he said. “They had gone into nuclear energy

because they wanted to change the world. Then Three Mile Island and

Chernobyl happened, and the industry was getting protested every day and

completely brutalized in the press.” Setting short-term goals and achieving

them, the plant’s workers and executives told Kerr, was one of the few things

they could feel good about at work.

The only way to improve performance at the nuclear factory, Kerr thought,

was to find a way to shake people out of their focus on short-term objectives.

GE had recently started a series of meetings among top executives called

“Work-Outs” that were designed to encourage people to think about bigger

ambitions and more long-term plans. Kerr helped expand those meetings to

factories’ rank-and-file.

The rules at Work-Outs were simple: Employees could suggest any goal

they thought GE ought to be pursuing. There were no SMART charts or

memos. “The concept was that nothing was off-limits,” Kerr told me.

Managers had to approve or deny each suggestion quickly, often right away,

and “we wanted to make it easy to say yes,” said Kerr. “We thought if we

could get people to identify the ambition first, and then figure out the plan afterward, it would encourage bigger thinking.” If an idea seemed half-baked,

Kerr said, a manager should “say yes, because even if the proposal is no

better than what you’re doing now, with the group’s energy behind it, the

plan will turn out great.” Only after a goal was approved would everyone

begin the formal process of determining how to make it realistic and

achievable and all the other SMART criteria.

At a Work-Out inside the engine factory in Massachusetts, one worker told

his bosses they were making a mistake by outsourcing construction of

protective shields for their grinding machines. The factory could make them

in-house for half the cost, he said. Then he unfurled a piece of butcher paper covered with scribbled blueprints. There was nothing SMART about the

man’s proposal. It was unclear if it was realistic or achievable, or what

measurements to apply. But when the factory’s top manager looked at the

butcher paper, he said, “I guess we’ll try it out.”

Four months later, after the blueprints had been professionally redrawn and

the plan transformed into a series of SMART goals, the first prototype was

installed. It cost $16,000—more than 80 percent less than the outsourced bid.

The factory saved $200,000 that year on ideas proposed at the Work-Out.

“Everybody gets caught up in this tremendous rush of adrenaline,” a team

leader at the plant, Bill DiMaio, said. “The ideas that people come up with

are so encouraging, it’s unbelievable. These people get psyched. All their

ideas are fair game.”

Then Kerr helped take the Work-Out program company-wide. By 1994,

every GE employee within GE had participated in at least one Work-Out. As

profits and productivity rose, executives at other companies began imitating

the Work-Out system inside their own firms. By 1995, there were hundreds

of companies conducting Work-Outs. Kerr joined GE full-time in 1994 and

eventually became the company’s “chief learning officer.”

“The Work-Outs were successful because they balanced the psychological

influence of immediate goals with the freedom to think about bigger things,”

said Kerr. “That’s critical. People respond to the conditions around them. If

you’re being constantly told to focus on achievable results, you’re only going

to think of achievable goals. You’re not going to dream big.”

Work-Outs, however, weren’t perfect. They took an entire day of

everyone’s time and usually meant the plant had to slow down production so

that workers could all attend the meetings. It was something a division or

plant could do once or twice a year, at most. And though the Work-Outs left

everyone feeling excited and hungry for change, the effects were frequently

short-lived. A week later, everyone was back at their old jobs and, often, their

old ways of thinking.

Kerr and his colleagues wanted to foster perpetual ambitions. How, they

wondered, do you get people to think expansively all the time?

IV.

In 1993, twelve years after becoming chief executive of General Electric,

Jack Welch traveled to Tokyo and, while touring a factory that made medical

testing equipment, heard a story about Japan’s railway system.

In the 1950s, during the lingering wake of the devastation of the Second

World War, Japan was intensely focused on growing the nation’s economy.

A large portion of the country’s population lived in or between the cities of

Tokyo and Osaka, which were separated by just 320 miles of train track.

Every day, tens of thousands of people traveled between the cities. Vast

amounts of raw industrial materials were transported on those rail lines. But

the Japanese topography was so mountainous and the railway system so

outdated that the trip could take as long as twenty hours. So, in 1955, the head of the Japanese railway system issued a challenge to the nation’s finest

engineers: invent a faster train.

Six months later, a team unveiled a prototype locomotive capable of going

65 miles per hour—a speed that, at the time, made it among the fastest

passenger trains in the world. Not good enough, the head of the railway

system said. He wanted 120 miles per hour.

The engineers explained that was not realistic. At those speeds, if a train

turned too sharply, the centrifugal force would derail the cars. Seventy miles

an hour was more realistic—perhaps 75. Any faster and the trains would

crash.

Why do the trains need to turn? the railway head asked.

There were numerous mountains between the cities, the engineers replied.

Why not make tunnels, then?

The labor required to tunnel through that much territory could equal the

cost of rebuilding Tokyo after World War II.

Three months later, the engineers unveiled an engine capable of going 75

miles per hour. The railway chief lambasted the designs. Seventy-five miles

per hour, he said, had no chance of transforming the nation. Incremental

improvements would only yield incremental economic growth. The only way

to overhaul the nation’s transportation system was to rebuild every aspect of

how trains functioned.

Over the next two years, the engineers experimented: They designed train

cars that each had their own motors. They rebuilt gears so they meshed with

less friction. They discovered that their new cars were too heavy for Japan’s

existing tracks, and so they reinforced the rails, which had the added bonus of

increasing stability, which added another half mile per hour to cars’ speed.

There were hundreds of innovations, large and small, that each made the

trains a little bit faster than before.

In 1964, the Tōkaidō Shinkansen, the world’s first bullet train, left Tokyo

along continuously welded rails that passed through tunnels cut into Japan’s

mountains. It completed its inaugural trip in three hours and fifty-eight

minutes, at an average speed of 120 miles per hour. Hundreds of spectators

had waited overnight to see the train arrive in Osaka. Soon other bullet trains

were running to other Japanese cities, helping fuel a dizzying economic

expansion. The development of the bullet train, according to a 2014 study,

was critical in spurring Japan’s growth well into the 1980s. And within a

decade of that innovation, the technologies developed in Japan had given

birth to high-speed rail projects in France, Germany, and Australia, and had

revolutionized industrial design around the world.

For Jack Welch, this story was a revelation. What GE needed, he told Kerr

when he got home from Japan, was a similar outlook, an institutional

commitment to audacious goals. Going forward, every executive and

department, in addition to delivering specific and achievable and timely

objectives, would also have to identify a stretch goal—an aim so ambitious that managers couldn’t describe, at least initially, how they would achieve it.

Everyone, Welch said, had to partake in “bullet train thinking.”

In a 1993 letter to shareholders, the chief executive explained that “stretch

is a concept that would have produced smirks, if not laughter, in the GE of three or four years ago, because it essentially means using dreams to set

business targets—with no real idea of how to get there. If you do know how

to get there—it’s not a stretch target.”

Six months after Welch’s trip to Japan, every division at GE had a stretch

goal. The division manufacturing airplane engines, for instance, announced

they would reduce the number of defects in finished engines by 25 percent.

To be honest, the division’s managers figured they could hit that target pretty

easily. Almost all the defects they found on engines were small, cosmetic

issues, such as a slightly misaligned cable or unimportant scratches. Anything

more serious was corrected before the engine was shipped. If they hired more

quality assurance employees, managers figured, they could reduce cosmetic

defects with little effort.

Welch agreed that reducing defects was a wise goal.

Then he told them to cut errors by 70 percent.

That’s ridiculous, managers said. Manufacturing engines was such a

complicated affair—each one weighed five tons and had more than ten

thousand parts—that there was no way they could achieve a 70 percent

reduction.

They had three years, Welch said.

The division’s managers started panicking—and then began analyzing

every error that had been recorded in the previous twelve months. Simply

hiring more quality assurance workers, they quickly realized, wouldn’t do the

trick. The only way to reduce errors by 70 percent was to make every single

employee, in effect, a quality assurance auditor. Everyone had to take

responsibility for catching mistakes. But most factory workers didn’t know

enough about the engines to identify every small defect as it occurred. The

only solution, managers decided, was a massive retraining effort.

Except that didn’t really work, either. Even after nine months of retraining,

the error rate had fallen by only 50 percent. So managers started hiring

workers with more technical backgrounds, the kind of people who knew what

an engine ought to look like and, therefore, could more easily spot what was

amiss. The GE factory manufacturing CF6 engines in Durham, North

Carolina, determined that the best way to find the right employees was to hire

only candidates with FAA certification in engine manufacturing. Such

workers, however, were already in high demand at other plants. So to attract

them, managers said employees could have more autonomy. They could

schedule their own shifts and organize teams however they wanted. That

required the plant to do away with centralized scheduling. Teams had to self-

organize and figure out their own workflow.

Welch had given his aircraft manufacturing division a stretch goal of reducing errors by 70 percent, an objective so audacious the only way to go

about it was to change nearly everything about (a) how workers were trained,

(b) which workers were hired, and (c) how the factory ran. By the time they

were done, the Durham plant’s managers had collapsed organizational charts,

remade job duties, and overhauled how they interviewed candidates, because

they needed people with better team skills and more flexible mindsets. In

other words, Welch’s stretch goal set off a chain reaction that remade how

engines were manufactured in ways no one had imagined. By 1999, the

number of defects per engine had fallen by 75 percent and the company had

gone thirty-eight months without missing a single delivery, a record. The cost

of manufacturing had dropped by 10 percent every year. No SMART goal

would have done that.

Numerous academic studies have examined the impact of stretch goals,

and have consistently found that forcing people to commit to ambitious,

seemingly out-of-reach objectives can spark outsized jumps in innovation and

productivity. A 1997 study of Motorola, for instance, found that the time it

took engineers to develop new products fell tenfold after the company

mandated stretch goals throughout the firm. A study of 3M said stretch goals

helped spur such inventions as Scotch tape and Thinsulate. Stretch goals

transformed Union Pacific, Texas Instruments, and public schools in

Washington, D.C., and Los Angeles. Surveys of people who have lost large

amounts of weight or have become marathon runners later in life have found

that stretch goals are often integral to their success.

Stretch goals “serve as jolting events that disrupt complacency and

promote new ways of thinking,” a group of researchers wrote in Academy of

Management Review business journal in 2011. “By forcing a substantial

elevation in collective aspirations, stretch goals can shift attention to possible

new futures and perhaps spark increased energy in the organization. They

thus can prompt exploratory learning through experimentation, innovation,

broad search, or playfulness.”

There is an important caveat to the power of stretch goals, however.

Studies show that if a stretch goal is audacious, it can spark innovation. It can

also cause panic and convince people that success is impossible because the

goal is too big. There is a fine line between an ambition that helps people

achieve something amazing and one that crushes morale. For a stretch goal to inspire, it often needs to be paired with something like the SMART system.

The reason why we need both stretch goals and SMART goals is that

audaciousness, on its own, can be terrifying. It’s often not clear how to start

on a stretch goal. And so, for a stretch goal to become more than just an

aspiration, we need a disciplined mindset to show us how to turn a far-off

objective into a series of realistic short-term aims. People who know how to

build SMART goals have often been habituated into cultures where big

objectives can be broken into manageable parts, and so when they encounter

seemingly outsized ambitions, they know what to do. Stretch goals, paired

with SMART thinking, can help put the impossible within reach.

In one experiment conducted at Duke University, for instance, varsity

athletes were asked to run around a track and, when signaled, get as close as

possible to a finish line 200 meters away within ten seconds. The runners in

the study all knew, simply by looking at the distance they were being asked

to cover, that the goal was absurd. No person has ever run anything close to

200 meters in ten seconds. The athletes made it 59.6 meters, on average,

during their sprint.

A few days later, those same participants were presented with the same

task, but this time the finish line was only 100 meters away. The goal was

still audacious—but it was within the realm of possibility. (Usain Bolt ran

100 meters in 9.58 seconds in 2009.) During this trial, the runners made it, on

average, 63.1 meters in ten seconds—“a large difference by track and field

standards,” the researchers noted.

This difference in performance was explained by the fact that the shorter

distance, while still challenging, lent itself to the kind of methodical planning

and mental models that experienced runners are accustomed to using. The

shorter distance, in other words, allowed the runners to participate in the

athletic equivalent of breaking a stretch goal into SMART components. “All

runners in our sample engaged in regular workouts,” the researchers wrote,

and so when confronted with running 100 meters in ten seconds, they knew

how to wrestle with the task. They broke it into pieces and treated it like they

would other sprints. They started strong, and paced off other runners, and

then pushed themselves as hard as possible in the final seconds. But when

they were confronted with running 200 meters in ten seconds, there was no

practical approach. There was no way to break the problem into manageable parts. There were no SMART criteria they could apply. It was simply

impossible.

Experiments at the University of Waterloo, the University of Melbourne,

and elsewhere show similar results: Stretch goals can spark remarkable

innovations, but only when people have a system for breaking them into

concrete plans.

This lesson can extend to even the most mundane aspects of life. Take, for

instance, to-do lists. “To-do lists are great if you use them correctly,”

Timothy Pychyl, a psychologist at Carleton University, told me. “But when

people say things like ‘I sometimes write down easy items I can cross off

right away, because it makes me feel good,’ that’s exactly the wrong way to

create a to-do list. That signals you’re using it for mood repair, rather than to

become productive.”

The problem with many to-do lists is that when we write down a series of

short-term objectives, we are, in effect, allowing our brains to seize on the sense of satisfaction that each task will deliver. We are encouraging our need

for closure and our tendency to freeze on a goal without asking if it’s the right aim. The result is that we spend hours answering unimportant emails

instead of writing a big, thoughtful memo—because it feels so satisfying to

clean out our in-box.

At first glance, it might seem like the solution is creating to-do lists filled

solely with stretch goals. But we all know that merely writing down grand

aspirations doesn’t guarantee we will achieve them. In fact, studies show that

if you’re confronted with a list of only far-reaching objectives, you’re more

likely to get discouraged and turn away.

So one solution is writing to-do lists that pair stretch goals and SMART

goals. Come up with a menu of your biggest ambitions. Dream big and

stretch. Describe the goals that, at first glance, seem impossible, such as

starting a company or running a marathon.

Then choose one aim and start breaking it into short-term, concrete steps.

Ask yourself: What realistic progress can you make in the next day, week,

month? How many miles can you realistically run tomorrow and over the

next three weeks? What are the specific, short-term steps along the path to

bigger success? What timeline makes sense? Will you open your store in six

months or a year? How will you measure your progress? Within psychology, these smaller ambitions are known as “proximal goals,” and repeated studies

have shown that breaking a big ambition into proximal goals makes the large

objective more likely to occur.

When Pychyl writes a to-do list, for instance, he starts by putting a stretch

goal—such as “conduct research that explains goal/neurology interface”—at

the top of the page. Underneath comes the nitty-gritty: the small tasks that tell

him precisely what to do. “Specific: Download grant application. Timeline:

By tomorrow.”

“That way, I’m constantly telling myself what to do next, but I’m also

reminded of my larger ambition so I don’t get stuck in the weeds of doing

things simply to make myself feel good,” Pychyl said.

In short, we need stretch and SMART goals. It doesn’t matter if you call

them by those names. It’s not important if your proximal goals fulfill every

SMART criterion. What matters is having a large ambition and a system for

figuring out how to make it into a concrete and realistic plan. Then, as you check the little things off your to-do list, you’ll move ever closer to what really matters. You’ll keep your eyes on what’s both wise and SMART.

“I had no idea how what we were doing would affect the rest of the

world,” Kerr told me. GE’s embrace of SMART and stretch goals has been

analyzed in academic studies and psychology textbooks; the firm’s system

has been imitated throughout corporate America. “We proved you can change

how people act by asking them to think about goals differently,” said Kerr.

“Once you know how to do that, you can get pretty much anything done.”

V.

Twenty-seven days after fighting concluded in the Yom Kippur War, the

Israeli parliament established a national committee of inquiry to examine why

the nation had been so dangerously unprepared. Officials met for 140

sessions and heard testimony from fifty-eight witnesses, including Prime

Minister Golda Meir, Defense Minister Moshe Dayan, and the head of the

Directorate of Military Intelligence, Eli Zeira.

“In the days that preceded the Yom Kippur war, the Research Division of

Military Intelligence had plenty of warning indicators,” investigators

concluded. There was no justification for Israel to have been caught off

guard. Zeira and his colleagues had ignored obvious signs of danger. They

had dissuaded other leaders from following their instincts. These mistakes

were not made out of malice, investigators said, but because Zeira and his

staff had become so obsessed with avoiding unnecessary panic and making

firm decisions that they lost sight of their most important objective: keeping

Israelis safe.

Prime Minister Meir resigned one week after the government’s report was

released. Moshe Dayan, the onetime hero, was hounded by critics until his

death six years later. And Zeira was relieved of his position and forced to

resign from government service.

Zeira’s failings in the run-up to the Yom Kippur War illustrate one final

lesson regarding how goals function and influence our psychology. He, in

fact, was using both stretch and SMART goals when he convinced the

nation’s leaders to ignore obvious signs of war. He had clear and grand

ambitions to end the cycle of anxiety plaguing Israelis; he knew that his big

aim was to stop the endless debates and second-guessing. And his methods

for breaking those larger goals into smaller pieces involved finding proximal

goals that were specific, measurable, achievable, and realistic, and that

occurred according to a timeline. He remade his agency in a deliberate, step-

by-step manner. He did everything that psychologists like Latham and Locke

have said we ought to do in order to achieve both big and small goals.

Yet Zeira’s craving for closure and his intolerance for revisiting questions once they were answered are among the biggest reasons why Israel failed to

anticipate the attacks. Zeira is an example of how stretch and SMART goals,

on their own, sometimes aren’t enough. In addition to having audacious

ambitions and plans that are thorough, we still need, occasionally, to step

outside the day-to-day and consider if we’re moving toward goals that make

sense. We still need to think.

On October 6, 2013, the fortieth anniversary of the Yom Kippur War, Eli

Zeira addressed an audience of national security scholars in Tel Aviv. He was

eighty-five years old and his gait was a bit unsteady as he walked onto the stage. He spoke haltingly from handwritten notes. He had come to defend

himself, he said. Mistakes had been made, but not just by him. Everyone had

learned they needed to be more careful and less certain. They were all to

blame.

A former colleague in the audience began heckling him.

“You are telling us fairy tales!” the man shouted. “You are lying!”

“This is not a field court marshal,” Zeira replied. The war was not his fault

alone, he said. No one had been willing to stare the most terrifying possibility

—a full-scale invasion—in the face.

But then, in a moment of reflection, Zeira conceded that he had made an

error. He had ignored the seemingly impossible. He hadn’t thought through

all the alternatives as deeply as he should.

“I usually had a note in my pocket,” he told the audience, “and on that little

note, it said, ‘and if not?’ ” The note was a talisman, a reminder that the desire to get something done, to be decisive, can also be a weakness. The

note was supposed to prompt him to ask bigger questions.

But in the days before the Yom Kippur War, “I didn’t read that little note,”

Zeira said. “That was my mistake.”

MANAGING OTHERS

Solving a Kidnapping with Lean and Agile Thinking and a Culture of Trust

Frank Janssen had just returned home from a bike ride when he heard a

knock at the front door. It was a sunny Saturday morning; there were kids

playing soccer a few blocks away. When Janssen looked out the window, he

saw a woman holding a clipboard and two men dressed in khakis and button-

down shirts. Perhaps they were conducting a survey? Or they were religious

missionaries? Janssen didn’t know why they were on his doorstep, but he

hoped it wouldn’t take more than a moment to shoo them away.

When he opened the door, however, the men pushed their way inside. One

of them grabbed Janssen, shoved him against the wall, and then threw him to

the floor. He pulled a gun from his waistband and slammed the barrel across

Janssen’s face. The other man pressed a stun gun against Janssen’s torso and

pulled the trigger, momentarily paralyzing the sixty-three-year-old. Then they

bound his hands with a plastic zip tie and carried him outside, into the

backseat of a silver Nissan waiting in the driveway. The two men sat on

either side of Janssen while the woman sat up front, next to the driver. As Janssen slowly regained control of his body, he began shoving his attackers.

They pushed him to the floor and applied the stun gun again. The car backed

onto the street and headed west, past the field where kids were playing

soccer. One of the assailants draped a blanket over Janssen’s body. The

vehicle turned onto a freeway and slipped into southbound traffic.*

Janssen’s wife came home about an hour later and found the house empty

and the front door ajar. Frank’s bike was propped against the garage. Maybe

he had gone for a walk? An hour later, with no sign of him, his wife grew concerned. She searched the entryway, thinking he might have left a note. On

the doorstep, she saw a few drops of blood. Panicked, she walked toward the

driveway and found more blood outside. She phoned her daughter, who told

her to call the police.

Her husband, she explained to officers, was a consultant at a firm

specializing in national security. Soon her home was surrounded by police

cruisers and yellow crime-scene tape. Black SUVs pulled up, delivering a

team of FBI agents who dusted for prints and photographed indentations in

the grass. For the next two days, agents pored over Janssen’s cellphone

records and interviewed neighbors and coworkers, but found nothing to

indicate what was going on.

Then, three days after the abduction, in the middle of the night of April 7,

2014, his wife’s phone buzzed. It was a series of text messages from an

unfamiliar number with a New York City area code.

We have your husband, the texts read, and he is in the trunk of a car going

to California. If she contacted the police, we will send him back to you in 6

boxes and every chance we get we will take someone in you family to italy and torture them and kill them, we will do a drive by and gun down anybody

in you family and we will throw grenades in you window.

The texts also referenced Janssen’s daughter and a man named Kelvin

Melton. Suddenly, things started making a bit more sense. Janssen’s

daughter, Colleen, was an assistant district attorney in nearby Wake Forest,

and she had prosecuted Melton, a high-ranking gang member in the Bloods, a

few years earlier. Colleen had successfully sent Melton to jail for the rest of

his life on a charge of assault with a deadly weapon. A theory began to

emerge: Government investigators suspected that the Bloods had kidnapped

Frank Janssen to punish his daughter. This was revenge for putting one of

their leaders behind bars.

Within hours, the police had subpoenaed the records of the phone sending

the texts looking for a link with known gang members. They could tell the

messages had been transmitted from Georgia, but the device was a burner, an

unregistered phone bought with cash at a Walmart. There was nothing in the

cellular records or purchase receipts that told investigators who owned the

phone or where it was currently located.

Two days later, another text arrived from a different number, this time with

an Atlanta area code. Here is 2 picture of you husband, it read and included

photos of Janssen tied to a chair. If you can not tell me where my things are at

tomorrow i will start torchering colleen father. None of the investigators had

any idea what “things” the kidnappers were referring to. The texts also demanded that someone bring Melton, the incarcerated gang leader, a pack of

cigarettes, as well as other commands. Jefe wants his things and he needs to

get another phone fast so we can finish our business and if I don’t get word

from him very very fast then we have problems with his people. The police didn’t know if “Jefe” referred to Melton or someone else, or why Melton

would want cigarettes delivered to him since he could buy them inside the

Polk Correctional Institution. More texts arrived with references to unknown

people. Now he know ah playin games, one said. Tell him we got franno, tell

him he better find a way to tell me where my things at an get my money or we

kill these people in 2 days. Investigators were confused by these mentions of

“Jefe” and “Franno,” and the threats to kill multiple people even though

authorities were aware of only one kidnapping victim. If this was a revenge

plot, why were the kidnappers sending so many ambiguous messages? Why

hadn’t they made any ransom demands? One federal agent thought the

kidnappers were acting as if they weren’t sure what was going on themselves,

as if they didn’t have a plan.

The FBI asked Google to look for searches around the time of the

abduction that had included Janssen’s address. The computer giant reported

that someone using a disposable T-Mobile phone had Googled “Colleen

Janssen address,” but what had come up was her parents’ home, where she

had once lived. A new theory emerged: The kidnappers had intended to

kidnap Colleen as revenge for prosecuting Kelvin Melton, but had

accidentally grabbed her father.

Investigators determined that the Georgia phone sending the latest texts

was also a burner, but this time, when agents approached cellular companies,

their records proved more fruitful. The texts had been sent from Atlanta.

Moreover, the phone had recently received a call from another number,

which had been sending and receiving texts with yet another phone that

police were able to determine was located inside the walls of Polk

Correctional itself. That phone had placed almost a hundred calls to Melton’s

daughters.

The kidnapping, investigators came to believe, was being directed by

Melton himself.

The FBI phoned Polk Correctional and told the warden to search Melton’s

cell. When Melton saw guards approaching, he barricaded the door and smashed his phone to pieces. It would take days to recover data from the

device.

There was nothing the FBI could do to force Melton to cooperate with the

investigators. He was already in prison for life. There was no additional

information to be gleaned from any cellphone records. Agents had looked at

surveillance tapes from the stores where the burner phones had been

purchased and had scrutinized footage from cameras overlooking roads near

Janssen’s house. None of it was helpful. The FBI had hundreds of pieces of

information. There were numerous dots, but nothing to connect them.

Some agents hoped the FBI’s new computer system, a piece of complex

software named Sentinel, might help unearth connections they had

overlooked. Others were more skeptical. More than a decade earlier, the

bureau had started building technologies that officials had promised would

provide powerful new tools for solving crimes. Most of those efforts,

however, were failures. One notable effort was abandoned in 2005 after $170

million was spent creating a search engine that crashed constantly. Another

attempt was suspended in 2010 after auditors concluded it would cost

millions more simply to figure out why the system wasn’t working. A few

years before Janssen was kidnapped, the agency’s databases were still so

outdated that most agents didn’t even bother inputting the bulk of the

information they collected during investigations. Instead, they used paper

files and index cards, like their predecessors decades before.

Then, in 2012, the bureau had rolled out Sentinel. Simply put, it was a

system for sorting and managing evidence, clues, witness testimony, and the

tens of thousands of other little pieces of information agents collected every

day. Sentinel was tied into analytical engines and databases that the bureau

and other law-enforcement agencies had developed to look for patterns. The

software’s development had been overseen by a young man from Wall Street

who had convinced the FBI to hire him by arguing that the bureau needed to

draw on lessons from companies such as Toyota, and methods such as “lean

manufacturing” and “agile programming.” He had promised he could get

Sentinel working in less than two years with a handful of software engineers

—and then he had delivered.

Now Sentinel was functional. No one working on the Janssen case was

certain if it would provide any help, but they were desperate. One of the agents began inputting each piece of information they had collected thus far,

and then sat back to see if Sentinel would spit anything useful out.

II.

When Rick Madrid showed up for his job interview at the old General Motors

plant, he wore mirrored shades, an Iron Maiden T-shirt, and a pair of cutoff

jeans he had once described as “the greatest aphrodisiac in Northern

California.” It was 1984. Out of courtesy toward his interviewers—and

because Madrid wanted this job—he had combed his beard and put on

deodorant. He drew the line, however, at wearing sleeves that covered his

tattoos.

Madrid was familiar with the plant in Fremont, California, because he had

worked there until two years earlier, when GM had shut it down. Fremont

was known, locally and nationally, as the worst auto factory in the world.

Eight hours a day for twenty-seven years, Madrid had pounded rims into

place with a sledgehammer, proselytized about the greatness of the United

Auto Workers, and served rounds of “magic screwdrivers,” a high-octane

mixture of vodka and orange juice that he poured into plastic cups wedged

into car frames so coworkers could partake as the vehicles progressed down

the line. Fremont’s assembly conveyors always moved smoothly, so the

drinks hardly ever spilled. The bags of ice he put in the vehicles’ trunks often

warped the liners, but that was the problem of whoever bought the car.

“Work was an interruption in my leisure time,” Madrid later said. “I was

there to earn money. I really didn’t care about the quality of the job and

neither did GM. They just wanted to get as many cars out as they could.”

When Madrid showed up for this interview, however, he suspected things

might be different this time. GM was partnering with the Japanese automaker

Toyota to reopen the Fremont plant. For Toyota, this was a chance to build

cars inside the United States and expand the company’s sales in America. For

General Motors, it was an opportunity to learn about the famed “Toyota

Production System,” which was producing cars of very high quality at very

low costs in Japan. One hitch in the partnership was that GM’s agreement

with the UAW dictated that the plant had to hire at least 80 percent of its workers from employees who had been laid off two years earlier. So Madrid

and his friends were showing up, one by one, to interview with New United Motor Manufacturing, Inc., or NUMMI.

Madrid figured he was a good candidate because his on-the-job drinking,

truth be told, was tame compared to the antics of his former colleagues. Yes,

he may have gotten drunk and had sex in the warehouse where they stored

Chevy seats, but unlike many of his coworkers, he didn’t snort coke while

attaching brake pads or smoke weed from bongs built from muffler parts. He

hadn’t been a patron of the parking-lot RV where prostitutes offered services

perfectly timed to union-mandated work breaks. Nor had Madrid ever

deliberately sabotaged a vehicle like those who put empty whiskey bottles

and loose screws behind door panels so they would bang around after the cars

were sold.

The saboteurs were an extreme example of a fierce war that had consumed

the Fremont plant in the GM days. Workers weren’t above dirty tactics if

they thought it would strengthen their union’s hand. Employees knew that as

long as they kept the assembly line moving, no one was likely to get punished

for misbehavior, no matter how egregious. At GM, all that really mattered

was keeping production on pace. Employees sometimes discovered mistakes

on cars as they moved along the conveyor belt, but rather than stop and fix the problems, they would mark the vehicle with a wax crayon or a Post-it

note and let it continue on its way. Eventually, those fully assembled autos

would be hauled into the back lot and taken apart to repair the error. Once, a

worker had a heart attack and fell into the pit as a car passed over; everyone

waited for the vehicle to rumble along before they pulled him out. They all

knew the plant’s fundamental law: The line doesn’t stop.

Madrid’s first interview occurred in a small conference room. Across the

table was a representative from the UAW, two Toyota executives from Japan,

and a GM manager. Everyone exchanged pleasantries. They asked Madrid

about his background and gave him some basic math and assembly problems

to test his knowledge of auto manufacturing. They asked if he intended to

drink while working. No, he said, he was done with that. It was a relatively

brief conversation. Then, as he was walking out, one of the men from Japan

asked Madrid what he had disliked about the plant when he worked there last

time.

Rick Madrid had never been shy about speaking his mind. He didn’t like

working on cars he knew had problems, he told them, because whatever he was doing would have to be undone to repair a mistake. He didn’t like that

his suggestions were always ignored by his superiors. Once, he said, when a

new tire mounting machine was being installed, he had come up with an idea

for putting the controls in a different place to speed up work. He even sought

out an engineer to show him a diagram of the concept. But when he came

back from lunch, the new machine was in place with the controls in their

original location. “I operated from the left side of the tire machine, all the controls were on the right side,” he told the interviewers. “Thank goodness

that engineer didn’t build bridges.”

When the plant was run by GM, workers were just cogs in a machine,

Madrid told them. “You were just there to do what they told you to do,” he

said. Nobody ever asked him his opinion or cared what he thought.

He expressed all these frustrations to his interviewers and then kicked

himself on the long drive home. He really needed this job. He should have

kept his mouth shut.

A few days later, Madrid got the call. The Japanese executives had

appreciated his honesty and were offering him a job. First, though, he would

have to go to Japan for two weeks and learn about the Toyota Production

System. Sixteen days later, NUMMI flew Madrid and about two dozen other

workers to the Takaoka auto plant outside Toyota City, Japan, the first in a series of trips nearly every employee at NUMMI would take. When Madrid

walked into the Japanese factory, he saw familiar assembly lines and heard

the recognizable sounds of pneumatic tools hissing and buzzing. Why had

they bothered flying him across the world to train inside a factory just like the

one at home? After a basic tour and an orientation meeting, Madrid walked

onto the factory floor and watched one man put bolts into doorframes, over

and over, with an air-powered gun. By the time each car rolled off the line,

Madrid knew, those bolts would be buried under layers of metal and plastic.

It was just like California, except the signs were in Japanese and the

bathrooms were much cleaner.

Then the worker manning the pneumatic gun pushed a bolt into place,

applied his tool, and an ugly squeal sounded. The bolt had misthread the hole

—a common mistake—and was stuck halfway in the doorframe. Madrid

expected the man to signal the defect by marking the door, like they did at

GM, so the car could be eventually towed to a back lot and repaired. The problem with that system, however, was that replacing the bolt would require

disassembling the door, repairing the mistake, and then rebuilding

everything. The trim would be less snug in the vehicle’s frame afterward.

Whoever bought the car wouldn’t notice at first, but after a few years, the

door would start jiggling. It would be a shoddier vehicle.

When the screw gun squealed inside the Japanese plant, though, something

unexpected happened. The worker who made the mistake reached above his

head and pulled a hanging cable that turned on a spinning yellow light. He

then reversed the direction of his screw gun and pulled the bolt out of the doorframe, grabbed another tool, and used it to smooth the hole’s threads. At

this point, a manager walked over, stood behind the worker, and began asking

questions. The worker ignored his boss except to bark out a few orders, and

then grabbed another tool to rethread the hole. The conveyor belt was still

moving, but the worker hadn’t finished his repair. When the door got to the

end of the worker’s station, the entire assembly line stopped. Madrid had no

idea what was going on.

Another man, clearly a senior manager, came over. Instead of yelling, he

laid out a new bolt and equipment on a tray, like a nurse in an operating

room. The worker kept issuing orders to his superiors. In Fremont, that would

have gotten him slugged. Here, though, there were no angry shouts or

anxious whispers. The other men on the line were calmly standing in place or

double-checking parts they had just installed. No one seemed surprised at

what was happening. Then the worker completed his rethreading, put a new

bolt in the door, and pulled the cord above his head again. The assembly line

started moving at normal speed. Everyone went back to work.

“I just didn’t believe it,” Madrid said. “Back home, I had seen a guy fall in

the pit and they didn’t stop the line. For so many years, I had learned you don’t stop the line, no matter what.” He had been told it cost $15,000 a

minute to pause an assembly line. “But, for Toyota, quality came before

income.

“That’s when it dawned on me, that we can do this, we can compete

against these guys by learning what they do,” Madrid said. “One bolt, one

bolt changed my attitude. I felt that I could finally, finally take pride in what I

do.”

As Madrid continued his training in Japan, there were other surprises as

well. One day he shadowed a worker who, midway through a shift, told a

manager he had an idea for a new tool that would help him install struts. The

manager walked to the machine shop and returned fifteen minutes later with a

prototype. The worker and manager refined the design throughout the day.

The next morning, everyone had their own versions of the tool waiting at

their stations.

Madrid’s trainers explained that the Toyota Production System—which in

the United States would become known as “lean manufacturing”—relied on

pushing decision making to the lowest possible level. Workers on the

assembly line were the ones who saw problems first. They were closest to the

glitches that were inevitable in any manufacturing process. So it only made

sense to give them the greatest authority in finding solutions.

“Every person in an organization has the right to be the company’s top

expert at something,” John Shook, who trained Madrid as one of Toyota’s

first Western employees, told me. “If I’m attaching mufflers or I’m a

receptionist or a janitor, I know more about exhaust systems or receiving

people or cleaning offices than anyone else, and it’s incredibly wasteful if a

company can’t take advantage of that knowledge. Toyota hates waste. The

system was built to exploit everyone’s expertise.”

When Toyota had first proposed this management philosophy to General

Motors, the Americans literally laughed at their naïveté. Maybe that approach

works in Japan, they said, but it would fail in California. Workers at the

Fremont plant didn’t care about contributing expertise. What they cared about

was doing as little work as possible.

“But the only way we would agree to the partnership was if GM promised

to give this a try,” said Shook. “Our basic philosophy was that no one goes to

work wanting to suck. If you put people in a position to succeed, they will.

“What we didn’t say was that if we couldn’t figure out how to export the

Toyota Production System, we were screwed,” Shook said. “It’s the culture

that makes Toyota successful, not hanging cords or prototyping tools. If we

couldn’t export a culture of trust, we had no other ideas. So we sent everyone

to America and prayed it would work.”

In 1994, two business school professors at Stanford began studying how, exactly, one creates an atmosphere of trust within a company. For years, the

professors—James Baron and Michael Hannan—had been teaching students

that a firm’s culture mattered as much as its strategy. The way a business

treats workers, they said, was critical to its success. In particular, they argued

that within most companies—no matter how great the product or loyal the

customers—things would eventually fall apart unless employees trusted one

another.

Then, each year, a few students would ask for evidence that supported

those claims.

The truth was, Baron and Hannan believed their assertions were true, but they didn’t have much data to back them up. Both men were trained as

sociologists and could point to studies showing the importance of culture in

making employees happy or recruiting new workers or encouraging a healthy

work-life balance. But there were few papers showing how a company’s

culture impacted profitability. So in 1994, they embarked on a multiyear

project to see if they could prove their assertion right.

First, though, they needed to find an industry that had lots of new

companies they could track over time. It occurred to them that the flurry of technology start-ups appearing in Silicon Valley might provide the perfect

sample. At the time, the Internet was in its infancy. Most Americans thought

@ was something to ignore on a keyboard. Google was still just a number

spelled “googol.”

“We weren’t inherently interested in tech and we had no idea the

companies we were studying would become big deals,” said Baron, who now

teaches at Yale. “We just needed start-ups to study, and there were tech

companies getting founded nearby and so every morning, we would buy the

San Jose Mercury News and go through each page, and whenever a young

company was mentioned, we would put our team into pursuit to find a phone

number or mailing address, and then send someone to see if the CEO would

answer a questionnaire.” Over time, as they put it in a study they later wrote,

“without realizing it when we started our study in 1994–1995, we assembled

the most comprehensive database to date on the histories, structures, and HR

practices of high-tech companies in Silicon Valley, just as the region was

about to witness an economic and technological boom of historic

proportions.” The project ended up taking fifteen years and examining close to two hundred firms.

Their surveys looked at almost every variable that might influence a start-

up’s culture, including how employees were recruited, how applicants were

interviewed, how much people were paid, and which workers executives

decided to promote or fire. They watched college dropouts become

billionaires and, in other cases, high-flying executives crash and burn.

Eventually, they collected enough data to conclude that most companies

had cultures that fell into one of five categories. One was a culture they

referred to as the “star” model. At these firms, executives hired from elite

universities or other successful companies and gave employees huge amounts

of autonomy. Offices had fancy cafeterias and lavish perks. Venture

capitalists loved star model companies because giving money to the A-Team,

conventional wisdom held, was always the safest bet.

The second category was the “engineering” model. Inside firms with

engineering cultures, there weren’t many individual stars, but engineers, as a

group, held the most sway. An engineering mindset prevailed in solving

problems or approaching hiring decisions. “This is your stereotypical Silicon

Valley start-up, with a bunch of anonymous programmers drinking Mountain

Dew at their computers,” said Baron. “They’re young and hungry and might

be the next generation of stars once they prove themselves, but right now,

they’re focused on solving technical problems.” Engineering-focused cultures

are powerful because they allow firms to grow quickly. “Think of how fast

Facebook expanded,” said Baron. “When everyone comes from a similar

background and mindset, you can rely on common social norms to keep

everyone on the same path.”

The third and fourth categories of companies included those firms built

around “bureaucracies” and those constructed as “autocracies.” In the

bureaucratic model, cultures emerged through thick ranks of middle

managers. Executives wrote extensive job descriptions, organizational charts,

and employee handbooks. Everything was spelled out, and there were rituals,

such as weekly all-hands meetings, that regularly communicated the firm’s

values to its workers. An autocratic culture is similar, except that all the rules,

job descriptions, and organizational charts ultimately point to the desires and

goals of one person, usually the founder or CEO. “One autocratic chief

executive told us that his cultural model was, ‘You work. You do what I say.

You get paid,’ ” Baron said.

The final category was known as the “commitment” model, and it was a

throwback to an age when people happily worked for one company their

entire life. “Commitment CEOs say things like, ‘I want to build the kind of

company where people only leave when they retire or die,’ ” said Baron.

“That doesn’t necessarily mean the company is stodgy, but it does imply a set

of values that might prioritize slow and steady growth.” Some Silicon Valley

executives told Baron they saw commitment firms as outdated, remnants of a

corporate paternalism that had undermined industries such as American

manufacturing. Commitment companies were more hesitant to lay people off.

They often hired HR professionals when other start-ups were using precious

dollars to recruit engineers or salespeople. “Commitment CEOs believe that

getting the culture right is more important at first than designing the best

product,” Baron said.

Over the next decade, Baron and Hannan kept close tabs on which start-

ups thrived and which ones stumbled. About half the firms they studied

remained in business for at least a decade; some became the most successful

companies in the world. Baron and Hannan’s goal was to see if particular corporate cultures were more likely to correlate with success. They were

unprepared, however, for how dramatically the impact of culture came

through. “Even in the fast-paced world of high-tech entrepreneurship in

Silicon Valley, founders’ employment models exert powerful and enduring

effects on how their companies evolve and perform,” the researchers wrote in

2002 in the journal California Management Review. The enormous impact of

cultural decisions “is evident even after taking account of numerous other

factors that might be expected to affect the success or failure of young

technology ventures, such as company age, size, access to venture capital,

changes in senior leadership, and the economic environment.”

Just as Baron and Hannan had suspected, the star model produced some of

the study’s biggest winners. As it turned out, putting all the smartest people

in the same room could yield vast influence and wealth. But, unexpectedly,

star firms also failed in record numbers. As a group, they were less likely to

make it to an IPO than any other category, and they were often beset by

internal rivalries. As anyone who has ever worked in such a company knows,

infighting is often more vicious inside a star-focused firm, because everyone

wants to be the star.

In fact, when Baron and Hannan looked at their data, they found the only

culture that was a consistent winner were the commitment firms. Hands

down, a commitment culture outperformed every other type of management

style in almost every meaningful way. “Not one of the commitment firms we

studied failed,” said Baron. “None of them, which is amazing in its own right.

But they were also the fastest companies to go public, had the highest

profitability ratios, and tended to be leaner, with fewer middle managers,

because when you choose employees slowly, you have time to find people

who excel at self-direction.” Employees in commitment firms wasted less

time on internal rivalries because everyone was committed to the company,

rather than to personal agendas. Commitment companies tended to know

their customers better than other kinds of firms, and as a result could detect

shifts in the market faster. “Despite its being widely pronounced dead in

Silicon Valley in the mid-1990s, the Commitment model fares very well in

our sample,” the researchers wrote.

“Venture capitalists love star firms because when you’re investing in a

portfolio of companies, all you need are a few huge successes,” Baron told

me. “But if you’re an entrepreneur and you’re betting on just one company,

then the data says you’re much better off with a commitment-focused

culture.”

One of the reasons commitment cultures were successful, it seemed, was

because a sense of trust emerged among workers, managers, and customers

that enticed everyone to work harder and stick together through the setbacks

that are inevitable in any industry. Most commitment companies avoided

layoffs unless there was no other alternative. They invested heavily in

training. There were higher levels of teamwork and psychological safety.

Commitment companies might not have had lavish cafeterias, but they

offered generous maternity leaves, daycare programs, and work-from-home

options. These initiatives were not immediately cost-effective, but

commitment firms valued making employees happy over quick profits—and

as a result, workers tended to turn down higher-paying jobs at rival firms.

And customers stayed loyal because they had relationships that stretched over

years. Commitment firms dodged one of the business world’s biggest hidden

costs: the profits that are lost when an employee takes clients or insights to a

competitor.

“Good employees are always the hardest asset to find,” said Baron. “When

everyone wants to stick around, you’ve got a pretty strong advantage.”

The first thing Rick Madrid did upon returning to California was tell

everyone what he had seen in Japan. He talked about the hanging cables,

called “andon cords,” and about how managers took commands from workers

instead of the other way around. He described watching assembly lines stop

because some mechanic decided he needed more time to rebolt a door. He

declared that everything at the Fremont plant was about to change now that

NUMMI was in charge.

His friends were skeptical. They had heard this story before. GM had often

said the company valued employee input—until employees began

recommending changes that management didn’t want to hear. In the weeks

before the NUMMI plant opened, the factory’s workers made sure their union

memberships were up-to-date and held meetings to discuss tactics for

fighting management, if it came to that. They voted to create a “NUMMI work-stoppage fund” to pay for workers’ expenses if they went on strike.

They demanded—and NUMMI immediately agreed to provide—a formal

system for filing grievances.

Then, NUMMI’s management announced the company’s layoff policy.

“New United Motor Manufacturing, Inc., recognizes that job security is

essential to an employee’s well-being,” read the company’s agreement with

the United Auto Workers. “The Company agrees that it will not lay off

employees unless compelled to do so by severe economic conditions that

threaten the long-term viability of the Company.” NUMMI promised it would

cut executive pay rather than fire workers and train people to sweep floors,

repair machines, or serve meals in the cafeteria to preserve their jobs. Every

employee complaint and suggestion, no matter how far-fetched or expensive,

would be implemented, or a response would be publicly posted explaining

why not. Every team was given authority to change their stations’ layouts and

work flow. Anyone, at any time, could stop the assembly line if they saw a

problem. No American car company had ever made so public a promise to

avoid layoffs and respond to worker complaints.

Skeptical workers said that such pledges were easy to make when the plant

wasn’t even operating yet, but they grudgingly agreed to play along. The

factory began producing Chevy Novas on December 10, 1984.

Rick Madrid was assigned to a team that stamped out hoods and doors

from giant sheets of steel. It was immediately clear to him that things were different. People who once sought trysts in the storage room kept their hands

to themselves. There was no obvious drinking on the job. The RV hadn’t

returned to the parking lot. People were scared to try anything. They didn’t

want to push their luck. This hesitancy, however, also had less useful

consequences. No one was pulling andon cords or making suggestions

because no one was eager to cost the factory $15,000 a minute. No one was

sure it wouldn’t cost them their job.

A month after the plant reopened, Tetsuro Toyoda—NUMMI’s president,

whose grandfather had founded Toyota in 1933—walked the Fremont floor.

He saw an employee struggling to install a taillight that was wedged into a

car frame at an odd angle. Toyoda approached the worker and, reading the

name stitched on his uniform, said, “Joe, please pull the cord.”

“I can fix this, sir,” Joe said.

“Joe, please pull the cord.”

Joe had never pulled an andon cord. No one in his area had. Since the plant

had opened, there had been only a few cord pulls, and one of those had been

an accident.

“Sir, I can fix this,” Joe said, working furiously to pop the taillight into place.

Joe’s team leader was standing nearby. That man’s manager had been

shadowing Toyoda as he walked through the factory, so he was orbiting

nearby as well. When Joe glanced up, he saw a half dozen of the plant’s most

senior executives staring back.

“Joe, please,” Toyoda said. Then he stepped over, took Joe’s hand in his

own and guided it to the andon cord, and together they pulled. A flashing

light began spinning. When the chassis reached the end of Joe’s station

without the taillight correctly in place, the line stopped moving. Joe was

shaking so much, he had to hold his crowbar with both hands. He finally got

the taillight positioned and, with a terrified glance at his bosses, reached up and pulled the andon cord, restarting the line.

Toyoda faced Joe and bowed. He began speaking in Japanese.

“Joe, please forgive me,” a lieutenant translated. “I have done a poor job of

instructing your managers of the importance of helping you pull the cord

when there is a problem. You are the most important part of this plant. Only

you can make every car great. I promise I will do everything in my power to

never fail you again.”

By lunchtime, everyone inside the factory had heard the story. The next

day, andon cords were pulled more than a dozen times. The next week, more

than two dozen times. A month later, the plant was averaging nearly a

hundred pulls a day.

The importance of the andon cords and the employee suggestions and

Toyoda’s apology was that they demonstrated that the fate of the company

was in the employees’ hands. “There was a genuine devotion to convincing

employees they were part of a family,” said Joel Smith, the UAW

representative at NUMMI. “It had to be reinforced constantly, but it was real.

We might have disagreements or see things differently, but at the end of the

day, we were committed to each other’s success.”

“If people started pulling andons for no good reason, the plant would have

fallen apart,” said Smith. Everyone knew it still cost thousands of dollars

each minute a line was stopped, “and that anyone could stop the line, at any

time, without penalty. So employees could bankrupt the place if they wanted

to.

“Once you’re entrusted with that kind of authority, you can’t help feel a

sense of responsibility,” said Smith. “The most junior workers didn’t want

NUMMI to go bankrupt, and the management didn’t want that, and so,

suddenly, everyone was on the same side of the table.” And as workers were

empowered to make more choices, their motivation skyrocketed. Just as

Mauricio Delgado and the U.S. Marine Corps had found in other settings,

when workers felt a greater sense of control, their drive expanded.

Word of the NUMMI experiment spread quickly. When professors from

Harvard Business School visited a few years after the plant reopened, they

found that former GM employees who once spent only forty-five seconds of

every minute working now averaged fifty-seven seconds of labor per minute.

By 1986, “NUMMI’s productivity was higher than that of any other GM

facility and more than twice that of its predecessor, GM-Fremont,” they

wrote. Absenteeism had dropped from 25 percent during the GM days to 3

percent under NUMMI. There were no observable levels of substance abuse,

prostitution, or sabotage. The formal grievance system was hardly ever used.

NUMMI’s productivity was as high as that of plants in Japan, “even though

its workers were, on average, ten years older and much less experienced with

the Toyota production system,” the Harvard researchers wrote. In 1985, Car

and Driver magazine printed an issue with the cover line “Hell Freezes Over,” announcing NUMMI’s accomplishments. The worst auto factory on

earth had become one of the most productive plants in existence, using the

same workers as before.

Then, four years after NUMMI opened, the recession hit the auto industry.

The stock market crashed. Unemployment was rising. Car sales plummeted.

NUMMI’s managers estimated they needed to reduce production by 40

percent. “Everyone was saying there were going to be layoffs,” said Smith,

the UAW rep. Instead, the plant’s top sixty-five executives all took pay cuts.

Assembly line workers were reassigned to janitorial duties or landscaping, or

sent into the paint room to scrape air vents instead of let go. The company proved it was committed.

“After that, workers were willing to do anything for the company,” Smith

said. “Four separate sales slumps over thirty years, and NUMMI never did

layoffs once. And each time, when the business finally came back, everyone

worked harder than before.”

Rick Madrid retired from NUMMI in 1992, after almost four decades of

building cars. Three years later, the Smithsonian mounted an exhibit at the

National Museum of American History that included Madrid’s ID badge and

his hat in a show named A Palace of Progress. NUMMI, the curators wrote,

was iconic, a factory that had demonstrated it was possible to unite workers

and managers around a common cause through mutual commitment and

shared power.

Even now NUMMI is cited inside business schools and by corporate

chieftains as an example of what organizations can achieve when a

commitment culture takes hold. Since NUMMI was founded, the “lean

manufacturing” principles have infiltrated nearly every corner of American

commerce, from Silicon Valley to Hollywood to healthcare. “I’m really glad

I ended up my years as an autoworker at NUMMI,” Madrid said. “I went

from being depressed, bored, people didn’t even know I existed, to seeing J.

D. Power name NUMMI as a top quality plant.”

NUMMI’s workers got together for a party after that J. D. Power

announcement. “And when I spoke, I said, we’re the best damn autoworkers

in the world,” said Madrid. “Not just the workers. Not just the managers. All

of us, together, are the best, because we’re devoted to each other.”

III.

Six years before Frank Janssen was kidnapped, the Federal Bureau of

Investigation had reached out to a thirty-four-year-old Wall Street executive

to see if he would be interested in overseeing development of the bureau’s

technology systems. Chad Fulgham had never worked in law enforcement

before. His specialty was developing large computer networks for investment

banks such as Lehman Brothers and JPMorgan Chase. So he was surprised

when he received a call in 2008 explaining that the FBI wanted an interview.

Improving the bureau’s technology had long been a priority for federal

officials. As early as 1997, the FBI’s top leaders had promised Congress they

would deliver an overhauled system that tied together the dozens of internal

databases and analytical engines the bureau maintained. This network,

officials said, would give agents powerful new tools for connecting the dots

among disparate cases. But by the time the bureau contacted Fulgham eleven

years later, work on that system, Sentinel, had already consumed $305

million with no end in sight. The agency had brought in an outside group to

figure out why Sentinel was taking so long. The experts said the bureau was

so bogged down by bureaucracy and conflicting agendas that it would take

tens of millions of dollars simply to get the program back on track.

The bureau called Fulgham to see if he could find a cheaper way to get

things moving again. “I secretly always wanted to work for the FBI or the

CIA,” he told me. “So when they called me, particularly with this huge, hairy

problem, it was like getting offered my dream job.”

First, though, Fulgham needed to convince the bureau that his approach

was the right one. Fulgham’s management style, he explained, drew its

inspiration from examples like NUMMI. In the previous two decades, as

NUMMI’s success had become better known, executives in other industries

had started adapting the Toyota Production System philosophy to other

industries. In 2001, a group of computer programmers had gathered at a ski

lodge in Utah to write a set of principles, called the “Manifesto for Agile

Software Development,” that adapted Toyota’s methods and lean

manufacturing to how software was created. The Agile methodology, as it came to be known, emphasized collaboration, frequent testing, rapid iteration,

and pushing decision making to whoever was closest to a problem. It quickly

revolutionized software development and now is the standard methodology

among many tech firms.

Among filmmakers, the “Pixar method” was modeled specifically on

Toyota’s management techniques and became famous for empowering low-

level animators to make critical choices. When Pixar’s leadership was asked

to take over Disney Animation in 2008, executives introduced themselves

with what became known as “the Toyota Speech,” “in which I described the

car company’s commitment to empowering its employees and letting people

on the assembly line make decisions when they encountered problems,” Pixar

cofounder Ed Catmull later wrote. “I stressed that no one at Disney needed to

wait for permission to come up with solutions. What is the point of hiring

smart people, we asked, if you don’t empower them to fix what’s broken?”

In hospitals, the distribution of authority to nurses and others who are not

physicians is referred to as “lean healthcare.” It is a management philosophy

and a “culture in which anyone can, and indeed must, ‘stop the line,’ or stop

the care process if they feel something is not right,” the chairman of one lean

hospital, the Virginia Mason Medical Center, wrote in 2005.

These approaches emerged in different industries, but they and other

adaptations of lean manufacturing shared key attributes. Each was dedicated

to devolving decision making to the person closest to a problem. They all

encouraged collaboration by allowing teams to self-manage and self-

organize. They emphatically insisted on a culture of commitment and trust.

Fulgham argued that the FBI’s technology efforts could succeed only if the

bureau embraced a similar approach. FBI officials had to commit to

distributing critical decision-making power to people on the ground, he said,

such as lowly software engineers or junior field agents. This approach was a

significant shift because previously, bureau executives—distrustful of one

another and consumed by internal power struggles—had designed new

technology systems by first outlining thousands of specifications each piece

of software needed to satisfy. Committees filled hundreds of pages with rules

for how databases ought to function. Any major change required approvals

from numerous officials. The system was so dysfunctional that software

development teams would sometimes spend months building a program, only

to be told it was canceled when they were done. And the results were often

dysfunctional as well. When Fulgham asked for a demonstration of the

Sentinel work done thus far, for instance, an engineer led him to a computer

monitor and invited him to input some keywords, such as a criminal’s alias

and an address associated with a crime.

“In fifteen minutes, we’ll have a report of previous cases linked to that

address and name,” the engineer said.

“The people I’m going to report to carry guns, and you want me to tell

them it will take the computer fifteen minutes to provide help?” said

Fulgham.

A 2010 inspector general’s report had said it would take another six years

and $396 million to get Sentinel working. Fulgham told the bureau’s director

that if they gave him the authority to distribute control, he would cut the

number of people needed from more than four hundred to just thirty

employees and deliver Sentinel for $20 million in a bit over a year. Soon

Fulgham and a team of software engineers and FBI agents were holed up in

the basement of the bureau’s Washington, D.C., headquarters. The only rules,

Fulgham told them, were that everyone had to make suggestions, anyone

could declare a time-out if they thought a project was moving in the wrong

direction, and the person closest to a problem had primary responsibility for

figuring out how to solve it.

The main problem with Sentinel, Fulgham believed, was that the bureau—

like many big organizations—had tried to plan everything in advance. But

creating great software requires flexibility. Problems pop up unexpectedly

and breakthroughs are unpredictable. The truth was, no one knew exactly

how FBI agents would use Sentinel once it was functional, or how it would

need to change as crime-fighting techniques evolved. So instead of

meticulously predesigning each interface and system—instead of trying to

control from above—they needed to make Sentinel into a tool that could

adapt to agents’ needs. And the only way to do that, Fulgham was convinced,

was if developers were unfettered themselves.

Fulgham’s team started by coming up with more than one thousand

scenarios in which Sentinel could be useful, everything from inputting

victims’ statements to tracking evidence to interfacing with FBI databases

that looked for patterns among clues. Then they started working backward to figure out what kind of software should accommodate each need. Every

morning, the team conducted a “stand-up”—meetings where everyone stood

to encourage brevity—and recounted the previous day’s work and what they

hoped to accomplish over the next twenty-four hours. Whoever was closest to

a particular problem or a piece of code was considered the expert on that

topic, but any programmer or agent, no matter their rank, was free to make

suggestions. In one case, a programmer and a field agent, after brainstorming,

suggested that they model part of Sentinel on TurboTax, the popular financial

software that reduced thousands of pages of complicated tax laws into a

series of basic questions. “The idea was basically ‘Investigations and Justice

for Dummies,’ ” said Fulgham. “It was absolutely brilliant.”

Under the old system, getting approval for that proposal would have taken

upward of six months and required dozens of memos, each carefully

scrubbed of any mention of TurboTax or any indications that programmers

intended to simplify federal procedures. No one would have wanted an

enterprising lawyer or journalist getting their hands on something that used

plain English to explain how the system worked. Under Fulgham, though,

none of that bureaucracy existed. The programmer and agent mentioned the

idea on a Monday, had a prototype ready by Wednesday, and everyone

agreed to use the approach going forward on Friday. “It was like government

on steroids,” Fulgham said.

Every two weeks, the team demonstrated their work for a broad audience

of high-ranking officials who provided feedback. The bureau’s director had

forbidden anyone from micromanaging or making demands. At most,

division heads could offer suggestions, each of which was cataloged and

evaluated by whoever was closest to that piece of code. Gradually, the

Sentinel team became bolder and more ambitious, not just building systems

for record keeping, but linking Sentinel to tools that identified trends and

threats and made comparisons among cases. By the time they were done,

Sentinel was at the core of a system so powerful it could look across millions

of investigations simultaneously and pick out patterns agents had missed. The

software went live sixteen months after Fulgham took over. “Deployment of

the Sentinel application in July 2012 represented a pivotal moment for the

FBI,” the agency later wrote. Sentinel, in its first month alone, was used by

more than thirty thousand agents. Since then, it has been credited with helping solve thousands of crimes.

At NUMMI, decentralizing decision making helped inspire a workforce.

At the FBI, it played a different role. Lean management and agile methods

helped fuel the ambitions and innovation of junior programmers who had

been beaten down by bureaucracy. It emboldened them to come up with

solutions no one had considered before. It convinced people to swing for the

fences because they knew they wouldn’t be punished if they missed the ball

now and then.

“The effect of Sentinel on the FBI has been dramatic,” wrote Jeff

Sutherland, one of the authors of the Agile Manifesto, in a study of Sentinel’s

development published in 2014. “The ability to communicate and share

information has fundamentally changed what the bureau is capable of.”

More important, the way that Sentinel succeeded served as a source of

inspiration to the agency and its leadership. “The Sentinel experience taught

us a lot about how much potential can be unlocked when you give people

more authority,” Jeff Johnson, the bureau’s current chief technology officer,

told me. “We saw how much more passionate people can become. You look

at some of our recent cases—the kidnapping in North Carolina, hostage

rescue situations, terrorism investigations—and in situations like that, we’ve

learned it’s critical that agents feel like they can make independent decisions.

“But empowering people in an agency this size is really hard,” Johnson

said. “That was one of the problems before 9/11—people didn’t feel

rewarded for independent thinking. Then you look at things like the

development of Sentinel, and you see how much is possible.”

IV.

After the agents working on the Frank Janssen kidnapping case inputted the

data they had collected into Sentinel, the software and the databases

connected to it began looking for patterns and leads. The agents had entered

the cellphone numbers the bureau had collected, the addresses investigators

had visited, and the aliases the kidnappers had used in intercepted calls.

Others inputted the names of people who had visited Kelvin Melton in prison,

license plates caught on cameras around Janssen’s home, and credit card

transactions from inside the stores where the burner phones were bought.

Every detail was fed into Sentinel in the hopes a connection would emerge.

Eventually, the agency’s databases discovered a coincidence: The phone

that had sent photos of Frank Janssen to his wife had also made a call to

Austell, Georgia, a small city outside Atlanta. The FBI’s computers had

looked through millions of records from other cases, and had found a link to

Austell from another case.

In March 2013, a year earlier, a confidential informant had given the

bureau the address of an apartment in Austell that he said criminals used as a

safe house. That same informant, in a different conversation, had also

mentioned an imprisoned gang leader who had “put a hit on the female

District Attorney who prosecuted him,” a reference the FBI believed was to

Kelvin Melton, the man who allegedly planned the Janssen kidnapping.

At the time of those conversations, no one inside the FBI knew what the

informant was talking about. Janssen wouldn’t be kidnapped until a year

later. And since then, no one had given the conversation a second thought.

The agents who had interviewed the informant weren’t even part of the team

looking for Janssen.

But now, the computers connected to Sentinel found a link: A confidential

informant had mentioned someone who fit the description of Kelvin Melton,

who had allegedly planned the kidnapping. That informant had also

mentioned an apartment in Austell—an apartment that, Sentinel had just

revealed, might have received a phone call from one of the kidnappers’

phones.

Someone needed to visit that apartment.

The problem was, this was just one of dozens of leads investigators were

chasing. There were former associates of Melton’s to track down, prison

visitors to scrutinize, former girlfriends who might be involved. There were

too many potential leads, in fact, for agents to pursue them all. The FBI

needed to prioritize, and it wasn’t clear that chasing a clue from a year-old conversation was the best use of time.

In recent years, however, as the success of Sentinel had attracted more

notice within the bureau, officials had become increasingly committed to

using lean and agile techniques throughout the agency. Commanders and

field agents had embraced the philosophy that the person closest to a question

should be empowered to answer it. FBI director Robert Mueller had launched

a series of initiatives—the Strategy Management System, the Leadership

Development Program, Strategic Execution Teams—that were designed to

spark, as he told Congress in 2013, “a paradigm shift in the FBI’s cultural

mindset.” One particular focus was encouraging junior agents to make

independent decisions about which leads they should pursue, rather than

waiting for orders from superiors. Any agent could chase a clue if they

thought something was being overlooked. It was a law enforcement version

of pulling the andon cord. “It’s a critical shift,” said Johnson, the FBI chief technology officer. “The people closest to the investigation have to be empowered to make choices about how they spend their time.” Sentinel

wasn’t the only influence behind this change, but it accelerated the adoption

of an agile philosophy inside the bureau. “The FBI’s basic mindset is agile

now,” Fulgham told me. “Sentinel’s success solidified that.”

The investigators on the Janssen case had dozens of leads to choose from.

But junior agents were encouraged to make decisions for themselves. So two

young investigators decided to visit the apartment the confidential informant

had mentioned over a year ago.

When they arrived at the apartment, they learned it was occupied by a

woman named Tianna Brooks. She wasn’t home, but her two young children

were there, unsupervised. The agents called child protective services, and

once the kids had been collected by social workers, the agents began

canvassing neighbors, asking where Brooks had gone. No one knew, but one

person said Brooks had been visited by two men staying nearby. The agents found those men and questioned them. They said they didn’t know anything

about Brooks or any kidnappings.

At 11:33 P.M., a call came in to one of the many phones the FBI had linked

to the kidnappers and, as a result, were under surveillance.

“They got my kids!” a woman’s voice said.

The agents in Austell were told about the call, and began questioning their

suspects more forcefully. The agents pointed out that the two suspects had

recently visited Tianna Brooks. Now, the FBI had intercepted a telephone call

of a panicked woman—possibly Brooks herself—saying the FBI had her

children.

In other words, the two suspects had recently visited someone who may be

linked to a kidnapping.

Was there anything else they wanted to say?

One of them mentioned an apartment in Atlanta.

The agents radioed their colleagues at the kidnapping command center and

a few minutes before midnight, SWAT trucks arrived at the Atlanta

apartment complex the suspects had mentioned. Officers jumped out of the

vehicles and ran past dilapidated buildings. They paused in front of one home

and rammed their way through a wrought-iron door. Inside were two men

sitting in chairs with guns next to them, caught completely off guard. The

room also contained ropes, a shovel, and bottles of bleach. The men had

recently used their phones to send texts about how to dispose of a body. “Get

bleach and throw it on the walls,” someone had ordered them. “Maybe do it

in the closet.”

An officer in riot gear ran into a bedroom and tore open all the doors.

Inside the closet, he found Frank Janssen tied to a chair, unconscious, with

blood still on his face from where the assailants had pistol-whipped him. He

had been missing for six days by then, and was severely dehydrated. The

police cut him free and carried him out of the apartment, past where Janssen’s

attackers lay on the floor, hands cuffed behind their backs. Janssen was put

into an ambulance and rushed to the hospital. When his wife saw him, she

began sobbing. For almost a week, no one had known if he was alive or dead.

And now here he was, with no serious injuries beyond bruises and cuts. He

was released two days later with a clean bill of health.

The breakthrough in the Janssen case didn’t occur simply because the

bureau’s computer systems connected the dots between his kidnapping and

an old, seemingly unrelated interview with a confidential informant. Rather,

Janssen was rescued because hundreds of dedicated people worked nonstop

to chase dozens of leads, and because an agile culture empowered junior

agents to make independent decisions and follow the clues they thought made

sense.

“Agents learn to investigate by listening to their guts and learning they can

change direction when new evidence appears,” Fulgham told me. “But for

those instincts to be unlocked, management has to empower them. There has

to be a system in place that makes you trust that you can choose the solution

you think is best and that your bosses are committed to supporting you if you

take a chance that might not pay off. That’s why agile has been embraced at

the bureau. It talks to who they are.”

This, ultimately, is one of the most important lessons of places such as

NUMMI and the lean and agile philosophies: Employees work smarter and

better when they believe they have more decisionmaking authority and when

they believe their colleagues are committed to their success. A sense of

control can fuel motivation, but for that drive to produce insights and

innovations, people need to know their suggestions won’t be ignored, that

their mistakes won’t be held against them. And they need to know that

everyone else has their back.

The decentralization of decision making can make anyone into an expert—

but if trust doesn’t exist, if employees at NUMMI don’t believe management

is committed to them, if programmers at the FBI aren’t trusted to solve

problems, if agents aren’t encouraged to follow a hunch without fear of

admonishment, organizations lose access to the vast expertise we all carry

within our heads. When people are allowed to stop the assembly line, redirect

a huge software project, or follow an instinct, they take responsibility for

making sure an enterprise will succeed.

A culture of commitment and trust isn’t a magic bullet. It doesn’t

guarantee that a product will sell or an idea will bear fruit. But it’s the best

bet for making sure the right conditions are in place when a great idea comes

along.

That said, there are good reasons companies don’t decentralize authority.

There is a powerful logic behind investing power in only a few hands. At

NUMMI, a small group of disgruntled workers could have bankrupted the

firm by pulling andon cords needlessly. Inside the FBI, a misguided

programmer could have built the wrong computer system. An agent might

have followed the wrong hunch. But, in the end, the rewards of autonomy

and commitment cultures outweigh the costs. The bigger misstep is when

there is never an opportunity for an employee to make a mistake.

A few weeks after his rescue, Frank Janssen sent a thank-you letter to the

agents who rescued him. “I have never felt a greater feeling of joy, relief, and

freedom than that miraculous moment when I heard a firm American

soldier’s voice say, ‘Mr. Janssen, we are here to take you home,’ ” he wrote.

“Despite the nightmare that I experienced, the fact that I am writing this letter

from the comfort of my home is a testament to the many wonderful things

that were done by many wonderful people.” It was a calamity that he was

kidnapped, Janssen wrote, and a testament to the commitment of the FBI that

he was saved.

* The Federal Bureau of Investigation was provided with summaries of this chapter. Please see the chapter’s endnotes for the bureau’s responses. The Janssen family did not reply to repeated attempts to seek their comments by telephone and certified mail. Details regarding this case come from court documents, interviews, and other materials specified in the endnotes. At the time of writing, the allegations of criminal activity contained in this chapter had not been adjudicated by a court of law, and thus remain allegations rather than proven facts. Please see the chapter’s endnotes for further details and the responses provided by the attorneys of those implicated in this alleged crime.

DECISION MAKING

Forecasting the Future (and Winning at Poker) with Bayesian Psychology

The dealer looks at Annie Duke and waits for her to say something. There is

a pile of chips worth $450,000 in the middle of the table and nine of the

world’s best poker players—all men, except for Annie—impatiently waiting

for her to bet. It’s the 2004 Tournament of Champions, a televised

competition with $2 million to the winner. There is no prize for second place.

The dealer hasn’t put down any communal cards yet, and Annie is holding

a pair of tens. Her hand is strong—strong enough that she has already shoved

most of her chips into the pot. Now she has to decide if she wants to bet everything. All the other players have folded except for one—Greg Raymer,

aka “the FossilMan,” a rotund gentleman from Connecticut who carries

pieces of petrified bark in his pockets and wears sunglasses with holographic

lizard eyes.

Annie doesn’t know what cards the FossilMan is holding. Until a few

seconds ago, based on how things were proceeding, she figured she was

going to win this hand. But then the FossilMan pushed everything onto the

pot and threw a wrench in Annie’s plans. Has the FossilMan been playing her

this whole time? Luring her into bigger and bigger bets while waiting to

pounce? Or is he trying to scare her off with a wager so large he thinks she’ll

get spooked and walk away?

Everyone is staring at Annie. She has no idea what to do.

She could fold. But that would mean forfeiting the tens of thousands of

dollars she’s spent to get to this table, all the progress she’s made over the past nine hours, everything she’s worked so hard to earn.

Or she could match his wager and bet everything. If she loses, she’ll be

knocked out of the tournament. If it pays off, though, and she wins this hand,

she’ll instantly become the tournament’s frontrunner, a step closer to paying

for her kids’ school bills and her mortgage, not to mention her messy divorce

and all the uncertainties that give her stomachaches at night.

She looks again at the mountain of chips on the table and feels a pressure

rising in her throat. She’s had panic attacks all her life, breakdowns so severe

that she used to lock herself inside her apartment and refuse to leave. Twenty

years ago, during her sophomore year at Columbia University, she became so

anxious she walked into a hospital, begged them to admit her, and didn’t

come out for two weeks.

Forty-five seconds pass while Annie tries to figure out what to do. “I’m so

sorry,” she says. “I know I’m taking too long. This is just a really hard

decision.”

Annie focuses on her pair of tens. She thinks about what she knows and

doesn’t know. What Annie likes about poker are the certainties. The trick to

this game is making predictions, imagining alternative futures and then

calculating which ones are most likely to come true. Statistics make Annie

feel in control. She might not know exactly what’s coming, but she knows the

precise likelihoods of being right or wrong. The poker table feels serene.

And now the FossilMan has blown that peacefulness to hell by making a

bet that doesn’t match any of the scenarios inside Annie’s head. She has no

idea how to gauge what is most likely to occur. She’s frozen.

“I’m really sorry,” she says. “I just need a second more.”

Many afternoons during Annie’s childhood, her mother would sit at the

kitchen table with a pack of cigarettes, a glass of scotch, and a deck of cards,

and play hand after hand of solitaire until the alcohol was gone and the

ashtray was full. Then she would stagger to the couch and fall asleep.

Annie’s father was an English teacher at St. Paul’s School in New

Hampshire, a boarding school for the scions of senators and CEOs. Her

family lived in a house attached to one of the dorms and so whenever her

parents fought about her mom’s drinking or her father’s lack of money—

which they did frequently—Annie was sure her classmates could overhear.

She often felt like an outcast at the school, too poor to vacation with the rich

kids, too smart to bond with the popular girls, too anxious to be comfortable among the hippies, too interested in math and science for student

government. For Annie, the key to surviving amid the shifting tectonics of

teenage popularity was learning to forecast. If she could predict which

students’ social capital was rising or falling, it was easier to avoid the

infighting. If she could predict when her parents were arguing or her mom

was drinking, she would know if it was safe to bring classmates home.

“When you have an alcoholic parent, you spend a lot of time thinking

about what’s coming,” Annie told me. “You never take for granted that you’ll

get dinner or that someone will tell you when to go to bed. You’re always

waiting for everything to fall apart.”

After graduation, Annie left for college at Columbia and soon discovered

the psychology department. Here, at last, was what she had been looking for.

There were classes that reduced human behavior to understandable rules and

social formulas; teachers who gave lectures on the different categories of

personality and why anxieties emerge; studies about the impact of having an

alcoholic parent. She felt like she was starting to understand why she

sometimes had panic attacks, why it occasionally felt impossible for her to

leave her bed, why she carried this dread that something bad might happen at

any time.

Psychology, at that moment, was undergoing a transformation brought on

by discoveries in cognitive sciences that were bringing a scientific rigor to

understanding behaviors that had long seemed immune to methodical

analysis. Psychologists and economists were working together to understand

the codes that explain why people do what they do. Some of the most

exciting research—work that would eventually win a Nobel Prize—was

focused on studying how people make decisions. Why, researchers

wondered, do some people decide to have children when the costs, in terms

of money and hard work, are so obvious, and the payoffs, such as love and

contentment, are so hard to calculate? How do people decide to send their

kids to expensive private schools instead of free public ones? Why does

someone decide to get married after playing the field for years?

Many of our most important decisions are, in fact, attempts to forecast the

future. When we send a child to private school, it is, in part, a bet that money

spent today on schooling will yield happiness and opportunities in the future.

When we decide to have a baby, we’re forecasting that the joy of becoming a parent will outweigh the cost of sleepless nights. When we choose to get

married—though it may seem completely unromantic—we are, at some level,

calculating that the benefits of settling down are greater than the opportunity

of seeing who else comes along. Good decision making is contingent on a

basic ability to envision what happens next.

What fascinated psychologists and economists was how frequently people

managed, in the course of their everyday lives, to choose among various

futures without becoming paralyzed by the complexities of each choice.

What’s more, it appeared that some people were more skilled than others at

envisioning various futures and choosing the best ones for themselves. Why

were some people able to make better decisions?

When Annie graduated from college, she enrolled in a PhD program in

cognitive psychology at the University of Pennsylvania and began collecting

grants and publications. After five years of hard work and a successful run of

papers and awards, with only months to go to her doctorate, she was invited

to give a series of “job talks” at several universities. If she performed well, she was practically guaranteed a prestigious professorship.

The night before her first speech at New York University, she took the

train to Manhattan. She had been feeling anxious all week. At dinner she

began throwing up. She waited an hour, drank a glass of water, and threw up

again. She couldn’t turn off her anxiety. She couldn’t stop thinking that she

was making a mistake, that she didn’t want to be a professor, that she was

only doing all this because it had seemed like the safest, most predictable

path. She called NYU to postpone her talk. Her fiancé flew to Manhattan and

took her back to Philadelphia, where she checked into a hospital. She was

discharged weeks later, but even then her anxiety was like a hot stone in her

stomach. She went straight from the hospital to a classroom at Penn where

she was supposed to teach and somehow made it through the lecture, so

nauseous and jumpy she almost fainted. She couldn’t teach another class, she

decided. She couldn’t give her job talks. She couldn’t become a professor.

She shoved her research into the trunk of her car, sent a note to her

professors saying that she would be hard to reach for a while, and drove west.

Her fiancé had found a house that cost $11,000 outside Billings, Montana.

When Annie arrived, she determined that, even at that price, they had paid

too much. But by then, she was too exhausted to do anything about it. She put her dissertation materials into the closet and settled onto the couch. Her only

goal was to think as little as possible.

A few weeks later, her brother, Howard Lederer, called to invite her to Las

Vegas for a vacation. Howard was a professional poker player, and every

spring for the past few years, he had flown Annie out to sit by the swimming

pool of the Golden Nugget while he played in a tournament. Whenever she

got bored, she would wander inside to watch him compete or play a few

hands of poker herself. When he called this year, however, Annie said she

was too sick to make the trip.

Howard was concerned. Annie loved Vegas. She never turned down a trip.

“Why don’t you at least find a local poker game?” he said. “It might help

you get out of the house.”

By then she was married, and so she asked her husband to make some

inquiries. They learned there was a bar in Billings named the Crystal Lounge

where retired ranchers, construction workers, and insurance agents played

poker every afternoon in the basement. It was a smoke-filled, joyless

dungeon. Annie went one afternoon and loved it. She went again a few days

later and walked out fifty dollars richer. “Playing poker down there was this

combination of math, which I loved, and all of the cognitive science stuff I had been doing in graduate school,” Annie told me. “You could watch people

try to bluff each other and hide their excitement when they got a good hand,

and all these other kinds of behaviors we’d spent hours talking about in

classes. Every night, I would call my brother and talk through the hands I had

played that day, and he would explain my mistakes, or how someone else had

figured out my game and had started using that against me or what I should

do different next time.” Initially, she wasn’t very good. But she won often

enough to keep going. She noticed that her stomach never hurt at the poker

table.

Pretty soon, she was going to the Crystal Lounge every weekday, like a

job, arriving at three P.M. and staying until midnight, taking notes and testing

strategies. Her brother sent her a check for $2,400 with the agreement that he

would get half her winnings. She was up $2,650 by the end of the first month

even after his cut. The next spring, when he invited her to Vegas, she drove

fourteen hours, bought a seat in a tournament, and by the end of the first day

had $30,000 in chips.

Thirty thousand dollars was more than Annie had ever earned in a full year

as a grad student. She understood poker—understood it better than many of

the people she was playing against. She understood that a losing hand isn’t

necessarily a loss. Rather, it’s an experiment. “The thing I had figured out by

that point was the difference between intermediate and elite players,” Annie

told me. “At the intermediate level, you want to know as many rules as

possible. Intermediate players crave certainty. But elite players can use that

craving against them, because it makes intermediate players more

predictable.

“To be elite, you have to start thinking about bets as ways of asking other

players questions. Are you willing to fold right now? Do you want to raise?

How far can I push before you start acting impulsively? And when you get an

answer, that allows you to predict the future a little bit more accurately than

the other guy. Poker is about using your chips to gather information faster

than everyone else.”

By the end of the tournament’s second day, Annie had $95,000 in chips.

She finished in twenty-sixth place, ahead of hundreds of professionals, some

with decades of experience. Three months later, she and her husband moved

to Las Vegas. At some point, she called her professors at Penn and told them

she wasn’t coming back.

A full minute has passed. Annie still has a pair of tens. If the FossilMan is holding a higher pair—say, two queens—and Annie stays in the hand, it’s

almost certain she’ll be eliminated from the Tournament of Champions. But

if she wins the hand, she’ll become the table’s chip leader.

All of the odds and probability charts floating around Annie’s head are

telling her to do one thing: Match the FossilMan’s bet. But every time Annie

has asked the FossilMan a question during this tournament by placing a

wager, he’s answered with a highly rational response. He’s never put

everything on the line without a good reason. Now, in this hand, he’s pushed

all of his chips into the pot, even as Annie has raised again and again.

Annie is aware that the FossilMan knows how hard it is for her to back

down at this point. He knows that, unlike some of the other people at this table, she isn’t in the Poker Hall of Fame. This is her first time in front of a

million television viewers. He might even know that she’s worried she

doesn’t belong here at all, that she suspects she was only invited because the

TV producers wanted a woman at the table.

Annie suddenly realizes she’s been thinking about this hand wrong all

along. The FossilMan has been betting as if he has a good hand because, in

fact, he has a good hand. Annie has been overthinking—or, at least, she

thinks she’s been overthinking. She’s not sure.

She looks at her pair of tens, looks at the $450,000 on the table, and folds.

The FossilMan takes the money. Annie has no idea if she just made a good or

bad choice because the FossilMan doesn’t have to show anyone his cards.

Another player leans over. You completely misread the situation, he whispers

to her. If you had stayed in, you would have won.

A few hands later, Annie has already folded when the FossilMan, with a

ten and a nine, bets all his chips once again. It’s a smart play, the right move,

but as the other cards fall on the table, they go against him. Even the smartest

poker players can be undone by bad luck. Probabilities can help you forecast

likelihoods, but they can’t guarantee the future. Just like that, the FossilMan

is out of the tournament. As he stands to leave, he leans over to Annie.

“I know the hand you had earlier was really hard for you,” he tells her. “I

want you to know that I had two kings and you made a good fold.”

When he says that, the knot of panic in her stomach melts. She’s been

distracted ever since she folded against him. She’s been second-guessing

herself, turning the hand over in her mind, trying to figure out if she played it

right or wrong. Now her head is back in the game.

It’s normal, of course, to want to know how things will turn out. It’s scary

when we realize how much rides on choices where we can’t predict the

future. Will my baby be born healthy or sick? Will my fiancée and I still love

each other ten years from now? Does my kid need private school or will the

local public school teach her just as much? Making good decisions relies on

forecasting the future, but forecasting is an imprecise, often terrifying,

science because it forces us to confront how much we don’t know. The

paradox of learning how to make better decisions is that it requires

developing a comfort with doubt.

There are ways, however, of learning to grapple with uncertainty. There are methods for making a vague future more foreseeable by calculating, with

some precision, what you do and don’t know.

Annie is still alive at the Tournament of Champions. She has enough chips

to stay in the game. The dealer gives each player their next cards and another

hand begins.

II.

In 2011, the federal Office of the Director of National Intelligence

approached a handful of universities with grant money and asked them to

participate in a project “to dramatically enhance the accuracy, precision, and

timeliness of intelligence forecasts.” The idea was that each school would

recruit a team of foreign affairs experts, and then ask them to make

predictions about the future. Researchers would study who made the most

accurate forecasts and, crucially, how they did it. Those insights, the

government hoped, would help CIA analysts become better at their jobs.

Most of the universities that participated in the program took a standard

approach. They sought out professors, graduate students, international policy

researchers, and other specialists. Then they gave them questions no one yet

knew the answers to—Will North Korea reenter arms talks by the end of the

year? Will the Civic Platform party win the most seats in the Polish

parliamentary elections?—and watched how they went about answering.

Studying various approaches, everyone figured, would provide the CIA with

some fresh ideas.

Two of the universities, however, took a different tack. A group of

psychologists, statisticians, and political scientists from the University of

Pennsylvania and the University of California–Berkeley, working together,

decided to use the government’s money as an opportunity to see if they could

train regular people to become better forecasters. This group called

themselves “the Good Judgment Project,” and rather than recruit specialists,

the GJP solicited thousands of people—lawyers, housewives, master’s

students, voracious newspaper readers, and enrolled them in online

forecasting classes that taught them different ways of thinking about the future. Then, after the training, those participants were asked to answer the

same foreign affairs questions as the experts.

For two years, the GJP conducted training sessions, watched people make

predictions, and collected data. They tracked who got better, and how

performance changed as people were exposed to different types of tutorials.

Eventually, the GJP published their findings: Giving participants even brief training sessions in research and statistical techniques—teaching them

various ways of thinking about the future—boosted the accuracy of their

predictions. And most surprising, a particular kind of lesson—training in how

to think probabilistically—significantly increased people’s abilities to

forecast the future.

The lessons on probabilistic thinking offered by the GJP had instructed

participants to think of the future not as what’s going to happen, but rather as

a series of possibilities that might occur. It taught them to envision tomorrow

as an array of potential outcomes, all of which had different odds of coming

true. “Most people are sloppy when they think about the future,” said Lyle

Ungar, a professor of computer science at the University of Pennsylvania

who helped oversee the GJP. “They say things like, ‘It’s likely we’ll go to

Hawaii for vacation this year.’ Well, does that mean that it’s 51 percent

certain? Or 90 percent? Because that’s a big difference if you’re buying

nonrefundable tickets.” The goal of the GJP’s probabilistic training was to

show people how to turn their intuitions into statistical estimates.

One exercise, for instance, asked participants to analyze if the French

president Nicolas Sarkozy would win reelection in an upcoming vote.

The training indicated that, at a minimum, there were three variables

someone should consider in predicting Sarkozy’s reelection chances. The

first was incumbency. Data from previous French elections indicated that an

incumbent such as President Sarkozy, on average, can expect to receive 67

percent of the vote. Based on that, someone might forecast that Sarkozy is 67

percent likely to remain in office.

But there were other variables to take into account, as well. Sarkozy had

fallen into disfavor among French voters, and pollsters had estimated that,

based on low approval ratings, Sarkozy’s reelection chances were actually 25

percent. Under that logic, there was a three-quarters chance he would be

voted out. It was also worth considering that the French economy was

limping along, and economists guessed that, based on economic performance,

Sarkozy would garner only 45 percent of the vote.

So there were three potential futures to consider: Sarkozy could earn 67

percent, 25 percent, or 45 percent of the votes cast. In one scenario, he would

win easily, in another he would lose by a wide margin, and the third scenario

was a relatively close call. How do you combine those contradictory

outcomes into one prediction? “You simply average your estimates based on

incumbency, approval ratings, and economic growth rates,” the training

explained. “If you have no basis for treating one variable as more important

than another, use equal weighting. This approach leads you to predict [(67%

+ 25% + 45%)/3] = approximately a 46% chance of reelection.”

Nine months later, Sarkozy received 48.4 percent of the vote and was

replaced by François Hollande.

This is the most basic kind of probabilistic thinking, a simplistic example

that teaches an underlying idea: Contradictory futures can be combined into a

single prediction. As this kind of logic gets more sophisticated, experts

usually begin speaking about various outcomes as probability curves—graphs

that show the distribution of potential futures. For instance, if someone was

asked to guess how many seats Sarkozy’s party was going to win in the

French parliament, an expert might describe the possible outcomes as a curve

that shows how the likelihood of winning parliamentary seats is linked to

Sarkozy’s odds of remaining president:

In fact, when Sarkozy lost the election, his party, the Union pour un

Mouvement Populaire, or UMP, also suffered at the polls, claiming only 194

seats, a significant decline.

The GJP’s training modules instructed people in various methods for

combining odds and comparing futures. Throughout, a central idea was

repeated again and again. The future isn’t one thing. Rather, it is a multitude

of possibilities that often contradict one another until one of them comes true.

And those futures can be combined in order for someone to predict which one

is more likely to occur.

This is probabilistic thinking. It is the ability to hold multiple, conflicting

outcomes in your mind and estimate their relative likelihoods. “We’re not

accustomed to thinking about multiple futures,” said Barbara Mellers, another

GJP leader. “We only live in one reality, and so when we force ourselves to

think about the future as numerous possibilities, it can be unsettling for some

people because it forces us to think about things we hope won’t come true.”

Simply exposing participants to probabilistic training was associated with

as much as a 50 percent increase in the accuracy of their predictions, the GJP

researchers wrote. “Teams with training that engaged in probabilistic thinking

performed best,” an outside observer noted. “Participants were taught to turn

hunches into probabilities. Then they had online discussions with members of

their team [about] adjusting the probabilities, as often as every day….Having

grand theories about, say, the nature of modern China was not useful. Being

able to look at a narrow question from many vantage points and quickly

readjust the probabilities was tremendously useful.”

Learning to think probabilistically requires us to question our assumptions

and live with uncertainty. To become better at predicting the future—at

making good decisions—we need to know the difference between what we

hope will happen and what is more and less likely to occur.

“It’s great to be 100 percent certain you love your girlfriend right now, but

if you’re thinking of proposing to her, wouldn’t you rather know the odds of

staying married over the next three decades?” said Don Moore, a professor at

UC-Berkeley’s Haas School of Business who helped run the GJP. “I can’t tell

you precisely whether you’ll be attracted to each other in thirty years. But I

can generate some probabilities about the odds of staying attracted to each other, and probabilities about how your goals will coincide, and statistics on

how having children might change the relationship, and then you can adjust

those likelihoods based on your experiences and what you think is more or

less likely to occur, and that’s going to help you predict the future a little bit

better.

“In the long run, that’s pretty valuable, because even though you know

with 100 percent certainty that you love her right now, thinking

probabilistically about the future can force you to think through things that

might be fuzzy today, but are really important over time. It forces you to be

honest with yourself, even if part of that honesty is admitting there are things

you aren’t sure about.”

When Annie started playing poker seriously, it was her brother who sat her

down and explained what separated the winners from everyone else. Losers,

Howard said, are always looking for certainty at the table. Winners are

comfortable admitting to themselves what they don’t know. In fact, knowing

what you don’t know is a huge advantage—something that can be used against other players. When Annie would call Howard and complain that she

had lost, had suffered bad luck, that the cards had gone against her, he would

tell her to stop whining.

“Have you considered that you might be the idiot at the table who’s

looking for certainty?” he asked.

In Texas Hold’Em, the kind of poker Annie was playing, each player

received two private cards, and then five communal cards were dealt, faceup,

onto the middle of the table to be shared by everyone. The winner was

whoever had the best combination of private and communal cards.

When Howard was learning to play, he told Annie, he would go to a late-

night game with Wall Street traders, world-champion bridge players, and

other assorted math nerds. Tens of thousands of dollars would trade hands as

they played until dawn, and then everyone would get breakfast together and

deconstruct the games. Howard eventually realized that the hard part of poker

wasn’t the math. With enough practice, anyone can memorize odds or learn

to estimate the chances of winning a pot. No, the hard part was learning to make choices based on probabilities.

For example, let’s say you’re playing Texas Hold’Em, and you have a

queen and nine of hearts as your private cards, and the dealer has put four communal cards on the table:

One more communal card is going to be dealt. If that last card is a heart,

you have a flush, or five hearts, which is a strong hand. A quick mental

calculation tells you that since there are 52 cards in a deck, and 4 hearts are

already showing, there are 9 possible hearts remaining that might be dealt

onto the table, as well as 37 nonheart cards. Put differently, there are 9 cards

that will get you a flush, and 37 that won’t. The odds, then, of getting a flush

are 9 to 37, or roughly 20 percent. *1

In other words, there’s an 80 percent chance you won’t make the flush and

could lose your money. A novice player, based on those odds, will often fold

and get out of the hand. That’s because a novice is focused on certainties:

The odds of getting a flush are relatively small. Rather than throw money

away on an unlikely outcome, they’ll quit.

But an expert sees this hand differently. “A good poker player doesn’t care

about certainty,” Annie’s brother told her. “They care about knowing what

they know and don’t know.”

For instance, if an expert is holding a queen and nine of hearts and hoping

for a flush, and she sees her opponent bet $10, bringing the total pot to $100,

a second set of probabilities starts getting calculated. To stay in the game—

and see if the last card is a heart—the expert needs only to match the last wager, $10. If the expert bets $10 and makes the flush, she’ll win $100. The

expert is being offered “pot odds” of 10 to 1, because if she wins, she’ll get

$10 for every $1 she bets right now.

Now the expert player can compare those odds by imagining this hand one

hundred times. The expert doesn’t know if she is going to win or lose this hand, but she does know that if she played this exact same hand one hundred

times, she would, on average, win twenty times, collecting $100 with each

victory, yielding $2,000.

And she knows that playing one hundred times will cost her only an

additional $1,000 (because she has to bet only $10 each time). So even if she

lost eighty times and won only twenty times, she would still pocket an extra

$1,000 (which is the winnings of $2,000 less the $1,000 needed to play).

Got it? It’s okay if you don’t, because the point here is that probabilistic thinking tells the expert how to proceed: She is aware there’s a lot she can’t

predict. But if she played this same hand one hundred times, she would

probably end up $1,000 richer. So the expert makes the bet and stays in the

game. She knows, from a probabilistic standpoint, it will pay off over time. It

doesn’t matter that this hand is uncertain. What matters is committing to odds

that pay off in the long run.

“Most players are obsessed with finding the certainty on the table, and it

colors their choices,” Annie’s brother told her. “Being a great player means

embracing uncertainty. As long as you’re okay with uncertainty, you can

make the odds work for you.”

Annie’s brother, Howard, is competing in this Tournament of Champions

right alongside her when the FossilMan is eliminated. Over the past two

decades, Howard has established himself as one of the finest players in the

world. He has two World Series of Poker bracelets and millions in winnings.

Early in the tournament, Annie and Howard lucked out and didn’t have to

directly compete for many big pots. Now, however, seven hours have passed.

First the FossilMan was eliminated by that bit of bad luck. Another

competitor named Doyle Brunson, a seventy-one-year-old nine-time

champion, was knocked out by a risky attempt to double his chips. Phil Ivey,

who won his first World Series of Poker tournament at twenty-four, was

eliminated by Annie when she drew an ace and queen against Ivey’s ace and

eight. Over time, the players at the table have dwindled until there are only three players remaining: Annie, Howard, and a man named Phil Hellmuth. It

is inevitable Annie and Howard will butt up against each other. The

contestants spar over chips and hands for ninety minutes. Then Annie gets a

pair of sixes.

She starts tallying what she does and doesn’t know. She knows she has

strong cards. She knows, from a probabilistic standpoint, that if she played

this hand one hundred times, she would do okay. “Sometimes when I’m

teaching poker, I’ll tell people there are situations where you shouldn’t even

look at your cards before you bet,” Annie told me. “Because if the pot odds

are in your favor, you should always make the bet. Just commit to it.”

Howard, her brother, seems to like his hand as well, because he pushes all

of his chips, $310,000, onto the table. Phil Hellmuth folds. The bet is to

Annie.

“I’ll call,” she says.

They both turn over their cards. Annie reveals her pair of sixes.

Howard reveals a pair of sevens.

“Nice hand, Bub,” Annie says. Howard has an 82 percent chance of

winning this hand, collecting chips worth more than half a million dollars,

and becoming the table’s dominant leader. From a probabilistic perspective,

they both played this hand exactly right. “Annie made the right choice,”

Howard later said. “She committed to the odds.”

The dealer turns over the first three communal cards.

“Oh, God,” Annie says and covers her face. “Oh, God.”

The six and the two queens in the communal pile give Annie a full house.

If Annie and Howard replayed this hand one hundred times, Howard would

likely win eighty-two of those contests. But not this time. The dealer puts the

remaining cards on the table.

Howard is out.

Annie jumps from her chair and hugs her brother. “I’m sorry, Howard,”

she whispers. Then she runs out of the studio. She starts sobbing before she

makes it to the door.

“It’s okay,” Howard says when he finds her in the hall. “Just beat Phil

now.”

“You have to learn to live with it,” Howard told me later. “I just went

through this same thing with my son. He was applying to colleges and he was

nervous about it, so we came up with a list of twelve schools—four safety

schools, four he had an even chance of getting into, and four that were

stretches—and we sat down and started calculating the odds.”

By looking at the statistics those schools had published online, Howard

and his son calculated the likelihood of getting into each college. Then they

added all those probabilities together. It was fairly basic math, the kind even

English majors can manage with a little bit of Googling. They figured out

that Howard’s son had a 99.5 percent chance of getting into at least one

school, and a better than even chance of getting into a good school. But it was

far from certain he would get into one of the stretch schools, the ones he had fallen in love with. “That was disappointing, but by going through the

numbers, he felt less anxious,” Howard said. “It prepared him for the

possibility that he wouldn’t get into his first choice, but he would definitely

get in somewhere.

“Probabilities are the closest thing to fortune-telling,” Howard said. “But

you have to be strong enough to live with what they tell you might occur.”

III.

In the late 1990s, a professor of cognitive science at the Massachusetts

Institute of Technology named Joshua Tenenbaum began a large-scale

examination of the casual ways that people make everyday predictions. There

are dozens of questions each of us confront on a daily basis that can be

answered only with some amount of forecasting. When we estimate how long

a meeting will last, for instance, or envision two driving routes and guess at

which one will have less traffic, or predict whether our families will have

more fun at the beach or at Disneyland, we’re making forecasts that assign

likelihoods to various outcomes. We may not realize it, but we’re thinking

probabilistically. How, Tenenbaum wondered, do our brains do that?

Tenenbaum’s specialty was computational cognition—in particular, the

similarities in how computers and humans process information. A computer

is an inherently deterministic machine. It can predict if your family will

prefer the beach or Disneyland only if you give it a specific formula for

comparing the merits of beach fun versus amusement parks. Humans, on the

other hand, can make such decisions even if we’ve never visited the seaside

or Magic Kingdom before. Our brains can infer from past experiences that,

because the kids always complain when we go camping and love watching

cartoons, everyone will probably have more fun with Mickey and Goofy.

“How do our minds get so much from so little?” Tenenbaum wrote in a

paper published in the journal Science in 2011. “Any parent knows, and scientists have confirmed, that typical 2-year-olds can learn how to use a new

word such as ‘horse’ or ‘hairbrush’ from seeing just a few examples.” To a

two-year-old, horses and hairbrushes have a great deal in common. The

words sound similar. In pictures, they both have long bodies with a series of

straight lines—in one case legs, in the other bristles—extruding outward.

They come in a range of colors. And yet, though a child might have seen only

one picture of a horse and used only one hairbrush, she can quickly learn the

difference between those words.

A computer, on the other hand, needs explicit instructions to learn when to

use “horse” versus “hairbrush.” It needs software that specifies that four legs

increases the odds of horsiness, while one hundred bristles increases the

probability of a hairbrush. A child can make such calculations before she can

form sentences. “Viewed as a computation on sensory input data, this is a

remarkable feat,” Tenenbaum wrote. “How does a child grasp the boundaries

of these subsets from seeing just one or a few examples of each?”

In other words, why are we so good at forecasting certain kinds of things—

and thus, making decisions—when we have so little exposure to all the

possible odds?

In an attempt to answer this question, Tenenbaum and a colleague, Thomas

Griffiths, devised an experiment. They scoured the Internet for data on

different kinds of predictable events, such as how much money a movie will

make at the box office, or how long the average person lives, or how long a

cake needs to bake. They were interested in these events because if you were

to graph multiple examples of each one, a distinct pattern would emerge. Box

office totals, for instance, typically conform to a basic rule: There are a few

blockbusters each year that make a huge amount of money, and lots of other

films that never break even.

Within mathematics, this is known as a “power law distribution,” and

when the revenues of all the movies released in a given year are graphed

together, it looks like this:

Graphing other kinds of events results in different patterns. Take life spans.

A person’s odds of dying in a specific year spike slightly at birth—because

some infants perish soon after they arrive—but if a baby survives its first few

years, it is likely to live decades longer. Then, starting at about age forty, our

odds of dying start accelerating. By fifty, the likelihood of death jumps each

year until it peaks at about eighty-two.

Life spans adhere to a normal, or Gaussian, distribution curve. That pattern

looks like this:

Most people intuitively understand that they need to apply different kinds

of reasoning to predicting different kinds of events. We know that box office

totals and life spans require different types of estimations, even if we don’t know anything about medical statistics or entertainment industry trends.

Tenenbaum and Griffiths were curious to find out how people intuitively

learn to make such estimations. So they found events with distinct patterns,

from box office grosses to life spans, as well as the average length of poems,

the careers of congressmen (which adhere to an Erlang distribution), and the

length of time a cake needs to bake (which has no strong pattern).

Then they asked hundreds of students to predict the future based on one

piece of data:

You read about a movie that has made $60 million to date. How

much will it make in total?

You meet someone who is thirty-nine years old. How long will

he or she live?

A cake has been baking for fourteen minutes. How much longer

does it need to stay in the oven?

You meet a U.S. congressman who has served for fifteen years.

How long will he serve in total?

The students weren’t given any additional information. They weren’t told anything about power law distributions or Erlang curves. Rather, they were

simply asked to make a prediction based on one piece of data and no

guidance about what kinds of probabilities to apply.

Despite those handicaps, the students’ predictions were startlingly

accurate. They knew that a movie that’s earned $60 million is a blockbuster,

and is likely to take in another $30 million in ticket sales. They intuited that if

you meet someone in their thirties, they’ll probably live another fifty years.

They guessed that if you meet a congressman who has been in power for

fifteen years, he’ll probably serve another seven or so, because incumbency

brings advantages, but even powerful lawmakers can be undone by political

trends.

If asked, few of the participants were able to describe the mental logic they

used to make their forecasts. They just gave answers that felt right. On average, their predictions were often within 10 percent of what the data said

was the correct answer. In fact, when Tenenbaum and Griffiths graphed all of

the students’ predictions for each question, the resulting distribution curves

almost perfectly matched the real patterns the professors had found in the data they had collected online.

Just as important, each student intuitively understood that different kinds

of predictions required different kinds of reasoning. They understood,

without necessarily knowing why, that life spans fit into a normal distribution

curve whereas box office grosses tend to conform to a power law.

Some researchers call this ability to intuit patterns “Bayesian cognition” or

“Bayesian psychology,” because for a computer to make those kinds of

predictions, it must use a variation of Bayes’ rule, a mathematical formula

that generally requires running thousands of models simultaneously and

comparing millions of results.*2 At the core of Bayes’ rule is a principle: Even if we have very little data, we can still forecast the future by making assumptions and then skewing them based on what we observe about the

world. For instance, suppose your brother said he’s meeting a friend for

dinner. You might forecast there’s a 60 percent chance he’s going to meet a

man, since most of your brother’s friends are male. Now, suppose your

brother mentioned his dinner companion was a friend from work. You might

want to change your forecast, since you know that most of his coworkers are

female. Bayes’ rule can calculate the precise odds that your brother’s dinner

date is female or male based on just one or two pieces of data and your

assumptions. As more information comes in—his companion’s name is Pat,

he or she loves adventure movies and fashion magazines—Bayes’ rule will

refine the probabilities even more.

Humans can make these kinds of calculations without having to think

about them very hard, and we tend to be surprisingly accurate. Most of us

have never studied actuarial tables of life spans, but we know, based on

experience, that it is relatively uncommon for toddlers to die and more typical

for ninety-year-olds to pass away. Most of us don’t pay attention to box

office statistics. But we are aware that there are a few movies each year that

everyone sees, and a bunch of films that disappear from the theaters within a

week or two. So we make assumptions about life spans and box office

revenues based on our experiences, and our instincts become increasingly

nuanced the more funerals or movies we attend. Humans are astoundingly

good Bayesian predictors, even if we’re unaware of it.

Sometimes, however, we make mistakes. For instance, when Tenenbaum

and Griffiths asked their students to predict how long an Egyptian pharaoh

would reign if he has already ruled for eleven years, a majority of them

assumed that pharaohs are similar to other kinds of royalty, such as European

kings. Most people know, from reading history books and watching

television, that some royalty die early in life. But, in general, if a king or queen survives to middle age, they usually stay on the throne until their hair

is gray. So it seemed logical, to Tenenbaum’s participants, that pharaohs

would be similar. They offered a range of guesses with an average of about

twenty-three additional years in power:

That would be a great guess for a British king. But it’s a bad guess for an

Egyptian pharaoh, because four thousand years ago people had much shorter

life spans. Most pharaohs were considered elderly if they made it to thirty-

five. So the correct answer is that a pharaoh with eleven years on the throne

is expected to reign only another twelve years and then die of disease or some

other common cause of death in ancient Egypt:

The students got the reasoning right. They intuited correctly that

calculating a pharaoh’s reign follows an Erlang distribution. But their

assumption—what Bayesians call the “prior” or “base rate”—was off. And

because they had a bad assumption about how long ancient Egyptians lived,

their subsequent predictions were skewed, as well.

“It’s incredible that we’re so good at making predictions with such little

information and then adjusting them as we absorb data from life,”

Tenenbaum told me. “But it only works if you start with the right

assumptions.”

So how do we get the right assumptions? By making sure we are exposed

to a full spectrum of experiences. Our assumptions are based on what we’ve

encountered in life, but our experiences often draw on biased samples. In

particular, we are much more likely to pay attention to or remember

successes and forget about failures. Many of us learn about the business

world, for instance, by reading newspapers and magazines. We most

frequently go to busy restaurants and see the most popular movies. The

problem is that such experiences disproportionately expose us to success.

Newspapers and magazines tend to devote more coverage to start-ups that

were acquired for $1 billion, and less to the hundreds of similar companies

that went bankrupt. We hardly notice the empty restaurants we pass on the way to our favorite, crowded pizza place. We become trained, in other words,

to notice success and then, as a result, we predict successful outcomes too

often because we’re relying on experiences and assumptions that are biased

toward all the successes we’ve seen—rather than the failures we’ve

overlooked.

Many successful people, in contrast, spend an enormous amount of time

seeking out information on failures. They read inside the newspaper’s

business pages for articles on companies that have gone broke. They schedule

lunches with colleagues who haven’t gotten promoted, and then ask them

what went wrong. They request criticisms alongside praise at annual reviews.

They scrutinize their credit card statements to figure out why, precisely, they

haven’t saved as much as they hoped. They pick over their daily missteps

when they get home, rather than allowing themselves to forget all the small

errors. They ask themselves why a particular call didn’t go as well as they

had hoped, or if they could have spoken more succinctly at a meeting. We all

have a natural proclivity to be optimistic, to ignore our mistakes and forget others’ tiny errors. But making good predictions relies on realistic

assumptions, and those are based on our experiences. If we pay attention only

to good news, we’re handicapping ourselves.

“The best entrepreneurs are acutely conscious of the risks that come from

only talking to people who have succeeded,” said Don Moore, the Berkeley

professor who participated in the GJP and who also studies the psychology of

entrepreneurship. “They are obsessed with spending time around people who

complain about their failures, the kinds of people the rest of us usually try to

avoid.”

This, ultimately, is one of the most important secrets to learning how to

make better decisions. Making good choices relies on forecasting the future.

Accurate forecasting requires exposing ourselves to as many successes and

disappointments as possible. We need to sit in crowded and empty theaters to

know how movies will perform; we need to spend time around both babies

and old people to accurately gauge life spans; and we need to talk to thriving

and failing colleagues to develop good business instincts.

This is hard, because success is easier to stare at. People tend to avoid

asking friends who were just fired rude questions; we’re hesitant to

interrogate divorced colleagues about what precisely went wrong. But

calibrating your base rate requires learning from both the accomplished and

the humbled.

So the next time a friend misses out on a promotion, ask him why. The

next time a deal falls through, call up the other side to find out what you did

wrong. The next time you have a bad day or you snap at your spouse, don’t

simply tell yourself that things will go better next time. Instead, force

yourself to really figure out what happened.

Then use those insights to forecast more potential futures, to dream up

more possibilities of what might occur. You’ll never know with 100 percent

certainty how things will turn out. But the more you force yourself to

envision potential futures, the more you learn about which assumptions are

certain or flimsy, the better your odds of making a great decision next time.

Annie knows a lot about Bayesian thinking from graduate school, and she

uses it in poker games. “When I play against someone I’ve never met before,

the first thing I do is start thinking about base rates,” she told me. “To

someone who has never studied Bayes’ rule, the way I play might seem like

I’m prejudiced, because if I’m sitting across from, say, a forty-year-old

businessman, I’m going to assume all he cares about is telling his friends he

played against pros and he doesn’t really care about winning, so he’ll take

lots of risks. Or, if I’m sitting across from a twenty-two-year-old in a poker

T-shirt, I’m going to assume he learned to play online so he’s got a tight, limited game.

“But the difference between prejudice and Bayesian thinking is that I try to

improve my assumptions as we go along. So once we start playing, if I see that the forty-year-old is a great bluffer, that might mean he’s a professional

hoping everyone will underestimate him. Or, if the twenty-two-year-old is

trying to bluff every hand, it probably means he’s some rich kid who doesn’t

know what he’s doing. I spend a lot of time updating my assumptions

because, if they’re wrong, my base rate is off.”

With Annie’s brother out of the competition, there are only two players left

at the Tournament of Champions table: Annie and Phil Hellmuth. Hellmuth is

a card room legend, a television celebrity known as “the Poker Brat.” “I’m

the Mozart of poker,” he told me. “I can read other players probably better than anyone playing, maybe anyone in the world. It’s white magic, instinct.”

Annie is at one end of the table, Hellmuth at the other. “I had a good idea

of how Phil viewed me at that point,” Annie said later. “He’s told me before

that he has a low opinion of my creativity, that he thinks I’m more lucky than

smart, that I’m too scared to bluff when it matters.”

That’s a problem for Annie, because she wants Phil to think she’s bluffing.

The only way she can lure him into a big pot is by convincing him she’s

bluffing when, in fact, she isn’t. To win this tournament, Annie needs to force

Phil to change his assumptions of her.

Phil, though, has a different plan. He believes he’s the stronger player. He

believes he can read Annie. “I have this capacity to learn very, very quickly,”

he told me. “When I know what people are doing, I can control the table.”

Those aren’t idle boasts. Hellmuth has won fourteen poker championships.

Annie and Phil have roughly equal piles of chips. For the next hour, they

play hand after hand, neither gaining a clear advantage. Phil keeps subtly

trying to throw Annie off, to make her mad or lose her cool.

“I would have preferred to play your brother,” he says.

“This is all right,” Annie replies. “I’m just happy to be in the finals.”

Annie bluffs Phil four times. “I wanted him to reach the breaking point

where he says, ‘Screw this, she’s bluffing me hand after hand and I gotta

fight back,’ ” Annie said. But Phil doesn’t seem shaken. He doesn’t

overreact.

Finally, Annie gets the hand she’s been waiting for. The dealer gives her a

king and a nine. Phil receives a king and a seven. In the middle of the table,

the dealer lays down a communal king, six, nine, and jack.

Phil knows he has a pair of kings. But unbeknownst to him, Annie has two

pair—kings and nines. Neither sees what the other is holding.

It’s Annie’s bet, and she raises $120,000. Phil, thinking his pair of kings is

likely the strongest hand at the table, matches it. Then Annie goes all in,

bringing the pot to $970,000.

The bet is now to Phil.

He starts muttering to himself. “This is unbelievable,” he says out loud.

“Really unbelievable. She might not even know how strong I am here. I’m

not sure she fully even understands the value of the hand.”

He stands up.

“I don’t know,” he says, pacing around the table. “I don’t know, I have a

bad feeling about this hand.” He folds.

Phil flips over his king, showing Annie that he had a pair. Then Annie

strikes: She casually turns over one of her cards—but not both—showing Phil

her pair of nines, but not revealing that she also had a pair of kings.

“I wanted to force him to change his assumptions about me,” Annie later

said. “I wanted him to think I was bluffing with a pair of nines.”

“Wow, did you really just push in with a nine?” Phil says to Annie. “That’s

so reckless, especially against someone like me. Maybe I acted too fast.”

The players ready for the next hand. Annie has $1,460,000 in chips; Phil

has $540,000. The dealer gives them their cards. Annie has a king and a ten;

Phil a ten and an eight. The first communal cards come out as a two, ten, and

seven.

Phil has a pair of tens, with an eight backing it up. It’s a good hand. Annie

also has a pair of tens, with a king, slightly better.

Phil pushes $45,000 into the pot. Annie raises $200,000. It’s an aggressive

move. But Phil is starting to believe that Annie is playing recklessly. He

thinks he sees a pattern he didn’t expect from her: She’s bluffing and bluffing

and bluffing again. Phil’s base rate is gradually shifting.

Phil looks at the pile of chips on the table. Maybe his assumption that

Annie is too scared to bluff at critical moments is wrong? Maybe Annie is

bluffing right now? Maybe she’s finally overplayed her hand?

“I’m all in,” Phil says, pushing his stack into the middle of the table.

“I call,” Annie says.

Both players turn over their cards.

“Shit,” Phil says, seeing that they both have a pair of tens—and that Annie

has the high card, a king to Phil’s eight.

The dealer puts a seven on the table, benefiting neither player.

Annie is now standing, gripping her cheeks. Phil is also on his feet,

breathing hard. “Give me an eight, please,” he says. It’s the only card that will keep him in the game. The dealer turns over the final communal card.

It’s a three.

Annie wins the $2 million. Phil is out. The game is over. Annie is the

champion.

Later, she will tell people that winning this tournament changed her life. It

made her, in effect, the most famous female poker player on earth. In 2010,

she went on to win the National Heads-Up Poker Championship. Today, she

holds a record for World Series of Poker profits. In total, she’s won more

than $4 million. She doesn’t worry about her mortgage anymore. She doesn’t

have panic attacks. In 2009, she appeared on a season of Celebrity

Apprentice. She was a little nervous before the filming started, but not too much. There were no anxiety breakdowns. She doesn’t play in many poker

tournaments these days. She spends most of her time giving lectures to

businesspeople about how to think probabilistically, about how to embrace

uncertainty, about how, if you commit to a Bayesian outlook, you’ll make

better decisions in life.

“A lot of poker comes down to luck,” Annie told me. “Just like life. You

never know where you’ll end up. When I checked myself into the psych

hospital my sophomore year, there’s no way I would have guessed I would

end up as a professional poker player. But you have to be comfortable not

knowing exactly where life is going. That’s how I’ve learned to keep the

anxiety away. All we can do is learn how to make the best decisions that are

in front of us, and trust that, over time, the odds will be in our favor.”

How do we learn to make better decisions? In part, by training ourselves to

think probabilistically. To do that, we must force ourselves to envision

various futures—to hold contradictory scenarios in our minds simultaneously

—and then expose ourselves to a wide spectrum of successes and failures to

develop an intuition about which forecasts are more or less likely to come

true.

We can develop this intuition by studying statistics, playing games like

poker, thinking through life’s potential pitfalls and successes, or helping our

kids work through their anxieties by writing them down and patiently

calculating the odds. There are numerous ways to build a Bayesian instinct.

Some of them are as simple as looking at our past choices and asking

ourselves: Why was I so certain things would turn out one way? Why was I

wrong?

Regardless of our methods, the goals are the same: to see the future as

multiple possibilities rather than one predetermined outcome; to identify what

you do and don’t know; to ask yourself, which choice gets you the best odds?

Fortune-telling isn’t real. No one can predict tomorrow with absolute

confidence. But the mistake some people make is trying to avoid making any

predictions because their thirst for certainty is so strong and their fear of doubt too overwhelming.

If Annie had stayed in academics, would any of this have mattered?

“Absolutely,” she said. “If you’re trying to decide what job to take, or

whether you can afford a vacation, or how much you need to save for

retirement, those are all predictions.” The same basic rules apply. The people

who make the best choices are the ones who work hardest to envision various

futures, to write them down and think them through, and then ask themselves,

which ones do I think are most likely and why?

Anyone can learn to make better decisions. We can all train ourselves to

see the small predictions we make every day. No one is right every time. But

with practice, we can learn how to influence the probability that our fortune-

telling comes true.

*1 Poker is a game of odds within odds. While this example provides an explanation of probabilistic thinking (and the concept of “pot odds”), it is worth noting that a full analysis of this hand is slightly more complex (and would take into account, for instance, the other players at the table). For a more nuanced analysis, please see the notes for chapter 6.

*2 Bayes’ rule, which was first postulated by the Reverend Bayes in a posthumously published 1763

manuscript, can be so computationally complex that for centuries most statisticians essentially ignored the work because they lacked tools to perform the calculations it demanded. Starting in the 1950s, however, as computers became more powerful, scientists found they could use Bayesian approaches to forecast events that were previously thought unpredictable, such as the likelihood of a war, or the odds that a drug will be broadly effective even if it has only been tested on a handful of people. Even today, though, calculating a Bayesian probability curve can, in some cases, tie up a computer for hours.

INNOVATION

How Idea Brokers and Creative Desperation Saved Disney’s Frozen

The audience starts lining up an hour before the screening room doors open.

They are directors and animators, story editors and writers, all of them

Disney employees, all eager to see a rough draft of the movie everyone is

talking about.

As they settle into their chairs and the lights dim, two sisters appear on the

screen against an icy landscape. Anna, the younger character, quickly

establishes herself as bossy and uptight, obsessed with her upcoming

wedding to the handsome Prince Hans and her coronation as queen. Elsa, her

older sister, is jealous, evil—and cursed. Everything she touches turns to ice.

She was passed over for the throne because of this power and now, as she

runs away from her family to a crystal palace high in the mountains, she

nurses a bitter grudge. She wants revenge.

As Anna’s wedding day approaches, Elsa plots with a snarky snowman

named Olaf to claim the crown for herself. They try to kidnap Anna but their

plan is foiled by the square-jawed, dashing Prince Hans. Bitter Elsa, in a rage,

orders an army of snow monsters to descend upon the town and destroy it.

The villagers repel the invaders, but when the smoke clears, casualties are

discovered: Princess Anna’s heart has been partially frozen by her evil sister

—and Prince Hans is missing.

The second half of the film follows Anna as she searches for her prince,

desperately hoping that his kiss will heal her damaged heart. Meanwhile, Elsa

prepares to attack again—and this time floods the village with vicious snow

creatures. The monsters, however, are soon out of her control. They begin to

threaten everyone, including Elsa herself. The only way to survive, Anna and

Elsa realize, is for them to join forces. Through cooperation, they defeat the

creatures and the sisters learn that working together is better than struggling

apart. They become friends. Anna’s heart thaws. Peace returns. Everyone lives happily ever after.

The name of the movie is Frozen, and it is scheduled to be released in just

eighteen months.

Normally, when a movie screening ends at Disney, there’s applause. Often,

people cheer or shout. There are usually boxes of tissues inside the screening

room because, at Disney, a good cry is the sign of a successful film.

This time, there is no crying. There are no cheers. The tissues go

untouched. As everyone files out, they are very, very quiet.

—

After the screening ended, the film’s director, Chris Buck, and about a dozen

other Disney filmmakers gathered in one of the studio’s dining rooms to

discuss what they had just seen. This was a meeting of the studio’s “story

trust,” a group responsible for providing feedback on films as they go through

production. As the story trust prepared to discuss the latest draft of Frozen, people served themselves from a buffet of Swedish meatballs. Buck didn’t

get anything to eat. “The last thing I was feeling was hungry,” he told me.

Disney’s chief creative officer, John Lasseter, kicked off the conversation.

“You’ve got some great scenes here,” he said, and mentioned some of the

things he particularly liked: The battles were thrilling. The dialogue between

the sisters was witty. The snow monsters were terrifying. The film had a

good, fast pace. “It’s an exciting movie, and the animation is going to be

amazing,” he said.

And then he began listing the film’s flaws. The list was long.

“You haven’t dug deep enough,” he said after detailing a dozen problems.

“There’s not enough for the audience to connect with because there’s no

character to root for. Anna’s too uptight and Elsa’s too evil. I didn’t find myself liking anyone in the movie until the very end.”

When Lasseter was done speaking, the rest of the story trust chimed in,

pointing out other problems: There were logical holes in the plot—why, for

instance, does Anna stick with Prince Hans when he doesn’t seem like such a

catch? Also, there were too many characters to keep track of. The plot twists

were foreshadowed way too much. It didn’t seem believable that Elsa would

kidnap her sister and then attack the town without trying something less dramatic first. Anna seemed really whiny for someone who lives in a castle,

is marrying a prince, and would soon be queen. One member of the story

trust—a writer named Jennifer Lee—particularly disliked Elsa’s cynical

sidekick. “I f’ing hate Olaf,” she had scribbled in her notes. “Kill the

snowman.”

The truth was, Buck wasn’t surprised by all the criticisms. His team had

sensed the movie wasn’t working for months. The film’s screenwriter had

restructured the script repeatedly, first with Anna and Elsa as strangers rather

than sisters, then with Elsa, the cursed sister, assuming the throne and Anna

upset at being a “spare, rather than an heir.” The songwriters on the film—a

husband-and-wife team behind such Broadway hits as Avenue Q and The Book of Mormon—were exhausted from writing and discarding song after

song. They said they couldn’t figure out how to make jealousy and revenge

into lighthearted themes.

There were versions of the movie where the sisters were normal

townspeople rather than royalty, and others where the sisters reconciled over

a shared love of reindeer. In one script, they were raised apart. In another, Anna was jilted at the altar. Buck had introduced characters to explain the

origins of Elsa’s curse, and had tried creating another love interest. Nothing

worked. Every time he solved one problem—by making Anna more likable,

for instance, or Elsa less bitter—dozens of others popped up.

“Every movie sucks at first,” said Bobby Lopez, one of Frozen’ s

songwriters. “But this was like a puzzle where every piece we added upset

how everything else fit. And we knew time was running out.”

While most animated projects are given four or five years to mature,

Frozen was on an accelerated schedule. The movie had been in full

production for less than a year, but because another Disney movie had

recently collapsed, executives had moved Frozen’ s release date to November

2013, just a year and a half away. “We had to find answers fast,” said Peter

Del Vecho, the film’s producer. “But they couldn’t feel clichéd or like a

bunch of stories jammed together. The movie had to work emotionally. It was

a pretty stressful time.”

This conundrum of how to spur innovation on a deadline—or, put another

way, how to make the creative process more productive—isn’t unique to

filmmaking, of course. Every day, students, executives, artists, policy makers, and millions of other people confront problems that require inventive

answers delivered as quickly as possible. As the economy changes, and our

capacity to achieve creative insights becomes more important than ever, the

need for fast originality is even more urgent.

For many people, in fact, figuring out how to accelerate innovation is

among their most important jobs. “We’re obsessed with the productivity of

the creative process,” said Ed Catmull, president of Walt Disney Animation

Studios and cofounder of Pixar. “We think it’s something that can be

managed poorly or well, and if we get the creative process right, we find

innovations faster. But if we don’t manage it right, good ideas are

suffocated.”

Inside the story trust, the conversation about Frozen was winding down. “It

seems to me like there’s a few different ideas competing inside this movie,”

Lasseter told Buck, the director. “We’ve got Elsa’s story, we’ve got Anna’s

story, and we’ve got Prince Hans and Olaf the snowman. Each of those

stories has great elements. There’s a lot of really good material here, but you

need to make it into one narrative that connects with the audience. You need

to find the movie’s core.”

Lasseter rose from his seat. “You should take as long as you need to find

the answers,” he said. “But it would be great if it happened soon.”

II.

In 1949, a choreographer named Jerome Robbins contacted his friends

Leonard Bernstein and Arthur Laurents with an audacious idea. They should

collaborate on a new kind of musical, he told them, modeled on Romeo and

Juliet but set in modern-day New York City. They could integrate classical ballet with opera and experimental theater, and maybe bring in contemporary

jazz and modernist drama, as well. Their goal, Robbins said, should be to

establish the avant-garde on Broadway.

Robbins was already famous for creating theater—as well as a life—that

pushed boundaries. He was bisexual at a time when homosexuality was

illegal. He had changed his name from Jerome Rabinowitz to Jerome

Robbins to dodge the anti-Semitism he worried would doom his career. He

had named friends as Communists before the House Un-American Activities

Committee, terrified that if he didn’t cooperate, his sexuality would be

publicly revealed and he would be shunned. He was a bully and a

perfectionist and so despised by dancers that they sometimes refused to speak

to him off the stage. But few refused his invitations to perform. He was

widely acknowledged—revered, actually—as one of the most creative artists

of his time.

Robbins’s Romeo and Juliet idea was particularly bold because big

Broadway musicals, in those days, tended to adhere to fairly predictable

blueprints. Stories were built around a male and a female lead who pushed

the plot along with dialogue that was spoken, not sung. There were choruses

and dancers, as well as elaborate sets and a few duets about midway through

each show. The elements of plot, song, and dance, however, weren’t

intertwined as they were in, say, ballet, where the story and dancing are one,

or opera, where dialogue is sung and music shapes the drama as much as any

actor on the stage.

For this new show, Robbins wanted to try something different. “Why

couldn’t we, in aspiration, try to bring our deepest talents together?” Robbins

later said. “Why did Lenny have to write an opera, Arthur a play, me a

ballet?” The three men wanted to create something that felt modern yet timeless. When Bernstein and Laurents saw a newspaper article about race

riots, they proposed making their musical about two lovers—one Puerto

Rican, the other white—whose families were affiliated with warring gangs.

The name of the show, they decided, would be West Side Story.

Over the next few years the men traded scripts, scores, and choreography

ideas. They mailed one another drafts during their long months apart. After

half a decade of work, though, Robbins was impatient. This musical was

important, he wrote to Bernstein and Laurents. It would break new ground.

They needed to finish the script. To speed things up, he suggested, they

should stop trying to do something new at every turn. Instead, they should

stick with conventions they knew, from trial and error, had worked in other

shows. But they should combine those conventions in novel ways.

For instance, they had been wrestling with the first meeting between Tony

and Maria, the musical’s main characters. They should take a page from

Shakespeare, Robbins suggested, and have the lovers see each other across a

dance floor. But it should be made contemporary, a place where “a wild

mambo is in progress with the kids doing all the violent improvisation of

jitterbugging.”

For the battle in which Tony kills his enemy, Robbins said that the

choreography ought to imitate the way battles are staged in motion pictures.

“The fight scene must be provoked immediately,” Robbins wrote, “or else

we’re boring the audience.” During a dramatic encounter between Tony and

Maria, they needed something that echoed the classical marriage scene of

Romeo and Juliet, but also incorporated the theatricality of opera and a bit of

the sentimental romanticism that Broadway audiences loved.

The biggest challenge, however, was figuring out which theatrical

conventions were truly powerful and which had become clichés. Laurents, for

example, had written a script that was divided into the traditional three acts,

but it’s “a serious mistake to let the audience out of our grip for two

intermissions,” Robbins wrote. Motion pictures had proven that you can keep

audiences in their seats if the action is always progressing. What’s more,

Robbins wrote to Laurents, “I like best the sections in which you have gone

on your own path, writing in your own style with your own characters and

imagination. Least successful are those in which I sense the intimidation of

Shakespeare standing behind you.” Similarly, roles that were too predictable

had to be avoided at all costs. “You are way off the track with the whole

character of Anita,” Robbins wrote to his colleagues. “She is the typical

downbeat blues torch-bearing 2nd character,” he remarked. “Forget Anita.”

By 1957—eight years after they had first embarked on the project—the

men were finally done. They had combined different kinds of theater to

create something new: a musical where dance, song, and dialogue were

integrated into a story of racism and injustice that was as contemporary as the

newspapers sold outside the theater doors. All that was left was to find

financial backers. Nearly every producer they approached turned them down.

The show was too different from what audiences expected, the moneymen

said. Finally, Robbins found financiers willing to support a staging in

Washington, D.C.—far enough from Broadway, everyone hoped, that if the

show bombed, the news might not spread to New York.

The method Robbins suggested for jump-starting the creative process—

taking proven, conventional ideas from other settings and combining them in

new ways—is remarkably effective, it turns out. It’s a tactic all kinds of

people have used to spark creative successes. In 2011, two Northwestern

University business school professors began examining how such

combinations occur in scientific research. “Combinations of existing material

are centerpieces in theories of creativity, whether in the arts, the sciences, or

commercial innovation,” they wrote in the journal Science in 2013. And yet

most original ideas grow out of old concepts, and “the building blocks of new

ideas are often embodied in existing knowledge.” Why are some people so

much better at taking those old blocks and stacking them in new ways, the

researchers wondered?

The researchers—Brian Uzzi and Ben Jones—decided to focus on an

activity they were deeply familiar with: writing and publishing academic

papers. They had access to a database of 17.9 million scientific manuscripts

published in more than twelve thousand journals. The researchers knew there

was no objective way to measure each paper’s creativity, but they could

estimate a paper’s originality by analyzing the sources authors had cited in their endnotes. “A paper that combines work by Newton and Einstein is

conventional. The combination has happened thousands of times,” Uzzi told me. “But a paper that combines Einstein and Wang Chong, the Chinese

philosopher, that’s much more likely to be creative, because it’s such an

unusual pairing.” Moreover, by focusing primarily on the most popular

manuscripts in the database—those studies that had been cited by other

researchers thousands of times—they could estimate each manuscript’s

creative input. “To get into the top 5 percent of the most frequently cited

studies, you have to say something pretty new,” Uzzi said.

Uzzi and Jones—along with their colleagues Satyam Mukherjee and Mike

Stringer—wrote an algorithm to evaluate the 17.9 million papers. By

examining how many different ideas each study contained, whether those

ideas had been mentioned together previously, and if the papers were popular

or ignored, their program could rate each paper’s novelty. Then they could

look to see if the most creative papers shared any traits.

The analysis told them that some creative papers were short; others were

long. Some were written by individuals; the majority were composed by

teams. Some studies were authored by researchers at the beginning of their

careers; others came from more senior faculty.

In other words, there were lots of different ways to write a creative study.

But almost all of the creative papers had at least one thing in common:

They were usually combinations of previously known ideas mixed together in

new ways. In fact, on average, 90 percent of what was in the most “creative”

manuscripts had already been published elsewhere—and had already been

picked over by thousands of other scientists. However, in the creative papers,

those conventional concepts were applied to questions in manners no one had

considered before. “Our analysis of 17.9 million papers spanning all

scientific fields suggests that science follows a nearly universal pattern,” Uzzi

and Jones wrote. “The highest-impact science is primarily grounded in

exceptionally conventional combinations of prior work yet simultaneously

features an intrusion of unusual combinations.” It was this combination of

ideas, rather than the ideas themselves, that typically made a paper so creative

and important.

If you consider some of the biggest intellectual innovations of the past half

century, you can see this dynamic at work. The field of behavioral

economics, which has remade how companies and governments operate,

emerged in the mid-1970s and ’80s when economists began applying long-held principles from psychology to economics, and asking questions like why

perfectly sensible people bought lottery tickets. Or, to cite other

juxtapositions of familiar ideas in novel ways, today’s Internet social

networking companies grew when software programmers borrowed public

health models that were originally developed to explain how viruses spread

and applying them to how friends share updates. Physicians can now map

complicated genetic sequences rapidly because researchers have transported

the math of Bayes’ rule into laboratories examining how genes evolve.

Fostering creativity by juxtaposing old ideas in original ways isn’t new.

Historians have noted that most of Thomas Edison’s inventions were the

result of importing ideas from one area of science into another. Edison and

his colleagues “used their knowledge of electromagnetic power from the

telegraph industry, where they first worked, to transfer old ideas [to the

industries of] lighting, telephone, phonograph, railway and mining,” two

Stanford professors wrote in 1997. Researchers have consistently found that

labs and companies encourage such combinations to spark creativity. A 1997

study of the consumer product design firm IDEO found that most of the

company’s biggest successes originated as “combinations of existing

knowledge from disparate industries.” IDEO’s designers created a top-selling

water bottle, for example, by mixing a standard water carafe with the leak-

proof nozzle of a shampoo container.

The power of combining old ideas in new ways also extends to finance,

where the prices of stock derivatives are calculated by mixing formulas

originally developed to describe the motion of dust particles with gambling

techniques. Modern bike helmets exist because a designer wondered if he

could take a boat’s hull, which can withstand nearly any collision, and design

it in the shape of a hat. It even reaches to parenting, where one of the most

popular baby books—Benjamin Spock’s The Common Sense Book of Baby

and Child Care, first published in 1946—combined Freudian psychotherapy

with traditional child-rearing techniques.

“A lot of the people we think of as exceptionally creative are essentially

intellectual middlemen,” said Uzzi. “They’ve learned how to transfer

knowledge between different industries or groups. They’ve seen a lot of

different people attack the same problems in different settings, and so they

know which kinds of ideas are more likely to work.”

Within sociology, these middlemen are often referred to as idea or

innovation brokers. In one study published in 2004, a sociologist named

Ronald Burt studied 673 managers at a large electronics company and found

that ideas that were most consistently ranked as “creative” came from people

who were particularly talented at taking concepts from one division of the

company and explaining them to employees in other departments. “People

connected across groups are more familiar with alternative ways of thinking

and behaving,” Burt wrote. “The between-group brokers are more likely to

express ideas, less likely to have ideas dismissed, and more likely to have

ideas evaluated as valuable.” They were more credible when they made

suggestions, Burt said, because they could say which ideas had already

succeeded somewhere else.

“This is not creativity born of genius,” Burt wrote. “It is creativity as an import-export business.”

What’s particularly interesting, however, is that there isn’t a specific

personality associated with being an innovation broker. Studies indicate that

almost anyone can become a broker—as long as they’re pushed the right

way.

Before rehearsals began for West Side Story, Robbins went to his colleagues

and said he was dissatisfied with the musical’s first scene. As initially

envisioned, the show opened in a traditional manner with the play’s

characters introducing themselves via dialogue that illustrated the plot’s

central tensions:

ACT 1

SCENE 1

A-rab, a teenager dressed in the uniform of his gang (THE JETS) comes

across the stage. Suddenly, two DARK-SKINNED BOYS plummet down from

a wall, crashing A-rab to the ground and attacking him. The attackers run off

and then several boys—dressed like A-rab—run on from the opposite side.

DIESEL

It’s A-rab!

BABY JOHN

He was hit hard.

ACTION

An’ right on our own turf!

Riff, the leader of THE JETS, enters

RIFF

Straight factualities, A-rab. Who did it?

ACTION

Those buggin’ Puerto Ricans!

DIESEL

We’re supposed to be the champeens in this area—

MOUTHPIECE

The PR’s ’re crowdin’ us like their lousy families ’re crowdin’

ours!

A-RAB

Let’s have some action, Riff.

ACTION

Let’s put it on the PRs!

BABY JOHN

A rumble!

RIFF

Whoa, buddy boys! Whadda you diapers know from rumbles?

The state of your ignorance is appalling. How do you think the

top brass go about a war?

BABY JOHN

Crack-O Jack-O!

RIFF

First—you dispatch scouts to the enemy leader to arrange a war

council. Then—

ACTION

Then you go!

RIFF

We oughta get Tony so we can take a vote.

ACTION

He always does what you say anyway. C’mon!

In this version of the opening scene, the audience has learned the basics of

the plot within moments of the curtain’s rise. They know there are two gangs

divided along ethnic lines. They know these gangs are engaged in an ongoing

battle. They know there is a hierarchy within each gang—Riff is clearly the

leader of the Jets—as well as a certain formality: A rumble can’t occur

without a meeting of the war council. The audience feels the energy and

tension ( Crack-O Jack-O! ) and they learn about another character, Tony, who

seems important. All in all, an effective opening.

Robbins discarded it. Too predictable, he said. Lazy and clichéd. Gangs

don’t just fight, they own territory, the same way a dancer owns a stage. The opening number of a musical about immigrants and the energy of New York

ought to feel ambitious and dangerous—it needed to make the audience feel

the same way Robbins, Bernstein, and Laurents had felt when they had come

up with this idea. They, the playwrights themselves, were strivers, Robbins

told them. They were Jews and outcasts, and this musical was an opportunity

to draw on their own experiences of exclusion and ambition and put their

own emotions on the stage.

“Robbins could be brutal,” said Amanda Vaill, Robbins’s biographer. “He

could sniff out creative complacency and force people to come up with

something newer and better than what everyone else settled for.” Robbins

was an innovation broker, and he forced everyone around him to become

brokers, as well.

This is what appeared onstage—and, later, on movie screens—in what

eventually became known as “The West Side Story Prologue.” It is one of the

most influential pieces of theater in the last sixty years:

The opening is musical: half-danced, half-mimed. It is primarily

a condensation of the growing rivalry between two teen-age

gangs, THE JETS and THE SHARKS, each of which has its own

prideful uniform. THE JETS—sideburned, long-haired—are

vital, restless, sardonic; The SHARKS are Puerto Ricans.

The stage opens with THE JETS on an asphalt court, snapping

their fingers as the orchestra plays. A handball strikes the fence

and the music stops. One of the boys, RIFF, indicates with a nod

to return the handball to its frightened owner. RIFF’s subordinate

complies and the music restarts.

THE JETS saunter across the court, and as the music swells,

they pirouette. They cry “yeah!” and begin a series of ronds de

jambe en l’air. They own this asphalt. They are poor and ignored

by society, but right now, they own this space.

A TEENAGER, the leader of THE SHARKS, appears. THE

JETS stop moving. Other SHARKS appear, and they start

snapping, and then whirl in a series of pirouettes of their own.

The SHARKS declare their own ownership of the stage.

The two gangs skirmish, contesting territory and dominance,

pantomiming threats and apologies, competing but never outright

fighting until dozens of SHARKS and JETS are flying across the

stage, almost but never touching as they taunt and challenge each

other. Then a SHARK trips a JET. The JET pushes his attacker.

A cymbal starts chiming and everyone is suddenly atop each

other, kicking and punching until a police whistle freezes them

and the gangs unite, pretending to be friends in front of

OFFICER KRUPKE.

For nine minutes, no dialogue is spoken. Everything is

communicated through dance.

The first time West Side Story was performed in 1957, the audience wasn’t

certain what to make of it. The actors were dressed in everyday clothes but

moved as if in a classical ballet. The dances were as formalized as Swan Lake, but described street fights, an attempted rape, and skirmishes with cops.

The music echoed the symphonic tritones of Wagner but also the rhythms of

Latin jazz. Throughout the musical, the actors switched between song and

dialogue interchangeably.

“The ground rules by which West Side Story is played are laid out in the opening number,” the theater historian Larry Stempel later wrote. “Before an

intelligible sentence has had a chance to be uttered, or a single phrase of

music sung, dance has conveyed the essential dramatic information.”

When the curtain went down on opening night, there was silence. The

audience had just seen a musical about rumbles and murder, songs describing

bigotry and prejudice and dances in which hoodlums moved like ballerinas

and actors sung slang words with the power of opera stars.

As everyone prepared to take their positions for the curtain call, “we ran to

our spaces and faced the audience holding hands. The curtain went up and we

looked at the audience, and they looked at us, and we looked at them, and I

thought, ‘Oh, dear Lord, it’s a bomb!’ ” said Carol Lawrence, who played the

original Maria. “And then, as if Jerry had choreographed it, they jumped to

their feet. I’d never heard people stamping and yelling, and by that time,

Lenny had worked his way backstage, and he came at the final curtain and

walked to me, put his arms around me and we wept.”

West Side Story went on to become one of the most popular and influential

musicals in history. It succeeded by mixing originality and convention to

create something new. It took old ideas and put them in novel settings so

gracefully that many people never realized they were watching the familiar

become unique. Robbins forced his colleagues to become brokers, to put their

own experiences onto the stage. “That was a real achievement,” Robbins later

said.

III.

The space assigned to the Frozen team for their daily meetings was large, airy, and comfortable. The walls were covered with sketches of castles and

ice caves, friendly-looking reindeer, a snow monster named “Marshmallow,”

and dozens of concepts for trolls. Each morning at nine A.M., the director,

Chris Buck, and his core team of writers and artists would assemble with

their coffee cups and to-do lists. The songwriters Bobby Lopez and Kristen

Anderson-Lopez would videoconference in from their home in Brooklyn.

Then everyone would start panicking about how little time was left.

Anxiety was particularly high the morning after the disastrous screening

and meeting with the story trust. From the beginning, the Frozen team had known they couldn’t simply retell an old fairy tale. They wanted to make a

movie that said something new. “It couldn’t just be that, at the end, a prince

gives someone a kiss and that’s the definition of true love,” Buck told me.

They wanted the film to say something bigger, about how girls don’t need to

be saved by Prince Charming, about how sisters can save themselves. The Frozen team wanted to turn the standard princess formula on its head. But that’s why they were in such trouble now.

“It was a really big ambition,” said Jennifer Lee, who joined the Frozen

team as a writer after working on another Disney film, Wreck-It Ralph. “And

it was particularly hard because every movie needs tension, but if the tension

in Frozen is between the sisters, how do you make them both likable? We tried a jealousy plotline, but it felt petty. We tried a revenge story, but Bobby

kept saying we needed an optimistic heroine instead of feuds. The story trust

was right: The movie needed to connect emotionally. But we didn’t know how to get there without falling into clichés.”

Everyone in the room was well aware they had only eighteen months to

finish the movie. Peter Del Vecho, the producer, asked them all to close their

eyes.

“We’ve tried a lot of different things,” he said. “It’s okay that we haven’t

found the answers yet. Every movie goes through this, and every wrong step

gets us closer to what works.

“Now, instead of focusing on all the things that aren’t working, I want you

to think about what could be right. I want you to envision your biggest hopes.

If we could do anything, what would you want to see on the screen?”

The group sat quietly for a few minutes. Then people opened their eyes

and started describing what had excited them about this project in the first

place. Some had been drawn to Frozen because it offered a chance to upend

the way girls are portrayed in films. Others said they were inspired by the

idea of a movie where two sisters come together.

“My sister and I fought a lot as kids,” Lee told the room. Her parents had

divorced when Lee was young. She had eventually moved to Manhattan

while her sister became a high school teacher in upstate New York. Then,

when Lee was in her early twenties, her boyfriend drowned in a boating

accident. Her sister had understood what she was going through at that

moment, had been there at a time of need. “There’s this moment when you

start to see your sibling as a person, instead of a reflection of yourself,” Lee

told the room. “I think that’s what’s been bothering me the most about this

script. If you have two sisters and one of them is the villain and one is a hero,

it doesn’t feel real. That doesn’t happen in real life. Siblings don’t grow apart

because one is good and one is bad. They grow apart because they’re both

messes and then they come together when they realize they need each other.

That’s what I want to show.”

Over the next month, the Frozen team focused on the relationship between

Anna and Elsa, the movie’s sisters. In particular, the filmmakers drew on

their own experiences to figure out how the siblings related. “We can always

find the right story when we start asking ourselves what feels true,” Del

Vecho told me. “The thing that holds us back is when we forget to use our

lives, what’s inside our heads, as raw material. That’s why the Disney

method is so powerful, because it pushes us to dig deeper and deeper until we

put ourselves on the screen.”

Jerry Robbins pushed his collaborators in West Side Story to draw on their

own experiences to become creative brokers. The Toyota Production System

unlocked employees’ capacity to suggest innovations by giving them more

control. The Disney system does something different. It forces people to use

their own emotions to write dialogue for cartoon characters, to infuse real

feelings into situations that, by definition, are unreal and fantastical. This method is worth studying because it suggests a way that anyone can become

an idea broker: by drawing on their own lives as creative fodder. We all have

a natural instinct to overlook our emotions as creative material. But a key part

of learning how to broker insights from one setting to another, to separate the

real from the clichéd, is paying more attention to how things make us feel.

“Creativity is just connecting things,” Apple cofounder Steve Jobs said in

1996. “When you ask creative people how they did something, they feel a

little guilty because they didn’t really do it, they just saw something. It

seemed obvious to them after a while. That’s because they were able to

connect experiences they’ve had and synthesize new things. And the reason

they were able to do that was that they’ve had more experiences or they have

thought more about their experiences than other people.” People become

creative brokers, in other words, when they learn to pay attention to how

things make them react and feel.

“Most people are too narrow in how they think about creativity,” Ed

Catmull, the president of Disney Animation, told me. “So we spend a huge

amount of time pushing people to go deeper, to look further inside

themselves, to find something that’s real and can be magical when it’s put

into the mouth of a character on a screen. We all carry the creative process inside us; we just need to be pushed to use it sometimes.”

This lesson isn’t limited to movies or Broadway. The Post-it note, for

instance, was invented by a chemical engineer who, frustrated by bookmarks

falling out of his church hymnal, decided to use a new adhesive to make them

stay put. Cellophane was developed by an exasperated chemist looking for a

way to protect tablecloths from wine spills. Infant formula was created, in

part, by an exhausted father who suspended vegetable nutrients in powder so

he could feed his crying child in the middle of the night. Those inventors

looked to their own lives as the raw materials for innovation. What’s notable

is that, in each case, they were often in an emotional state. We’re more likely

to recognize discoveries hidden in our own experiences when necessity

pushes us, when panic or frustrations cause us to throw old ideas into new

settings. Psychologists call this “creative desperation.” Not all creativity

relies on panic, of course. But research by the cognitive psychologist Gary

Klein indicates that roughly 20 percent of creative breakthroughs are

preceded by an anxiety akin to the stress that accompanied Frozen’ s development, or the pressures Robbins forced onto his West Side Story

collaborators. Effective brokers aren’t cool and collected. They’re often

worried and afraid.

A few months after the story trust meeting, the songwriters Bobby Lopez

and Kristen Anderson-Lopez were walking through Prospect Park in

Brooklyn, anxious about all the songs they needed to write, when Kristen

asked, “What would it feel like if you were Elsa?” As they walked past swingsets and joggers, Kristen and Bobby began discussing what they would

do if they were cursed and despised for something they couldn’t control.

“What if you tried to be good your entire life and it didn’t matter because people constantly judged you?” she asked.

Kristen knew this feeling. She had felt other parents’ looks when she let

their daughters eat ice cream instead of healthy snacks. She’d felt glances

when she and Bobby let their girls watch an iPad inside a restaurant because

they wanted a moment of peace. Perhaps Kristen wasn’t cursed with some

deadly power—but she knew what it felt like to be judged. It didn’t feel fair.

It wasn’t her fault that she wanted a career. It wasn’t her fault that she wanted

to be a good mom and be a good wife and a successful songwriter, and so, inevitably, that meant things like home-packed snacks and sparkling dinner

conversation—not to mention thank-you notes and exercise and replying to

emails—sometimes fell by the wayside. She didn’t want to apologize for not

being perfect. She didn’t think she needed to. And she didn’t think Elsa

should have to apologize for being flawed, either.

“Elsa has tried to do everything right, all her life,” Kristen said to Bobby.

“Now she’s being punished for being herself and the only way out is for her

to stop caring, to let it all go.”

As they walked, they began riffing, singing snippets of lyrics. What if they

wrote a song that started with a fairy-tale opening, Bobby suggested, like the

stories they read to their girls at night? Then Elsa could talk about the

pressures of being a good girl, said Kristen. She jumped up on a picnic bench.

“She could change into a woman,” she said. “That’s what growing up is,

letting go of the things you shouldn’t have to care about.”

She began singing to an audience of trees and trash cans, trying out lyrics

for Elsa to convey that she’s done being the good girl, that she doesn’t care

what anyone thinks anymore. Bobby was recording her impromptu song on his iPhone.

Kristen spread her arms.

Let it go, let it go.

That perfect girl is gone.

“I think you just figured out the chorus,” said Bobby.

Back in their apartment, they recorded a rough draft of the song in their

makeshift studio. In the background were the clinks of plates from the Greek

restaurant downstairs. The next day, they emailed it to Buck, Lee, and the rest

of the Frozen team. It was part power ballad and part classical aria, but infused with Kristen’s and Bobby’s frustrations and the emancipation they

felt when they let go of people’s expectations.

When the Frozen team gathered at the Disney headquarters the next

morning, they put “Let It Go” on the sound system. Chris Montan, Disney’s

head of music, slammed his hand on the table.

“That’s it,” he said. “That’s our song. That’s what this whole movie is

about!”

“I have to go rewrite the beginning of the movie,” said Lee.

“I was so happy,” Lee told me later. “So relieved. We had struggled for so

long, and then we heard ‘Let It Go’ and, finally, it felt like we had broken through. We could see the movie. We had been carrying the pieces in our

heads, but we needed someone to show us ourselves in the characters, to

make them familiar. ‘Let It Go’ made Elsa feel like one of us.”

IV.

Seven months later, the Frozen team had the first two-thirds of the film figured out. They knew how to make Anna and Elsa likable while driving

them apart to create the tension the film needed. They knew how to portray

the sisters as hopeful yet troubled. They had even transformed Olaf—the

f’ing snowman—into a lovable sidekick. Everything was falling into place.

Except they had no idea how to end the film.

“It was this huge puzzle,” said Andrew Millstein, president of Walt Disney

Animation Studios. “We tried everything. We knew we wanted Anna to

sacrifice herself to save Elsa. We knew we wanted the movie’s true love to

exist between the sisters. But we had to earn that ending. It had to feel real.”

When filmmakers get stuck at Disney, it’s referred to as spinning.

“Spinning occurs because you’re in a rut and can’t see your project from

different perspectives anymore,” said Ed Catmull. So much of the creative

process relies on achieving distance, on not becoming overly attached to your

creation. But the Frozen team had become so comfortable with their vision of

the sisters, so relieved to have figured out the movie’s basics, so grateful that

the creative desperation had lifted a bit, that they had lost their ability to see

other paths.

This problem is familiar to anyone who has worked on a long-term

creative project. As innovation brokers bring together different perspectives,

a creative energy is often released that is heightened by a small amount of

tension—such as the pressure that comes from deadlines, or clashes that

result when people from different backgrounds meld ideas, or the stresses of

collaborators’ pushing us to do more. And these “tensions can lead to greater

creativity, because all those differences trigger divergent thinking, the ability

to see something new when you are forced to look at an idea from someone

else’s point of view,” said Francesca Gino, who studies the psychology of

creativity at Harvard Business School. “But when that tension disappears,

when you solve the big problem and everyone starts seeing things the same

way, people also sometimes start thinking alike and forgetting all the options

they have.”

The Frozen team had solved almost all their problems. No one wanted to

lose all the progress they had already made. But they couldn’t figure out how

to end the film. “You start spinning when your flexibility drops,” said

Catmull. “You get so devoted to what you’ve already created. But you have

to be willing to kill your darlings to go forward. If you can’t let go of what

you’ve worked so hard to achieve, it ends up trapping you.”

So Disney’s executives made a change.

“We had to shake things up,” said Catmull. “We had to jolt everyone. So

we made Jenn Lee a second director.”

In one sense, this change should not have made a huge difference. Lee was

already the film’s writer. Naming her as a second director, with equal

authority to Chris Buck, didn’t alter who was participating in the daily

conversations. It didn’t add any new voices to meetings. And Lee herself was

the first to admit that she was as stuck as everyone else.

But, Disney executives hoped, disrupting the team’s dynamics just slightly

might be enough to stop everyone from spinning in place.

In the 1950s, a biologist named Joseph Connell began traveling between his

home in California and the rain forests and coral reefs of Australia in an

effort to understand why some parts of the world housed such incredible

biological diversity while other regions were so ecologically bland.

Connell had picked Australia for two reasons. First, he hated learning new

languages. Second, Australia’s forests and seascapes offered perfect

examples of biological diversity and homogeneity in close proximity. There

were long stretches of the Australian coast where hundreds of different kinds

of corals, fish, and sea vegetation lived in very close quarters. Less than a quarter mile away, in another portion of the sea that seemed essentially the

same, that diversity would plummet and you might find only one or two

kinds of coral and plants. Similarly, some pockets of Australia’s rain forests

contained dozens of different types of trees, lichen, mushrooms, and vines

flourishing side by side. But just a hundred yards away, that would dwindle

to just one species of each. Connell wanted to understand why nature’s

diversity—its capacity for creative origination—was distributed so unevenly.

His quest began in the Queensland rain forests: 12,600 square miles that

contain everything from forest canopies to eucalyptus groves, as well as the

Daintree tropical forest, where conifers and ferns grow right at the edge of the

sea, and the Eungella National Park, where trees are so dense that, at ground

level, it can be nearly lightless in the middle of the day. As Connell spent his

days walking under green canopies and hacking through thick foliage, he

found pockets of biodiversity that seemed to erupt out of nowhere. Then, just

a few minutes away, that medley would dwindle until just one or two species

remained. What explained this diversity and homogeneity?

Eventually, Connell began noticing something similar at the center of each

pocket of biodiversity: There was often evidence that a large tree had fallen.

Sometimes he would find a decaying trunk or a deep indentation in the soil.

In other verdant pockets, he found charred remains underneath the topsoil,

suggesting that a fire—perhaps caused by lightning—had blazed for a brief

but intense period before the rain forest’s dampness had extinguished the

flames.

These fallen trees and fires, Connell came to believe, played a crucial role

in allowing species to emerge. Why? Because at some point, there had been a

“gap in the forest where the trees had come down or had burned, and that gap

was big enough to let sunlight in and allow other species to compete,”

Connell told me. Retired now, he lives in Santa Barbara, but he remembers

the details from those trips. “By the time I found some areas, years had

passed since the fire or the tree fall, and so new trees had grown in their place

and were blocking out the sun again,” he said. “But there had been a time

when enough light had made it through that other species were able to claim

some territory. There had been some disturbance that had given new plants a

chance to compete.”

In those regions where trees hadn’t fallen or fires hadn’t occurred, one

species had become dominant and had crowded out all competitors. Put

differently, once a species solved the problem of survival, it pushed other

alternatives away. But if something altered the ecosystem just a little bit, then

biodiversity exploded.

“Only up to a point, though,” Connell told me. “If the gap in the forest was

too big, it had the opposite effect.” In those parts of the rain forest where

loggers had cleared entire fields, where a huge storm had wiped out whole sections of the forest, or where a fire had spread too far, there was much less

diversity, even decades later. If the trauma to the landscape was too great,

only the hardiest trees or vines could survive.

Next, Connell looked at reefs along the Australian coast. Here, too, he

found a similar pattern. In some places, there was a dizzying assortment of

coral and seaweed living in close proximity while, just a few minutes by boat

away, one species of fast-growing coral had dominated every square inch.

The difference, Connell found, was the frequency and intensity of waves and

storms. In those areas with high biodiversity, midsized waves and moderate

storms came through occasionally. Alternately, in places with no waves or

storms, just a handful of species dominated. Or, when waves were too

powerful or storms came through too often, they would scrub the reef clean.

It seemed as if nature’s creative capacities depended on some kind of

periodic disturbance—like a tree fall or an occasional storm—that

temporarily upset the natural environment. But the disturbance couldn’t be

too small or too big. It had to be just the right size. “Intermediate

disturbances are critical,” Connell told me.

Within biology, this has become known as the intermediate disturbance

hypothesis, which holds that “local species diversity is maximized when

ecological disturbance is neither too rare nor too frequent.” There are other

theories that explain diversification in different ways, but the intermediate

disturbance hypothesis has become a staple of biology.

“The idea is that every habitat is colonized by a variety of species, but over

time one or a few tend to win out,” said Steve Palumbi, the director of

Stanford’s marine station in Monterey, California. This is called “competitive

exclusion.” If there are no disturbances to the environment, then the strongest

species become so entrenched that nothing else can compete. Similarly, if

there are massive, frequent disturbances, only the hardiest species grow back.

But if there are intermediate disturbances, then numerous species bloom, and

nature’s creative capacities flourish.

Human creativity, of course, is different from biological diversity. It’s an

imprecise analogy to compare a falling tree in the Australian rain forest to a

change in management at Disney. Let’s play with the comparison for a

moment, though, because it offers a valuable lesson: When strong ideas take

root, they can sometimes crowd out competitors so thoroughly that

alternatives can’t prosper. So sometimes the best way to spark creativity is by

disturbing things just enough to let some light through.

“The thing I noticed, when I first became a director, was that the change was

subtle, but at the same time, very real,” Jennifer Lee told me. “When you’re a

writer, there’s certain things you know a film needs, but you’re just one

voice. You don’t want to seem defensive or presumptuous because other

people have just as many suggestions and your job is to integrate everyone’s

ideas.

“A director, though, is in charge. So when I became a director, I felt like I

had to listen even more closely to what everyone was saying because that was

my job now. And as I listened, I started picking up on things I hadn’t noticed

before.”

Some of the animators, for instance, were pushing to use the blizzard at the

end of the film as a metaphor for the characters’ internal turmoil. Others

thought they should withhold any foreshadowing, to make the ending a

surprise. As a writer, Lee had viewed those suggestions as devices. But now

she understood people were asking for clarity, for a direction in which every

choice—from the weather on-screen to choices about what is hidden or

revealed—reflected a core idea.

A few months after Lee’s promotion, Kristen Anderson-Lopez, the

songwriter, sent Lee an email. They had been speaking almost every day for a

year at this point. They talked at night and sent each other texts during the day. Their friendship didn’t end when Lee became a director. But it changed

a little bit.

Kristen was riding a school bus, chaperoning her second-grade daughter on

a class trip to the American Museum of Natural History in New York City,

when she pulled out her phone and typed a message to Lee.

“Yesterday I went to therapy,” she wrote. She and her therapist had

discussed the Frozen team members’ differing opinions about how the movie

should end. They had talked about Lee’s ascension to director. “I was

discussing dynamics and politics and power and all that bullshit and who do

you listen to and how do [you] start,” she typed. “Then she asked me, ‘Why do you do it?’

“And after parsing out the money and ego stuff, it all really comes down to

the fact that I have things I need to share about the human experience,”

Anderson-Lopez typed. “I want to take what I have learned or felt or

experienced and help people by sharing it.

“What is it about this frozen story that you, Bobby and I HAVE to say?”

Kristen asked. “For me, it has something to do with not getting frozen in

roles that are dictated by circumstances beyond our control.”

Lee herself was the perfect example of this. Lee had come to Disney as a

new film school graduate with little besides a young daughter and a fresh

divorce and student loans, and had quickly become a screenwriter at one of

the biggest studios on earth. Now she was the first female director in

Disney’s history. Kristen and Bobby were examples of people escaping their

circumstances, as well. They had fought for years to build the careers they

wanted, even when everyone said it was ridiculous to hope they could

support themselves by writing songs. Now, here they were, with hit

Broadway shows and the life they had always hoped for.

To earn Frozen’ s ending, Kristen said, they had to find a way to share that

sense of possibility with the audience.

“What is it for you?” Kristen typed.

Lee replied twenty-three minutes later. It was seven in the morning in Los

Angeles.

“I love your therapist,” she wrote, “and you.” All the different members of

the Frozen team had their own ideas for the movie. Everyone on the story trust had become locked into their own concept of how the film should end.

But none of them fit together perfectly, Lee felt.

However, Frozen could have only one ending. Someone had to make a

choice. And the right decision, Lee wrote, is that “fear destroys us, love heals

us. Anna’s journey should be about learning what love is; it’s that simple.” At

the end of the film, “when she sees her sister out on the fjords, she completes

her arc by the ultimate act of true love: sacrificing your needs for someone else’s. LOVE is a greater force than FEAR. Go with love.”

Becoming a director forced Lee to see things differently—and that small

jolt was enough to help her realize what the film needed, and to shift everyone else enough to agree with her.

Later that month, Lee sat down with John Lasseter.

“We need clarity,” she told him. “The core of this movie isn’t about good

and evil, because that doesn’t happen in real life. And this movie isn’t about

love versus hate. That’s not why sisters grow apart.

“This is a movie about love and fear. Anna is all about love, and Elsa is all

about fear. Anna has been abandoned, so she throws herself into the arms of

Prince Charming because she doesn’t know the difference between real love

and infatuation. She has to learn that love is about sacrifice. And Elsa has to

learn that you can’t be afraid of who you are, you can’t run away from your

own powers. You have to embrace your strengths.

“That’s what we need to do with the ending, show that love is stronger

than fear.”

“Say it again,” Lasseter told her.

Lee described her theory of love versus fear again, explaining how Olaf,

the snowman, embodies innocent love while Prince Hans demonstrates that

love without sacrifice isn’t really love at all; it’s narcissism.

“Say it again,” Lasseter said.

Lee said it again.

“Now, go tell the team,” said Lasseter.

In June 2013, a few months before the movie was set to open, the Frozen

team flew to a theater in Arizona to conduct a test screening. What appeared

on the screen was completely different from what had been shown in the

Disney screening room fifteen months earlier. Anna, the younger sister, was

now bubbly, optimistic, and lonely. Elsa was loving but scared of her own

powers and tortured by the memory of accidentally injuring her sister when

they were young. Elsa runs away to an ice castle, intending to live far from

humanity—but she inadvertently plunges her kingdom into an endless winter

and partially freezes Anna’s heart.

Anna begins searching for a prince in the hope that his true love’s kiss will

melt the ice in her chest. But the man she finds—Prince Hans—turns out to

be intent on taking the throne for himself. Prince Hans imprisons Elsa and

abandons the slowly freezing Anna, intent on killing both sisters so he can

seize the crown.

Elsa escapes from her cell and, near the end of the movie, is running across

the frozen fjords, fleeing the corrupt prince. Anna is growing weaker as the

ice inside her chest consumes her heart. A blizzard swirls around the sisters

and Hans as they all find one another on the frozen sea. Anna is almost dead

from the chill inside her body. Hans raises his sword, ready to slay Elsa and

put the throne within his reach. As Hans’s blade falls, however, Anna steps in

front of the blow. Her body turns to ice just as the sword descends, and it strikes her frozen body rather than her sister. By sacrificing herself, Anna has

saved Elsa—and this act of devotion, this genuine demonstration of true love,

finally melts Anna’s chest. She returns to life, and Elsa, released from the

anxiety that she’ll hurt the people she loves, can now direct her powers to

defeat the evil Hans. She knows now how to end the kingdom’s winter. The

sisters, united, are powerful enough to overcome their enemies and their self-

doubts. Hans is expelled, spring returns, and love defeats fear.

All the elements of a traditional Disney plot were included. There were

princesses and ball gowns, a handsome prince, a wisecracking sidekick, and a

stream of upbeat songs. But throughout the film, those elements had been

disturbed, just enough, to let something new and different emerge. Prince

Hans wasn’t charming—he was the villain. The princesses weren’t helpless;

instead, they saved each other. True love didn’t arrive in a rescue—rather, it

came from siblings learning to embrace their own strengths.

“When did this movie get so good?” Kristen Anderson-Lopez whispered to

Peter Del Vecho as the screening ended. Frozen would go on to win the Academy Award for Best Animated Feature of 2014. “Let It Go” would win

the Academy Award for best original song. The film would become the top-

grossing animated movie of all time.

Creativity can’t be reduced to a formula. At its core, it needs novelty,

surprise, and other elements that cannot be planned in advance to seem fresh

and new. There is no checklist that, if followed, delivers innovation on

demand.

But the creative process is different. We can create the conditions that help

creativity to flourish. We know, for example, that innovation becomes more

likely when old ideas are mixed in new ways. We know the odds of success go up when brokers—people with fresh, different perspectives, who have

seen ideas in a variety of settings—draw on the diversity within their heads.

We know that, sometimes, a little disturbance can help jolt us out of the ruts

that even the most creative thinkers fall into, as long as those shake-ups are

the right size.

If you want to become a broker and increase the productivity of your own

creative process, there are three things that can help: First, be sensitive to your own experiences. Pay attention to how things make you think and feel.

That’s how we distinguish clichés from true insights. As Steve Jobs put it, the

best designers are those who “have thought more about their experiences than

other people.” Similarly, the Disney process asks filmmakers to look inward,

to think about their own emotions and experiences until they find answers

that make imaginary characters come alive. Jerry Robbins pushed his West

Side Story collaborators to put their own aspirations and emotions on the stage. Look to your own life as creative fodder, and broker your own

experiences into the wider world.

Second, recognize that the panic and stress you feel as you try to create

isn’t a sign that everything is falling apart. Rather, it’s the condition that helps make us flexible enough to seize something new. Creative desperation

can be critical; anxiety is what often pushes us to see old ideas in new ways.

The path out of that turmoil is to look at what you know, to reinspect

conventions you’ve seen work and try to apply them to fresh problems. The

creative pain should be embraced.

Finally, remember that the relief accompanying a creative breakthrough,

while sweet, can also blind us to seeing alternatives. It is critical to maintain

some distance from what we create. Without self-criticism, without tension,

one idea can quickly crowd out competitors. But we can regain that critical

distance by forcing ourselves to critique what we’ve already done, by making

ourselves look at it from a completely different perspective, by changing the

power dynamics in the room or giving new authority to someone who didn’t

have it before. Disturbances are essential, and we retain clear eyes by

embracing destruction and upheaval, as long as we’re sensitive to making the

disturbance the right size.

There’s an idea that runs through these three lessons: The creative process

is, in fact, a process, something that can be broken down and explained.

That’s important, because it means that anyone can become more creative;

we can all become innovation brokers. We all have experiences and tools,

disturbances and tensions that can make us into brokers—if, that is, we’re

willing to embrace that desperation and upheaval and try to see our old ideas

in new ways.

“Creativity is just problem solving,” Ed Catmull told me. “Once people see

it as problem solving, it stops seeming like magic, because it’s not. Brokers

are just people who pay more attention to what problems look like and how

they’ve been solved before. People who are most creative are the ones who

have learned that feeling scared is a good sign. We just have to learn how to

trust ourselves enough to let the creativity out.”

ABSORBING DATA

Turning Information into Knowledge in Cincinnati’s Public Schools

Students were settling into their seats as the PA system crackled to life inside

South Avondale Elementary School.

“This is Principal Macon,” a voice said. “I am declaring a Hot Pencil Drill.

Please prepare yourselves, prepare your worksheets, and we will begin in

five, four, three, two…”

Two minutes and thirty-three seconds later, eight-year-old Dante Williams

slammed down his pencil, shot his hand into the air, and twitched impatiently

as the teacher scribbled his finish time at the top of the multiplication quiz.

Then Dante was out of his chair and flying through the door of his third-

grade classroom, arms pumping as he speed-walked down the hallway, his

worksheet creased in his fist.

Three years earlier, in 2007, when Dante entered kindergarten, South

Avondale had been ranked as one of the worst schools in Cincinnati—which,

given that the city had some of the lowest scores in the state, meant that the

school was among the worst in Ohio. That year, South Avondale’s students

had fared so poorly on their assessment exams that officials declared the

school an “academic emergency.” Just weeks before Dante had stepped onto

campus for the first time, a teenager had been murdered—one bullet to the

head, one in the back—right next to South Avondale during a football

tournament billed as a “Peace Bowl.” That crime, combined with the school’s

deep dysfunctions, poor academic scores, and a general sense that South

Avondale had problems too big for anyone to solve, had caused city officials

to ask if the board of education should close the campus altogether. The

question, however, was where would they send Dante and his classmates?

Nearby schools had scored only slightly better on assessment exams, and if

those classrooms were forced to absorb additional kids, they would likely fall

apart as well.

The community around South Avondale had been poor for decades. There

were race riots in the 1960s, and when the city’s factories started closing in

the ’70s, the area’s unemployment had skyrocketed. South Avondale

administrators saw students coming to school malnourished and with marks

of abuse. In the 1980s, the drug trade around the school exploded and never

really let up. At times, the violence got so bad that police would patrol the campus’s perimeter while classes were in session. “It could be a pretty scary

place,” said Yzvetta Macon, who was principal from 2009 to 2013. “Students

didn’t go to South Avondale unless there was no other place to go.”

One thing that wasn’t a problem, however, was resources. The city of

Cincinnati had poured millions of dollars into South Avondale. Local

companies such as Procter & Gamble built computer labs and paid for

tutoring and sports programs. In an effort to address the school’s

shortcomings, city officials spent nearly three times as many dollars on every

South Avondale student as they did on students in more affluent

communities, such as at the public Montessori campus across town. South

Avondale had energetic teachers, devoted librarians and tutors, reading

specialists, and guidance counselors who were trained in early childhood

education and prepared to help parents sign up for state and federal assistance

programs.

The school also used sophisticated software to track students’

performance. Administrators had embraced data collection and Cincinnati

Public Schools had created an individual website for every South Avondale

student—a dashboard of information that detailed kids’ attendance, test

scores, homework, and classroom participation—that was accessible to

parents and educators so they could track who was improving and who was

falling behind. The school’s faculty received a steady stream of memos and

spreadsheets showing how each pupil had fared over the past week, month,

and year. South Avondale, in fact, was at the forefront of educational Big

Data. “K–12 schools should have a clear strategy for developing a data-

driven culture,” read a U.S. Department of Education report that helped guide

Cincinnati’s efforts. By studying each student’s statistics closely enough,

educators believed they could deliver the specific kind of assistance each kid

needed most.

“Any idea or new program, we signed up for,” said Elizabeth Holtzapple,

director of research and evaluation at Cincinnati Public Schools. “We had

seen how data and analytics had turned around other districts, and we were

on board.”

The turnaround at South Avondale, however, was nowhere to be found.

Six years after the online dashboards were introduced, more than 90 percent

of South Avondale’s teachers admitted they hardly ever looked at them—or

used the data sent by the district, or read the memos they received each week.

In 2008, 63 percent of South Avondale’s third graders failed to meet the

state’s basic educational benchmarks.

So that year, Cincinnati decided to try something different. The district’s

top officials targeted South Avondale and fifteen other low-performing

campuses in what became known as the “Elementary Initiative,” or EI. The

effort was perhaps most notable for what it lacked: The schools were given

no additional funds or supplementary teachers; there were no new tutoring

sessions or after-school programs; the staff and student body at each campus

remained basically the same.

Instead, the EI focused on changing how teachers made decisions in their

classrooms. The reforms were built around the idea that data can be

transformative, but only if people know how to use it. To change students’

lives, educators had to understand how to transform all the spreadsheets and

statistics and online dashboards into insights and plans. They had to be forced

to interact with data until it influenced how they behaved.

By the time Dante entered the third grade, two years after the EI started,

the program was already so successful it was hailed by the White House as a

model of inner-city reform. South Avondale’s test scores went up so much

that the school earned an “excellent” rating from state officials. By the end of

Dante’s third-grade year, 80 percent of his classmates were reading at grade

level; 84 percent passed the state math exam. The school had quadrupled the

number of students meeting the state’s guidelines. “South Avondale

drastically improved student academic performance in the 2010–11 academic

year and changed the culture of the school,” a review by the school district read. The school’s transformation was so startling that researchers from

around the nation soon began traveling to Cincinnati to figure out what the

Elementary Initiative was doing right.

When those researchers visited South Avondale, teachers told them that the most important ingredient in the schools’ turnaround was data—the same

data, in fact, that the district had been collecting for years. Teachers said that

a “data-driven culture” had actually transformed how they made classroom

decisions.

When pressed, however, those teachers also said they rarely looked at the

online dashboards or memos or spreadsheets the central office sent around. In

fact, the EI was succeeding because teachers had been ordered to set aside

those slick data tools and fancy software—and were told instead to start

manipulating information by hand.

Each school, under orders from the central office, had established a “data

room”—in some cases, an empty conference room, in others, a large closet

that had previously contained cleaning supplies—where teachers had to

transcribe test scores onto index cards. They were told to draw graphs on

butcher paper that was taped to walls. They ran impromptu experiments—Do

test scores improve if kids are placed in smaller reading groups? What

happens when teachers trade off classes?—and then scribbled the results onto

whiteboards. Rather than simply receiving information, teachers were forced

to engage with it. The EI had worked because instead of passively absorbing

data, teachers made it “disfluent”—harder to process at first, but stickier once

it was really understood. By scribbling out statistics and testing

preconceptions, teachers had figured out how to use all the information they

were receiving. The Elementary Initiative, paradoxically, had made data more

cumbersome to absorb—but more useful. And from those index cards and

hand-drawn graphs, better classrooms emerged.

“Something special happened inside those data rooms,” said Macon, the

principal. South Avondale improved not because teachers had more

information but because they learned how to understand it. “With Google and

the Internet and all the information we have now, you can find answers to

almost anything in seconds,” said Macon. “But South Avondale shows

there’s a difference between finding an answer and understanding what it

means.”

II.

In the past two decades the amount of information embedded in our daily

lives has skyrocketed. There are smartphones that count our steps, websites

that track our spending, digital maps to plot our commutes, software that

watches our Web browsing, and apps to manage our schedules. We can

precisely measure how many calories we eat each day, how much our

cholesterol scores have improved each month, how many dollars we spent at

restaurants, and how many minutes were allocated to the gym. This

information can be incredibly powerful. If harnessed correctly, data can make

our days more productive, our diets healthier, our schools more effective, and

our lives less stressful.

Unfortunately, however, our ability to learn from information hasn’t

necessarily kept pace with its proliferation. Though we can track our

spending and cholesterol, we still often eat and spend in ways we know we

should avoid. Even simple uses of information—such as choosing a

restaurant or a new credit card—haven’t necessarily become more simple. To

find a good Chinese restaurant, is it better to consult Google, ask your

Facebook feed, call up a friend, or search your browser history to see where

you ordered from last time? To figure out which credit card to sign up for, should you consult an online guide? Call your bank? Open those envelopes

piling up on the dining room table?

In theory, the ongoing explosion in information should make the right

answers more obvious. In practice, though, being surrounded by data often

makes it harder to decide.

This inability to take advantage of data as it becomes more plentiful is

called “information blindness.” Just as snow blindness refers to people losing

the capacity to distinguish trees from hills under a blanket of powder, so

information blindness refers to our mind’s tendency to stop absorbing data

when there’s too much to take in.

One study of information blindness was published in 2004 when a group of

researchers at Columbia University tried to figure out why some people sign

up for 401(k) retirement plans while others don’t. They studied almost eight hundred thousand people, across hundreds of companies, who were offered

opportunities to enroll in 401(k) plans. For many workers, signing up for the

retirement plans should have been an easy choice: The 401(k)s offered large

tax savings and many of the companies in the study promised to match

employees’ contributions—in effect giving them free money. And at firms

where workers were offered information on two 401(k) options, 75 percent

enrolled. Employees at those companies told researchers that signing up

seemed obvious. They looked at the two brochures, picked the plan that

seemed most sensible, and then watched their retirement accounts grow fatter

over time.

At other companies, even as the number of plans to choose among

increased, sign-ups remained high. When workers were offered twenty-five

different kinds of plans, 72 percent of them enrolled.

But when employees received information on more than thirty plans,

something seemed to change. The amount of information people were

receiving became so overwhelming that workers stopped making good

choices—or, in some cases, any choice at all. At thirty-nine plans, only 65

percent of people signed up for 401(k) accounts. At sixty plans, participation

dropped to 53 percent. “Every ten funds added was associated with 1.5

percent to 2 percent drop in participation,” the researchers wrote in their 2004

study. Signing up for a 401(k) was still the right decision. But when

information became too plentiful, people put the brochures in a drawer and

never looked at them again.

“We’ve found this in dozens of settings,” said Martin Eppler, a professor at

the University of St. Gallen in Switzerland who studies information overload.

“The quality of people’s decisions generally gets better as they receive more

relevant information. But then their brain reaches a breaking point when the

data becomes too much. They start ignoring options or making bad choices or

stop interacting with the information completely.”

Information blindness occurs because of the way our brain’s capacity for

learning has evolved. Humans are exceptionally good at absorbing

information—as long as we can break data into a series of smaller and

smaller pieces. This process is known as “winnowing” or “scaffolding.”

Mental scaffolds are like file cabinets filled with folders that help us store and

access information when the need arises. If someone is handed a huge wine

list at a restaurant, for instance, they’ll typically have no problem making a selection because their brain will automatically place what they know about

wine into a scaffold of categories they can use to make binary decisions ( Do I

want a white or a red? White! ), and then finer subcategories ( Expensive or cheap? Cheap! ) until they confront a final comparison ( The six-dollar Chardonnay or the seven-dollar Sauvignon Blanc? ) that draws upon what

they have already learned about themselves ( I like Chardonnay! ). We do this

so quickly that, most of the time, we’re hardly aware it’s occurring.

“Our brains crave reducing things to two or three options,” said Eric

Johnson, a cognitive psychologist at Columbia University who studies

decision making. “So when we’re faced with a lot of information, we start

automatically arranging it into mental folders and subfolders and sub-

subfolders.”

This ability to digest large amounts of information by breaking it into

smaller pieces is how our brains turn information into knowledge. We learn

which facts or lessons to apply in a given situation by learning which folders

to consult. Experts are distinguished from novices, in part, by how many

folders they carry in their heads. An oenophile will look at a wine list and immediately rely on a vast system of folders—such as vintage and region—

that don’t occur to novices. The oenophile has learned how to organize

information ( Choose the year first, then look at the pricing) in ways that make it less overwhelming. So while a novice is flipping through pages, the

expert is already ignoring whole sections of the wine list.

So when we are presented with information on sixty different 401(k) plans

and no obvious way to start analyzing them, our brains pivot to a more binary

decision: Do I try to make sense of all this information, or just stick

everything in my drawer and ignore it?

One way to overcome information blindness is to force ourselves to

grapple with the data in front of us, to manipulate information by

transforming it into a sequence of questions to be answered or choices to be

made. This is sometimes referred to as “creating disfluency” because it relies

on doing a little bit of work: Instead of simply choosing the house wine, you

have to ask yourself a series of questions ( White or red? Expensive or

cheap? ). Instead of sticking all the 401(k) brochures into a drawer, you have

to contrast the plans’ various benefits and make a choice. It might seem like a

small effort at the time, but those tiny bits of labor are critical to avoiding information blindness. The process of creating disfluency can be as minor as

forcing ourselves to compare a few pages on a menu, or as big as building a

spreadsheet to calculate 401(k) payouts. But regardless of the intensity of the

effort, the underlying cognitive activity is the same: We are taking a mass of

information and forcing it through a procedure that makes it easier to digest.

“The important step seems to be performing some kind of operation,” said

Adam Alter, a professor at NYU who has studied disfluency. “If you make

people use a new word in a sentence, they’ll remember it longer. If you make

them write down a sentence with the word, they’ll start using it in

conversations.” When Alter conducts experiments, he sometimes gives

people instructions in a hard-to-read font because, as they struggle to make

out the words, they read the text more carefully. “The initial difficulty in

processing the text leads you to think more deeply about what you’re reading,

so you spend more time and energy making sense of it,” he said. When you

ask yourself a few questions about wine, or compare the fees on various

401(k) plans, the data becomes less monolithic and more like a series of

decisions. When information is made disfluent, we learn more.

In 1997, executives running the debt collection division of Chase Manhattan

Bank began wondering why a particular group of employees in Tampa,

Florida, were so much more successful than their peers at convincing people

to pay their credit card bills. Chase, at the time, was one of the largest credit

card issuers in the nation. As a result, it was also one of the largest debt collectors. It employed thousands of people, in offices all over the country,

who sat in cubicles all day and called debtor after debtor, to harass them

about overdue credit card bills.

Chase knew from internal surveys that debt collectors didn’t especially like

their jobs, and executives had grown accustomed to lackluster performance.

The company had tried to make the work easier by giving collectors tools to

help them convince debtors to pay. As each call occurred, for instance, the

computer in front of the debt collector served up information that would

assist in tailoring their pitch: It told them the debtor’s age, how frequently he

or she had paid off their balances, how many other credit cards they owned,

what conversational tactics had proven successful in the past. Employees

were sent to training sessions and given daily memos with charts and graphs

showing the success of various collection tactics.

But almost none of the employees, Chase found, paid much attention to the

information they received. No matter how many classes Chase provided or

memos they sent, collection rates never seemed to improve much. So

executives were pleasantly surprised when one team in Tampa started

collecting larger-than-usual amounts.

That group was overseen by a manager named Charlotte Fludd, an

evangelical minister in training with a passion for long skirts and Hooters

chicken wings, who had started out as a debt collector herself and had

worked her way through the ranks until she was overseeing a group

responsible for some of the hardest accounts, debtors who were 120 to 150

days overdue. Cardholders that far in arrears almost never paid off their

balances. However, Fludd’s group was collecting $1 million more per month

than any other collection team, even as they were going after some of the

most reticent debtors. What’s more, Fludd’s group reported some of Chase’s

highest employee satisfaction scores. Even the debtors they collected from, in

follow-up surveys, said they had appreciated how they had been treated.

Chase’s executives hoped Fludd might share her tactics with other

managers, and so they asked her to speak at the company’s regional meeting

at the Innisbrook Resort near Tampa. The title of her talk was “Optimizing

the Mosaix/Voicelink Autodialer System.” The room was packed.

“Can you tell us how you schedule your autodialer?” one manager asked.

“Carefully,” Fludd said. From 9:15 A.M. to 11:50 A.M., she explained, the collectors called people’s home numbers because they were more likely to

reach a wife taking care of the kids. Women were more likely to send in a

check, Fludd said.

“Then, from noon to one thirty, we call debtors’ work numbers,” Fludd

explained, “and we get a lot more men, but you can start the conversation by

saying, ‘Oh, I’m so glad I caught you on your way to lunch,’ like he’s real important and his schedule is busy, because that way, he’ll want to live up to

your expectations and he’ll promise to pay.

“Then at dinnertime, we call people we think are unmarried because

they’re more likely to be lonely and will want to talk, and then right after dinner, we call people whose balances have ballooned up and down, because

if they’ve already had a glass of wine and they’re relaxed, we can remind

them how good it feels to start paying the card off.”

Fludd had dozens of tips like these. She had advice on when to use a

comforting tone (if you hear soap operas in the background), when collectors

should reveal personal details (if the debtor mentions kids), and when to

deploy a stern approach (to anyone invoking religion).

The other managers didn’t know what to make of these suggestions. All of

them sounded perfectly logical—but they didn’t think their employees would

be able to use any of them. The average debt collector had just a high school

diploma. For many collectors, this was their first full-time job. Managers

mostly spent their time reminding employees to avoid sounding so wooden

on the phone. Their debt collectors weren’t going to be able to pay attention

to what television shows were playing in the background or listen for

religious references. No one was adept enough at analyzing debtors’ records

to figure out how to reach a housewife versus her husband. They just talked

to whoever picked up the phone. Chase might send the collectors memos

each morning, the company might give them computer screens of information

and provide them with classes—but managers knew almost no one actually

read those memos or looked at the screens or used what they learned in class.

Simply having a phone conversation with a stranger about a sensitive issue

like an overdue bill was overwhelming enough on its own. The average

collector couldn’t process additional information while conducting a call.

But when Fludd was asked why her employees were so effective at

processing more information than the average collector, she didn’t have any great answers. She couldn’t explain why her workers seemed to absorb so

much more. So after the conference, Chase hired the consulting firm Mitchell

Madison Group to examine her methods.

“How did you figure out that it’s better to call women in the morning?” a

consultant named Traci Entel asked her when Fludd was back in the office.

“Do you want me to show you my calendar?” said Fludd. The consultants

weren’t certain why she needed a calendar to explain her methods, but sure,

they said, let’s see the calendar. They expected Fludd to pull out a datebook

or journal. Instead, she dropped a binder onto the table. Then she wheeled

over a cart containing several more binders just like it.

“Okay,” Fludd said, leafing through pages filled with numbers and

scribbled notes. She found the sheet she was looking for. “One day, I came

up with this idea that it would be easier to collect from younger people,

because I figured they’re more eager to keep a good credit score,” she said.

Fludd explained that coming up with such theories was common on her

team. Employees would gather during lunch breaks or after work to kick

around ideas. Typically, these ideas didn’t make much sense—at least, at

first. In fact, the ideas were often somewhat nonsensical, such as the

suggestion that an irresponsible young person who is already behind on her

debts, for some reason, would suddenly care deeply about improving her

credit score. But that was okay. The point wasn’t to suggest a good idea. It was to generate an idea, any idea at all, and then test it.

Fludd looked at her calendar. “So the next day, we started calling people

between the ages of twenty-one and thirty-seven.” At the end of the shift,

employees reported no noticeable change in how much they had convinced

people to pay. So the following morning, Fludd changed one variable: She

told her employees to call people between the ages of twenty-six and thirty-

one. The collection rate improved slightly. The next day, they called a subset

of that group, cardholders between twenty-six and thirty-one with balances

between $3,000 and $6,000. Collection rates declined. The next day:

Cardholders with balances between $5,000 and $8,000. That led to the

highest collection rates of the week. In the evenings, before everyone left,

managers gathered to review the day’s results and speculate on why certain

efforts had succeeded or failed. They printed out logs and circled which calls

had gone particularly well. That was Fludd’s “calendar”: the printouts from each day with annotations and employees’ comments as well as notes

suggesting why certain tactics had worked so well.

With further testing, Fludd determined that her original theory regarding

young people was a dud. That, in itself, wasn’t surprising. Most of the

theories were duds initially. Employees had all kinds of hunches that didn’t

bear up under testing. But as each experiment unfolded, workers became

increasingly sensitive to patterns they hadn’t noticed before. They listened

more closely. They tracked how debtors would respond to various questions.

And eventually, a valuable insight would emerge—like, say, it’s better to call

people’s homes between 9:15 and 11:50 in the morning because the wife will

pick up and women are more likely to pay a family’s debts. Sometimes, the

debt collectors would develop instincts they couldn’t exactly put into words

but learned to heed nonetheless.

Then someone would propose a new theory or experiment and the process

would start all over again. “When you track every call and keep notes and

talk about what just happened with the person in the next cubicle, you start paying attention differently,” Fludd told me. “You learn to pick up on

things.”

To the consultants, this was an example of someone using the scientific

method to isolate and test variables. “Charlotte’s peers would generally

change multiple things at once,” wrote Niko Cantor, one of the consultants, in

a review of his findings. “Charlotte would only change one thing at a time.

Therefore she understood the causality better.”

But something else was going on, as well. It wasn’t just that Fludd was

isolating variables. Rather, by coming up with hypotheses and testing them,

Fludd’s team was heightening their sensitivity to the information flowing

past. In a sense, they were adding an element of disfluency to their work,

performing operations on the “data” generated during each conversation until

lessons were easier to absorb. The spreadsheets and memos that they received

each morning, the data that appeared on their screens, the noises they heard in

the background of phone calls—that became material for coming up with

new theories and running various experiments. Each phone call contained

tons of information that most collectors never registered. But Fludd’s

employees noticed it, because they were looking for clues to prove or

disprove theories. They were interacting with the data embodied in each conversation, turning it into something they could use.

This is how learning occurs. Information gets absorbed almost without our

noticing because we’re so engrossed with it. Fludd took the torrent of data

arriving each day and gave her team a method for placing it into folders that

made it easier to understand. She helped her employees do something with all

those memos they received and the conversations they were having—and, as

a result, it was easier for them to learn.

III.

Nancy Johnson became a teacher in Cincinnati because she didn’t know what

else to do with her life. It had taken her seven years to make it through

college, and after graduating, she’d become a flight attendant, married a pilot,

and then decided to settle down. In 1996, she started substituting in

Cincinnati’s public schools, hoping it would lead to a full-time job. She went

from classroom to classroom, guiding classes on everything from English to

biology, until she finally got a permanent offer as a fourth-grade teacher. On

her first day, the principal saw her and said, “So you’re Ms. Johnson.” He later admitted he had gotten a number of applications with the same last name

and wasn’t fully certain which one he had hired.

A few years later, in response to the federal government’s No Child Left

Behind law, Cincinnati began tracking students’ performances in reading and

math via standardized exams. Johnson was soon drowning in reports. Each

week, she received memos on students’ attendance and their progress in

vocabulary, math proficiency, reading, writing, literature comprehension, and

something called “cognitive manipulation,” as well as reviews of her

classroom’s proficiency, her teaching aptitude, and the school’s overall

scores. There was so much information that the city had hired a team of data

visualization experts to design the weekly memos the district delivered via

the Internet dashboards. The graphics team was talented. The charts Johnson

received were easy to read, and the Internet sites contained clear summaries

and color-coded trend lines.

But in those first few years, Johnson hardly looked at any of it. She was

supposed to use all that information in designing her curricula, but it made

her head hurt. “There were lots of memos and statistics, and I knew I was

supposed to be incorporating them into my classroom, but it all just kind of

washed over me,” she said. “It felt like there was this gap between all those

numbers and what I needed to know to become a better teacher.”

Her fourth-grade kids were mostly poor, and many were from single-parent

families. She was a good teacher, but her class still fared badly on assessment

exams. In 2007, the year before Cincinnati’s Elementary Initiative began, her students scored an average of 38 percent proficiency on the state’s reading

test.

Then, in 2008, the Elementary Initiative was launched. As part of that

reform, Johnson’s principal mandated that all teachers had to spend at least

two afternoons a month in the school’s new data room. Around a conference

table, teachers were forced to participate in exercises that made data

collection and statistical tabulation even more time consuming. At the start of

the semester, Johnson and her colleagues were told that as part of the EI, they

had to create an index card for every student in their class. Then, every other

Wednesday, Johnson would go into the data room and transcribe the past two

week’s test scores onto each student’s card, and then group all the cards into

color-coded piles—red, yellow, or green—based on whether students were

underperforming, meeting expectations, or exceeding their peers. As the

semester progressed, she also began grouping cards based on who was

improving or falling behind over time.

It was intensely boring. And, frankly, it seemed redundant because all this

information was already available on the students’ online dashboards.

Moreover, many of the people in that room had been teaching for decades.

They didn’t feel like they needed piles of cards to tell them what was going

on in their own classrooms. But an order was an order, and so they went into

the data room every other week. “The rule was that everyone had to actually

handle the cards, physically move them around,” Johnson said. “Everyone

hated it, at least at first.”

Then one day a third-grade teacher had an idea. Since he had to spend so

much time transcribing test scores, he decided to also note on each student’s

index cards which specific questions they had gotten wrong on that week’s

assessment exam. He convinced another third-grade teacher to do the same.

Next, they combined their cards and made piles by grouping students who

had made similar mistakes. When they were done, the piles showed a pattern:

A large number of students in one class had done well on pronoun use but

had stumbled at fractions; a large number of students in the other classroom

had scored the opposite way. The teachers traded curricula. Both classes’

scores went up.

The following week, someone else suggested dividing cards from multiple

classes into piles based on where students lived. Teachers started giving everyone from the same neighborhoods similar reading assignments. Test

scores ticked up. Students were doing their homework together on the bus

rides home.

Johnson began putting her students into work groups based on the piles of

cards she was making in the data room. Handling the index cards, she found,

gave her a more granular sense of each student’s strengths and weaknesses.

She found herself going into the data room a couple of times a week and

putting students’ cards into smaller and smaller piles, experimenting with

arranging them in different ways. She had felt, before, like she knew her class

pretty well. But this was a far deeper level of understanding. “When there are

twenty-five students and just one teacher, it’s easy to stop seeing them as

individuals,” she said. “I had always thought of them as a class. The data room made me focus on particular kids. It forced me to look at them one by

one and ask myself, what does this kid need?”

Midway through the year, some of Johnson’s colleagues noticed that a

small group of students in each class were struggling on math questions. It

wasn’t a big enough trend that any one teacher would have noticed on their

own, but inside the data room, the pattern became clear. That’s how the

school-wide Hot Pencil Drills started. Soon, students such as eight-year-old

Dante were spending each morning filling out multiplication tables as fast as

they could, and then speed-walking to the main office to have the fastest test

takers’ names read over the PA system. Within twelve weeks, the school’s

math scores were up by 9 percent.

Eight months after the Elementary Initiative was launched, Johnson’s class

sat for their yearly assessment exam. By that point, she was visiting the data

room all the time. She and her colleagues had created dozens of piles of index

cards. They had tested various lesson plans and were tracking results on long

strips of paper torn from rolls and taped to the walls. Columns of numbers

and scribbled notes filled the data room.

The test results came back six weeks later. Johnson’s students scored an

average of 72 percent, almost double her class’s result the previous year. The

school’s overall scores had more than doubled. In 2009, Johnson became a

teacher coach, traveling to other schools in Cincinnati to help instructors

learn to use their own data rooms. In 2010, she was selected by her peers as

Cincinnati’s Educator of the Year.

IV.

Delia Morris was a high school freshman when Cincinnati launched the

Elementary Initiative, and so she was too old to benefit from the reforms

occurring at places such as South Avondale. And by the time city officials

began expanding the program, it seemed too late for her in other respects.

Delia’s father was fired that year from his job as a security guard at a local

grocery store. Then he got into a fight with their landlord. Not long after, Delia came home to find an orange sticker and a padlock on the apartment’s

front door and everything she and her seven siblings owned stuffed into black

garbage bags in the hall. The family was able to stay with people from their

church for a while, and then crowded into the apartments of family friends,

but from that point on, they moved every few months.

Delia was a good kid and a hard worker. Her teachers had noticed she was

unusually smart—gifted enough, they felt, to make it out of Cincinnati’s bad

neighborhoods and into college. But that didn’t mean escape was guaranteed.

Every year there were a handful of students who seemed destined for

something better until poverty pulled them back down. Delia’s teachers were

hopeful but not naïve. They knew that even for gifted students, a better life was sometimes out of reach. Delia knew that, as well. She worried that even a

whiff of homelessness would change how her teachers perceived her, so she

didn’t tell anyone what was going on at home. “Going to school was the best

part of each day,” she told me. “I didn’t want to ruin that.”

When Delia started her sophomore year at Western Hills High in 2009, the

city began expanding its education reforms to high schools. However, some

early results among older students proved disappointing. Teachers

complained that innovations such as the data rooms were a start but not a

solution. Older students were already too hardened, their teachers said; their

timelines for intervention were too short. To change kids’ lives, they argued,

schools needed to help students get better at making the kinds of decisions

that offered few opportunities for experimentation. They needed to help

teenagers decide between going to college or getting a job; whether to

terminate a pregnancy or get married; how to pick among family members when everyone needs your help.

So the school district shifted its focus for high school students. Alongside

the Elementary Initiative, the district began creating engineering classes

within Western Hills High and other schools in partnership with local

universities and the National Science Foundation. The goal was “a

multidisciplinary approach to education that encourages students to leverage

the technology they use in their daily lives to solve real world problems,” a

summary of the program read. Ninety percent of students at Western Hills

lived below the poverty line. Their classrooms had peeling linoleum floors

and cracked chalkboards. “Leveraging technology” was not what most

students worried about. Delia signed up for an engineering course taught by

Deon Edwards, whose introductory remarks reflected the reality that

surrounded all of them.

“We’re going to learn how to think like scientists,” he told his class.

“We’re going to leave your parents and friends behind and learn to make

choices with clear eyes, without the baggage everyone wants to put on you.

And if any of you didn’t have anything to eat this morning, I keep energy

bars in my desk and you should help yourself. There’s nothing wrong with

saying you’re hungry.”

The real focus of Mr. Edwards’s class was a system for decision making

known as “the engineering design process,” which forced students to define

their dilemmas, collect data, brainstorm solutions, debate alternative

approaches, and conduct iterative experiments. “The engineering design

process is a series of steps that engineers follow when they are trying to solve

a problem and design a solution for something; it is a methodical approach to

problem solving,” one teacher’s manual explained. The engineering design

process was built around the idea that many problems that seem

overwhelming at first can be broken into smaller pieces, and then solutions

tested, again and again, until an insight emerges. The process asked students

to define precisely the dilemma they wanted to solve, then to conduct

research and come up with multiple solutions, and then conduct tests,

measure results, and repeat the procedure until an answer was found. It told

them to make problems more manageable until they fit into scaffolds and

mental folders that were easier to carry around.

The class’s first big assignment was to design an electric car. For weeks,

students in Mr. Edwards’s class arranged themselves into teams and followed

flowcharts detailing each engineering design process step. The classroom had

few materials to work with. But that was okay, because the real point of the

exercise was to learn how to squeeze information from your environment, no

matter where it comes from. Soon students were visiting car dealerships,

going to mechanics’ shops, and raiding aluminum cans from recycling bins to

make battery-testing kits from instructions they had found online. “My first

job is to teach them to slow down a little bit,” Deon Edwards told me. “These

are kids who solve problems all day long. They deal with missing parents and

violent boyfriends and classmates on drugs. Everything they experience says

they have to choose quickly. I just want to show them that if you have a

system for making choices, you can afford to slow down and think.”

Midway through the semester, after the class had completed their car

designs and moved on to building marble sorters, Delia’s twenty-one-year-

old sister had a baby. The child’s father was out of the picture and Delia’s sister, exhausted, begged her to babysit in the afternoons. It felt like a request

that was impossible for Delia to refuse. The right decision, Delia’s dad told her, was obvious. This was family.

So one day in Mr. Edwards’s class, Delia pulled the engineering flowchart

from her binder and, with her group, put her dilemma through the design

process’s steps. If she babysat, what would happen? One of the first tasks in

engineering design is finding data, so Delia began making a list of

experiences that seemed germane. Another sister, Delia told the group, had

taken an after-school job a few years earlier and the family had quickly come

to rely on that paycheck, making it impossible for her to quit and putting her

hopes of community college on hold. If Delia started babysitting, something similar would happen, she suspected. That was data point one.

Then Delia began writing out what her schedule might look like if she was

responsible for an infant every afternoon. School from 8:30 to 3:30.

Babysitting from 3:30 to 7:30. Homework from 7:30 to 10:00. She would be

tired after watching her nephew and would probably end up watching

television instead of doing her math or studying for a test. She would become

resentful and make bad choices on the weekends. Data point two.

As her group walked through the flowchart, they broke her dilemma into

smaller pieces and brainstormed solutions and role-played conversations

while the rest of the class discussed how to separate colored marbles from

clear ones. Eventually, an answer emerged: Babysitting seemed like a minor

sacrifice, but the evidence suggested it wasn’t minor at all. Delia prepared a

memo for her father listing the steps she had gone through. She wouldn’t be

able to do it, she told her dad.

Psychologists say learning how to make decisions this way is important,

particularly for young people, because it makes it easier for them to learn

from their experiences and to see choices from different perspectives. This is

a form of disfluency that allows us to evaluate our own lives more

objectively, to offset the emotions and biases that might otherwise blind us to

the lessons embedded in our pasts. When the animators behind Frozen were

trying to figure out their film, the Disney system pushed them to look to their

own lives as creative fodder. But it’s not just creative material we can mine

from our experiences—we can find data in our pasts, as well. We all have a

natural tendency to ignore the information contained in our previous

decisions, to forget that we’ve already conducted thousands of experiments

each time we made a choice. We’re often too close to our own experiences to

see how to break that data into smaller bits.

But systems such as the engineering design process—which forces us to

search for information and brainstorm potential solutions, to look for

different kinds of insights and test various ideas—help us achieve disfluency

by putting the past in a new frame of reference. It subverts our brain’s

craving for binary choices —Should I help my sister or let my family down? —

by learning to reframe decisions in new ways.

One important study of the power of such decision-making frames was

published in 1984, after a researcher from Northwestern University asked a group of participants to list reasons why they should buy a VCR based on

their own experiences. Volunteers generated dozens of justifications for such

a purchase. Some said they felt a VCR would provide entertainment. Others

saw it as an investment in their education or a way for their families to spend

time together. Then those same volunteers were asked to generate reasons not

to buy a VCR. They struggled to come up with arguments against the

expenditure. The vast majority said they were likely to buy one sometime

soon.

Next, the researcher asked a new group of volunteers to come up with a list

of reasons against purchasing a VCR. No problem, they replied. Some said watching television distracted them from their families. Others said that

movies were mindless, and they didn’t need the temptation. When those same

people were then asked to list reasons for buying a VCR, they had trouble coming up with convincing reasons to make the purchase and said they were

unlikely to ever buy one.

What interested the researcher was how much each group struggled to

adopt an opposing viewpoint once they had an initial frame for making a

decision. The two groups were demographically similar. They should have

been equally interested in buying a VCR. At the very least, they should have

generated equal numbers of reasons to buy or spurn the machines. But once a

participant grabbed on to a decision-making frame— This is an investment in

my education versus This is a distraction from my family—they found it hard to envision the choice in a different way. A VCR was either a tool for

learning or a time-wasting distraction, based on how the question was

framed. Similar results have been found in dozens of other experiments in

which people were presented with decisions ranging from the vital, such as

end-of-life choices, to the costly, such as buying a car. Once a frame is

established, that context is hard to dislodge.

Frames can be uprooted, however, if we force ourselves to seek fresh

vantage points. When Delia put her babysitting dilemma through Mr.

Edwards’s flowcharts, it introduced just enough disfluency to disrupt the

frame she had initially assumed she should use. When she went home and

walked her father through her logic, it shifted his frame, as well. She couldn’t

care for her nephew, she told him, because Mr. Edwards’s Robotics Club

required her to stay at school until six o’clock on Tuesdays and Thursdays,

and that club was her path to college. What’s more, the other days of the

week she needed to get her homework done in the library before coming

home because otherwise it wouldn’t get finished amid the family’s chaos and

noise. She reframed the decision as a choice between helping her family now,

or succeeding at school and helping in other, more important ways down the

road. Her father agreed. They would find another babysitter. Delia needed to

stay in school.

“Our brain wants to find a simple frame and stick with it, the same way it

wants to make a binary decision,” Eric Johnson, the Columbia psychologist,

told me. “That’s why teenagers get stuck thinking about breaking up with a

boyfriend as, ‘Do I love him or not?’ rather than ‘Do I want to be in a

relationship, or do I want to be able to leave for college?’ Or why, when

you’re buying a car, you start thinking, ‘Do I want the power windows or the

GPS?’ rather than ‘Am I sure I can afford this car?’

“But when we teach people a process for reframing choices, when we give

them a series of steps that causes a decision to seem a little bit different than

before,” said Johnson, “it helps them take more control of what’s going on

inside their heads.”

One of the best ways to help people cast experiences in a new light is to

provide a formal decision-making system—such as a flowchart, a prescribed

series of questions, or the engineering design process—that denies our brains

the easy options we crave. “Systems teach us how to force ourselves to make

questions look unfamiliar,” said Johnson. “It’s a way to see alternatives.”

As Delia moved into her senior year at Western Hills High, her home life

became increasingly chaotic. Her sister was there, raising the baby. Another

sister had dropped out of school. The family would find a place to live and then something would happen—another lost job or a neighbor who

complained about too many people in a one-bedroom apartment—and they

would have to move again. In her senior year, Delia’s family finally found a

long-term rental, but it didn’t have heat and, sometimes, when there wasn’t

money to pay the bill, the electricity went off.

Her teachers, by then, had figured out what was going on, and had seen

how hard Delia was working. She was getting straight As. They committed themselves to helping her however they could. When Delia needed to do

laundry, her English teacher, Ms. Thole, would invite her over for the

afternoon. When Delia seemed exhausted, Mr. Edwards would let her stay

late in his classroom and nap, her head on the desk, as he graded exams. They

saw her potential. They hoped, with a little help, she could make it to college.

Mr. Edwards, in particular, was a constant in Delia’s life. He introduced

her to the school’s guidance counselor and helped her apply for scholarships.

He edited her college applications and made sure they were sent in on time.

When Delia had a problem with her friends, when she was fighting with a

boyfriend or sparring with her dad, when it seemed like she had too much

homework and too little time—whenever it seemed like life was

overwhelming—she pulled out Mr. Edwards’s flowchart and put her troubles

through the engineering design process. It was calming. It helped her think of

solutions.

In the spring of Delia’s senior year, letters began arriving from scholarship

committees. She won the $10,000 Nordstrom Scholarship, then a Rotary

prize, then the University of Cincinnati’s minority scholar’s grant. The

envelopes kept coming. Seventeen scholarships in all. She was the class

valedictorian and was voted most likely to succeed. The night before

graduation, she slept at Ms. Thole’s house so she could take a hot shower and

curl her hair before the ceremony. In the fall, she enrolled at the University of

Cincinnati.

“College is a lot harder than I expected,” Delia told me. She’s a sophomore

now, majoring in information technology. She’s often the only girl in her

classes and the only black student. The university has tried to help students like Delia, first-generation college attendees, by creating a program named

“Gen-1” that provides mentors, tutors, mandatory study sessions, and

guidance counseling. Gen-1 participants all live in the same dorm freshman

year and sign a seven-page contract in which they promise to abide by a

curfew, respect evening quiet hours, and participate in study halls. The idea is

to help them get some distance from where they grew up, to see themselves

in a new context.

“There’s still drama at home,” Delia said. But when things feel

overwhelming, Delia thinks about Mr. Edwards’s class. Any problem can be

worked through, step-by-step. “If I take something that’s bothering me and

make it into smaller pieces, it feels like something I can think about without

getting upset,” she said.

“I’ve been through a lot. But I feel like, as long as I’ve got a system for getting outside my head, I can learn from it. Anything that’s happened to me

can be a lesson, if I think about it right.”

The people who are most successful at learning—those who are able to digest

the data surrounding them, who absorb insights embedded in their

experiences and take advantage of information flowing past—are the ones

who know how to use disfluency to their advantage. They transform what life

throws at them, rather than just taking it as it comes. They know the best

lessons are those that force us to do something and to manipulate

information. They take data and transform it into experiments whenever they

can. Whether we use the engineering design process or test an idea at work or

simply talk through a concept with a friend, by making information more

disfluent, we paradoxically make it easier to understand.

In one study published in 2014, researchers from Princeton and UCLA

examined the relationship between learning and disfluency by looking at the

difference between students who took notes by hand while watching a lecture

and those who used laptops. Recording a speaker’s comments via longhand is

both harder and less efficient than typing on a keyboard. Fingers cramp.

Writing is slower than typing, and so you can’t record as many words.

Students who use laptops, in contrast, spend less time actively working

during a lecture, and yet they still collect about twice as many notes as their

handwriting peers. Put differently, writing is more disfluent than typing,

because it requires more labor and captures fewer verbatim phrases.

When the researchers looked at the test scores of those two groups,

however, they found that the hand writers scored twice as well as the typists

in remembering what a lecturer said. The scientists, at first, were skeptical.

Maybe the hand writers were spending more time studying after class? They

conducted a second experiment, but this time they put the laptop users and

the hand writers in the same lecture and then took away their notes as soon as

it was over, so students couldn’t study on their own. A week later, they

brought everyone back. Once again, those who took notes by hand scored

better on a test of the lecture’s content. No matter what constraints were

placed on the groups, the students who forced themselves to use a more

cumbersome note-taking method—who forced disfluency into how they

processed information—learned more.

In our own lives, the same lesson applies: When we encounter new

information and want to learn from it, we should force ourselves to do

something with the data. It’s not enough for your bathroom scale to send daily updates to an app on your phone. If you want to lose weight, force

yourself to plot those measurements on graph paper and you’ll be more likely

to choose a salad over a hamburger at lunch. If you read a book filled with new ideas, force yourself to put it down and explain the concepts to someone

sitting next to you and you’ll be more likely to apply them in your life. When

you find a new piece of information, force yourself to engage with it, to use it

in an experiment or describe it to a friend—and then you will start building

the mental folders that are at the core of learning.

Every choice we make in life is an experiment. Every day offers fresh

opportunities to find better decision-making frames. We live in a time when

data is more plentiful, cheaper to analyze, and easier to translate into action

than ever before. Smartphones, websites, digital databases, and apps put

information at our fingertips. But it only becomes useful if we know how to

make sense of it.

In 2013, Dante Williams graduated from the fifth grade at South Avondale

Elementary. On his last day of school, he went to a party at the same

playground where the teenager had been murdered at the Peace Bowl six

years before. There were balloons and a bouncy castle, a cotton candy

machine and a DJ. South Avondale was still located inside one of

Cincinnati’s poorest areas. There were still drugs and boarded-up homes near

the campus. But 86 percent of the school’s students exceeded the state’s

education standards that year. The previous year, 91 percent of students had

tested above the state’s standards. There was a list of kids from outside the district waiting to transfer in.

No school changes because of just one program, of course, just as no

student succeeds because of one class or one teacher. Both Dante and Delia, as well as South Avondale and Western Hills High, changed because multiple

forces came together at once. There were dedicated teachers and a renewed

sense of purpose among administrators. There were focused principals and

parents supporting the reforms. But dedication and purpose only succeed

when we know how to direct them. The data rooms that turned information

into real knowledge, the teachers who learned how to see their students as

individuals with different needs and strengths: That’s how Cincinnati’s public

schools shifted.

At the graduation ceremony, as Dante walked across the makeshift stage,

his family cheered. Like all diplomas handed out that day, his contained a

blank space. There was one last thing, the principal told him. No one was

allowed to finish elementary school without doing a final bit of work. Dante

had to transform this diploma and make it his own. She handed Dante a pen.

He filled in the space with his name.

APPENDIX

A Reader’s Guide to Using These Ideas

A few months after I reached out to Atul Gawande—the author and physician

from the introduction who helped spark my interest in the science of

productivity—I began reporting this book. For almost two years, I conducted

interviews with experts, read piles of scientific papers, and tracked down case

studies. At some point, I began to imagine that I had become something of a

productivity expert myself. When it came time to start writing, I figured,

translating all those ideas onto paper would be relatively easy. The words

would fly from my fingertips.

That is not what happened.

Some days I would sit at my desk and spend hours jumping from website

to website looking for new studies to read, then organize my notes. I would

get onto airplanes, my carry-on bag stuffed with scientific papers I intended

to read, and spend the flight returning emails, writing to-do lists, and ignoring

the big, important tasks I needed to complete.

I had a goal in mind—I wanted to write a book about how we can apply

these discoveries in productivity to our own lives—but it seemed so far off,

so overwhelming, that I kept focusing on easier-to-accomplish objectives. A

few months went by, and all I had to show for it was a series of outlines, but

no chapters.

“I feel like a failure,” I wrote my editor during one particularly dispiriting

moment. “I don’t know what I’m doing wrong.”

When he wrote back, he pointed out the obvious: Maybe I needed to take

what I was learning from the experts and apply it to my own life. I had to live

by the principles described in this book.

MOTIVATION

One of my hardest challenges, for instance, concerned my motivation, which

seemed to flag at exactly the wrong times. While I was working on this book,

I was still also a reporter at The New York Times. What’s more, I was out promoting my previous book, and trying to be a good father and husband. In

other words, I was exhausted. After a long day at the Times, I would come home and need to start typing up notes, or draft a chapter, or help put my kids

to bed, or clean up the dishes, or reply to emails—and I’d find that self-

motivation was in short supply. Emails, in particular, were a small form of

daily torture. My in-box was constantly stuffed with questions from

colleagues, queries from other authors, correspondence from researchers

whom I hoped to interview, and other miscellaneous questions that required a

thoughtful response.

However, all I wanted to do was watch TV.

As I struggled each night to find the drive to reply to emails, I began

thinking about the key insight from chapter one and the ideas that Gen.

Charles Krulak used to redesign Marine Corps boot camp by strengthening

recruits’ internal locus of control:

• Motivation becomes easier when we transform a chore into a choice.

Doing so gives us a sense of control.

On any given day, for instance, I had—let’s say—at least about fifty

emails that needed responses. Every evening, I would resolve to sit down at

my computer and deal with them as soon as dinner was over. And, every

evening, I would find ways to procrastinate—by reading the kids one more

story, or cleaning up the living room, or checking Facebook—in order to

avoid the drudgery of typing response after response. Or, I would sweep

through my in-box, hitting the reply key again and again, and then,

confronted with a screen full of responses awaiting my words, feel

overwhelmed.

General Krulak had told me something that stuck with me: “Most recruits

don’t know how to force themselves to start something hard. But if we can

train them to take the first step by doing something that makes them feel in

charge, it’s easier to keep going.”

I realized that Krulak’s insight could help me motivate. And so one night,

after putting the kids to bed, I sat at my laptop and hit the reply button, creating a series of responses. Then, as fast as I could, I typed a sentence within each email—any sentence at all—to get me going. For instance, a coworker sent a note asking if I could join him at a meeting. I had put off replying because I didn’t want to attend. I knew the meeting would be long

and boring. But I couldn’t completely ignore him. So I wrote one sentence in

my response:

I can attend, but I’ll need to leave after twenty minutes.

I went through two dozen replies just like that, writing a short sentence in

each one, hardly thinking about it. And then, I went back and filled in the rest

of each email:

Hey Jim,

Sure, I can attend, but I’ll need to leave after twenty minutes.

I hope that’s okay.

Thanks,

Charles

I noticed two things: First, it was much easier to reply to an email once I

had at least one sentence on the screen. Second, and more important, it was

easier to get motivated when that first sentence was something that made me

feel in control. When I told Jim that I could only stay for twenty minutes, it

reminded me that I didn’t have to commit to his project if I didn’t want to.

When I drafted a reply to someone asking me to come speak at a conference,

I began by typing:

I would like to leave on Tuesday and be back in New York by

Thursday night.

Which reinforced that I was in control of whether I attended or not.

Put differently, as I typed a series of short replies, each reminded me that I

was in control of the choices being put before me. (As a psychologist might

say, I used those sentences to amplify my internal locus of control.) Within

thirty-five minutes, I had cleared out my in-box.

But what about other kinds of procrastination? What about when you’re

confronting a bigger, more involved task, like writing a long memo or having

a hard conversation with a colleague? What if there isn’t an easy way to

prove to yourself that you’re in control? For those, I remember the other key

lesson from the motivation chapter:

• Self-motivation becomes easier when we see our choices as affirmations

of our deeper values and goals.

That’s why Marine Corps recruits ask each other “why”: “Why are you

climbing this mountain?”, “Why are you missing the birth of your

daughter?”, “Why are you cleaning a mess hall, or doing push-ups, or

running onto a battlefield when there are safer, easier ways to live?” Forcing

ourselves to explain why we are doing something helps us remember that this

chore is a step along a longer path, and that by choosing to take that journey,

we are getting closer to more meaningful objectives.

To motivate myself to read studies on airplanes, for instance, I began

writing at the top of each manuscript why it was important for me to get that

task done. When I pulled a study out of my bag, then, it became a little easier

to dive in. Something as simple as jotting down a couple of reasons why I am

doing something makes it much simpler to start.

Motivation is triggered by making choices that demonstrate (to ourselves)

that we are in control—and that we are moving toward goals that are

meaningful. It’s that feeling of self-determination that gets us going.

TO GENERATE MOTIVATION

• Make a choice that puts you in control. If you’re replying to emails,

write an initial sentence that expresses an opinion or decision. If you

need to have a hard conversation, decide where it will occur ahead of

time. The specific choice itself matters less in sparking motivation than

the assertion of control.

• Figure out how this task is connected to something you care about.

Explain to yourself why this chore will help you get closer to a

meaningful goal. Explain why this matters—and then, you’ll find it

easier to start.

GOAL SETTING

Simply figuring out how to motivate myself wasn’t always enough, however.

Writing a book is a big goal—too big, in many ways, to grasp the entirety of

it at first. In trying to figure out how to wrap my head around the objective, I

was helped enormously by the reporting I conducted regarding goal setting.

The big takeaway was that I needed two kinds of aims:

• I needed a stretch goal, something to spark big ambitions.

• AND I needed a SMART goal, to help me form a concrete plan.

One of the most effective ways to formulate both objectives, experts told

me, is through a specific kind of to-do list. I needed to write out my goals—

but in a way that forced me to identify my stretch objectives and my SMART

aims. So I began writing to-do lists, and at the top of each one, I wrote my overarching ambition, what I was working toward in the long term. (That

helped me avoid the need for cognitive closure that can force us to become

obsessed with short-term, easy-to-achieve goals.) And then underneath, I

described a subgoal and all its SMART components, which forced me to

come up with a plan—which, in turn, made it more likely that all my goals

would be achieved.

One of my stretch goals in reporting this book, for instance, was to find a

story that illustrated how mental models worked. I knew that aviation experts

felt that mental models played an important role in how pilots responded to

emergencies, and so, at the top of my to-do list for this chapter, I wrote:

Then, below that stretch goal, I wrote my SMART goals related to that big

ambition:

In case that’s hard to read, here’s what I wrote:

Stretch: Find an aviation story (a narrowly averted crash?) that

demonstrates mental models.

Specific: Locate an aviation expert by researching academic

papers on Google Scholar.

Measurable: Call four experts each morning until I find the right

person/story.

Achievable: Clear my morning schedule to focus on this task,

and turn off email from 9:00 to 11:30.

Realistic: On Monday, spend an hour researching aviation

experts and creating a call list; rank those experts and, by

10:15, begin my four calls of the day. At the end of each

conversation, ask them to recommend other experts to call.

Timeline: If I do four calls a day, then I should have made at

least sixteen calls by Thursday. If I haven’t found the

perfect story by Thursday, I’ll come up with a new plan. If I

do find the right story, I’ll send a synopsis to my editor on

Friday.

It took only a few minutes to jot down these stretch and SMART goals—

but it made a huge difference in how much I got done that week. Now I

create a similar to-do list for every big task—and as a result, I know exactly

what to do when I sit at my desk each morning. Instead of having to make

decisions—and running the risks of distraction—I have a clear sense of how

to proceed.

In addition, because I’m always being reminded of my stretch goal, I don’t

get easily sidetracked, or captured by the need to simply check things off of

my list. As scientists might say, I’ve muted my craving for cognitive closure.

I don’t stop working merely because I had a good interview, or because I

found a helpful study, or because I found an interesting narrative that might

go in the book. Instead, I’m always reminded that I’m chasing SMART goals

for a bigger reason: to find the perfect story, or finish a chapter, or write a book. In fact, I have a whole series of stretch goals to remind me of my

grander ambitions:

TO SET GOALS:

• Choose a stretch goal: an ambition that reflects your biggest

aspirations.

• Then, break that into subgoals and develop SMART objectives.

FOCUS

This being real life, however, there are always distractions and other demands

competing for my attention. And so, in addition to having a plan, I needed to

work on maintaining my focus. There’s a key insight from the chapter about

the averted aviation disaster of Qantas Flight 32 that I have tried to keep in

mind:

• We aid our focus by building mental models—telling ourselves stories—

about what we expect to see.

To make sure I stayed focused on my stretch and SMART goals, I had to

envision what I expected to happen when I sat down at my desk each

morning. And so, every Sunday night, I got into a habit of taking a few

moments with a pad and pen to imagine what the next day and week ought to

look like. I usually chose three or four things I wanted to make sure

happened, and made myself answer a series of questions:

It typically takes only a few minutes to envision what I hope will occur.

But by the end of this exercise, I have a story in my mind—a mental model of

how my morning should proceed—and, as a result, when distractions

inevitably arise, it’s easier to decide, in the moment, whether they deserve my

focus or can be ignored.

If my email account says there are thirty new messages, I know that I

should ignore them until 11:30, because that’s what the story inside my head

tells me to do. If the phone rings and caller ID indicates it’s an expert I’m

trying to contact, I’ll take the call, because that interruption has a place within my mental model.

I have a stretch goal and a SMART goal that give me a plan—and a picture

inside my head of how that plan is supposed to unfold, so making the choices

that shape focus is much easier.

TO STAY FOCUSED:

• Envision what will happen. What will occur first? What are potential

obstacles? How will you preempt them? Telling yourself a story about

what you expect to occur makes it easier to decide where your focus

should go when your plan encounters real life.

DECISION MAKING

I had worked on coming up with stretch goals and SMART objectives. I had

a mental model to stay focused. I had found ways to improve my motivation.

Despite all that, however, every so often something came along that would

blow my well-crafted intentions apart. Sometimes it was small, like my wife

asking if I wanted to get lunch together. Sometimes it was big, like an editor

asking me to take on an exciting, but unplanned, assignment.

So how should I make a decision when confronted with the unexpected?

Perhaps there was a valuable insight in the chapter on probabilistic thinking:

• Envision multiple futures, and then force myself to figure out which ones

are most likely—and why.

For a simple decision like whether I should meet my wife for lunch, the

calculus is easy: In one potential future, I take an hour for lunch and come back happy and relaxed. In another, lunch goes long and we spend most of it

discussing family logistics and babysitter problems, and when I get back to

my desk I’m fried—and behind schedule.

By thinking through potential futures, I was better prepared to influence

which of those futures would actually occur. When choosing a restaurant to

meet my wife, for instance, I suggested one close to my office so I could

make it back to my desk quickly. When family logistics came up during

lunch, I asked my wife to wait until that evening to talk calendars. By

anticipating the future, I was better prepared to make wiser decisions.

But bigger decisions—such as whether to take on an exciting new writing

assignment—require a bit more analysis. Midway through writing this book,

for instance, a production company asked if I was interested in developing a

TV show. To decide if I should pursue that opportunity—which would delay

my reporting, but might pay off in the long run—I wrote out a few potential

futures of what might happen if I worked on writing a show:

I had no idea how to evaluate these potential futures. I knew there were

dozens of other possibilities I should be considering but couldn’t anticipate.

And so I called some friends in television. Based on those conversations, I

assigned each scenario a rough probability:

Based on the estimations of professionals, it seemed most possible that if I

invested a lot of time, it wasn’t likely to pay off. But if I invested a modest

amount of time, there was a likelihood I would learn something, if nothing

else.

At that point, I wanted to let my Bayesian instincts guide me, and so I

spent a few days letting my imagination play with various outcomes. In the

end, I decided that there was another potential future I was ignoring: That

even if this show never materialized, I might have a lot of fun. So I decided

to commit—but I specified, up front, that I wanted my participation to be modest.

It was a great decision. All told, my involvement in the project was small

—probably the equivalent of two weeks. But the payoffs have exceeded my

expectations. The show will premiere this fall and I’ve learned a lot working

on it.

What’s most important, however, is that I made this decision in a

deliberate way. Because I had anticipated various possibilities about what

might occur—and, in fact, had drawn up some stretch and SMART goals

before joining the project—I was able to manage my involvement.

TO MAKE BETTER DECISIONS:

• Envision multiple futures. By pushing yourself to imagine various

possibilities—some of which might be contradictory—you’re better

equipped to make wise choices.

• We can hone our Bayesian instincts by seeking out different

experiences, perspectives, and other people’s ideas. By finding

information and then letting ourselves sit with it, options become

clearer.

THE BIG IDEA

This appendix offers a quick overview of a few key concepts that have been

meaningful in my own day-to-day life. If you can become more motivated,

more focused, better at setting goals and making good decisions, then you’re

a long way down the path to becoming more productive. There are, of course,

other ideas in this book that also help when we are managing other people,

when we are trying to learn faster, when we need to innovate faster. Each of

those areas of productivity have their own insights, as well:

TO MAKE TEAMS MORE EFFECTIVE:

• Manage the how, not the who of teams. Psychological safety emerges

when everyone feels like they can speak in roughly equal measure and

when teammates show they are sensitive to how each other feel.

• If you are leading a team, think about the message your choices reveal.

Are you encouraging equality in speaking, or rewarding the loudest

people? Are you showing you are listening by repeating what people

say and replying to questions and thoughts? Are you demonstrating

sensitivity by reacting when someone seems upset or flustered? Are

you showcasing that sensitivity, so other people will follow your lead?

TO MANAGE OTHERS PRODUCTIVELY:

• Lean and agile management techniques tell us employees work smarter

and better when they believe they have more decisionmaking authority

and when they believe their colleagues are committed to their success.

• By pushing decision making to whoever is closest to a problem,

managers take advantage of everyone’s expertise and unlock

innovation.

• A sense of control can fuel motivation, but for that drive to produce

insights and solutions, people need to know their suggestions won’t be

ignored and that their mistakes won’t be held against them.

TO ENCOURAGE INNOVATION:

• Creativity often emerges by combining old ideas in new ways—and

“innovation brokers” are key. To become a broker yourself and

encourage brokerage within your organization:

• Be sensitive to your own experiences. Paying attention to how things

make you think and feel is how we distinguish clichés from real

insights. Study your own emotional reactions.

• Recognize that the stress that emerges amid the creative process isn’t a

sign everything is falling apart. Rather, creative desperation is often

critical: Anxiety can be what often pushes us to see old ideas in new

ways.

• Finally, remember that the relief accompanying a creative

breakthrough, while sweet, can also blind us to alternatives. By forcing

ourselves to critique what we’ve already done, by making ourselves

look at it from different perspectives, by giving new authority to

someone who didn’t have it before, we retain clear eyes.

TO ABSORB DATA BETTER:

• When we encounter new information, we should force ourselves to do

something with it. Write yourself a note explaining what you just

learned, or figure out a small way to test an idea, or graph a series of

data points onto a piece of paper, or force yourself to explain an idea to

a friend. Every choice we make in life is an experiment—the trick is

getting ourselves to see the data embedded in those decisions, and then

to use it somehow so we learn from it.

What’s most important, throughout all these concepts, is the foundational

idea undergirding these lessons, the tissue that connects the eight insights at

the heart of this book: Productivity is about recognizing choices that other

people often overlook. It’s about making certain decisions in certain ways.

The way we choose to see our own lives; the stories we tell ourselves, and the

goals we push ourselves to spell out in detail; the culture we establish among teammates; the ways we frame our choices and manage the information in

our lives. Productive people and companies force themselves to make choices

most other people are content to ignore. Productivity emerges when people

push themselves to think differently.

When I was working on this book, I came upon a story that I loved, one of

my favorite bits of reporting. The tale involved Malcom McLean, the man

who essentially created the modern shipping container. McLean died in 2001,

but he left behind videotapes and numerous records, and I spent months

reading about him, as well as interviewing members of his family and dozens

of his former colleagues. They described a man who had relentlessly chased

an idea—that shipping goods inside of big metal boxes would make docks

more

productive—and

how

that

insight

eventually

transformed

manufacturing, the transportation industry, and the economies of whole

continents. They explained that McLean was so productive because he was

fanatically obsessed with a single idea.

I devoted many, many hours to learning about McLean. I wrote several

drafts of his story, determined to fit it into this book.

In the end, however, none of them worked. The lesson he offered—that a

single-minded devotion to an idea can spur massive change—turned out not

to be as universal and important as the other concepts I wanted to explain.

McLean’s story was interesting but not vital. What worked for him doesn’t

work for everyone. There’re lots of examples where fanatical devotion has

backfired. His insight wasn’t big enough to be included among the other eight

ideas in this book.

And yet the time I spent researching McLean was worth it, because

discarding that work helped me understand the mechanics of focus. My

mental model of this book kept conflicting with what I was learning about

McLean. My SMART plan for the McLean story didn’t match up with my

stretch goal of describing universally applicable lessons. In other words,

researching McLean helped me figure out what this book was supposed to be

about. It served as a valuable reminder of how productivity actually

functions: Productivity doesn’t mean that every action is efficient. It doesn’t

mean that waste never occurs. In fact, as Disney learned, sometimes you have

to foster tension to encourage creativity. Sometimes a misstep is the most

important footfall along the path to success.

But in the end, if you learn how to recognize certain choices that, to many,

might not be obvious, then you can become smarter, faster, and better over

time. Anyone can become more creative, more focused, better at framing

their goals and making wise decisions. Schools can be transformed by

changing how people absorb data. Teams can be taught how to learn more

from mistakes, or use tension to their advantage, or make what seems like

misspent hours into lessons getting them closer to their goals. Schools can be

remade by empowering the people closest to a problem. The lives of senior

citizens can be remade by teaching them to become subversives.

We can all become more productive. Now you know how to start.

To Harry, Oliver,

Doris and John,

Andy,

and, most of all, Liz

ACKNOWLEDGMENTS

The truth of the matter is that most of my own ability to become smarter,

faster, and better relies on the kindness of other people, and so there are many

of them I wish to thank.

This book exists because Andy Ward willed it into existence by, at first,

buying an idea and then, over two years, helping mold it into a book.

Everything about Andy—from his graceful editing, to his unyielding

demands for quality, to his genuine and heartfelt friendship—inspires the

people around him to become better and to want to make the world more

beautiful and just. I’m incredibly thankful that I have a chance to know him.

I am also incredibly fortunate to have landed at Random House, which

operates under the wise and steady guidance of Gina Centrello, Susan Kamil,

and Tom Perry, as well as the superhuman efforts of Maria Braeckel, Sally

Marvin, Sanyu Dillon, Theresa Zoro, Avideh Bashirrad, Nicole Morano,

Caitlin McCaskey, Melissa Milsten, Leigh Marchant, Alaina Waagner,

Dennis Ambrose, Nancy Delia, Benjamin Dreyer, and the ever-patient Kaela

Myers. And I owe a huge debt to all of the people who are so talented at

taking these words and putting them into people’s hands: David Phethean,

Tom Nevins, Beth Koehler, David Weller, Richard Callison, Christine

McNamara, Jeffrey Weber, David Romine, Cynthia Lasky, Stacy

Berenbaum, Glenn Ellis, Allyson Pearl, Kristen Fleming, Cathy Serpico, Ken

Wohlrob, and everyone else in Random House Sales. I am equally lucky to

work with Jason Arthur, Emma Finnigan, Matthew Ruddle, Jason Smith,

Nigel Wilcockson, and Aslan Byrne at William Heinemann and Martha

Konya-Forstner and Cathy Poine in Canada.

I am also indebted to Andrew Wylie and James Pullen at the Wylie

Agency. Andrew is steadfast in his desire to make the world safer for his

writers, and I am grateful for his efforts. James Pullen has helped me

understand how to get published in languages I would have almost certainly failed in high school.

I owe a tremendous amount to The New York Times: A huge thanks goes to

Dean Baquet, Andy Rosenthal, and Matt Purdy, whose leadership and

examples help guide my personal choices daily. Arthur Sulzberger, Mark

Thompson, and Meredith Kopit Levien have been great friends and make it

possible for the pursuit of truth to occur. I’m so thankful for the time I’ve spent with Dean Murphy, business editor, and Peter Lattman, deputy business

editor, both of whose friendship, advice and patience allowed me to write this

book. Similarly, Larry Ingrassia’s guidance on nearly every issue is

indispensible. Gerry Marzorati has been a great friend, as has Kinsey Wilson,

Susan Chira, Jake Silverstein, Bill Wasik, and Cliff Levy.

A few other thanks: I’m indebted to my Times colleagues David

Leonhardt, A. G. Sulzberger, Walt Bogdanich, Sam Dolnick, Eduardo Porter,

David Perpich, Jodi Kantor, Vera Titunik, Peter Lattman, David Segal, Joe

Nocera, Michael Barbaro, Jim Stewart, and others who have been so

generous with their ideas.

Similarly, I’m thankful to Alex Blumberg, Adam Davidson, Paula

Szuchman, Nivi Nord, Alex Berenson, Nazanin Rafsanjani, Brendan

Koerner, Nicholas Thompson, Sarah Ellison, Amanda Schaffer, Dennis

Potami, James and Mandy Wynn, Noah Kotch, Greg Nelson, Caitlin Pike,

Jonathan Klein, Amanda Klein, Matthew and Chloe Galkin, Nick

Panagopulos and Marissa Ronca, Donnan Steele, Stacey Steele, Wesley

Morris, Adir Waldman, Rich Frankel, Jennifer Couzin, Aaron Bendikson,

Richard Rampell, David Lewicki, Beth Waltemath, Ellen Martin, Amy

Wallace, Russ Uman, Erin Brown, Jeff Norton, Raj De Datta, Ruben Sigala,

Dan Costello, and Peter Blake, who all provided crucial support and guidance

along the way. The book’s cover and interior graphics sprung directly from

the mind of the incredibly talented Anton Ioukhnovets. Thank you, Anton.

Thank you, as well, to my stalwart fact checkers—Cole Louison and

Benjamin Phalen—and Olivia Boone, who helped format and organize the

endnotes.

I am indebted to the many people who were generous with their time and

knowledge during the reporting of this book. Many are mentioned in the

notes, but I wanted to give additional thanks to William Langewiesche, who

provided guidance on the mechanics (and writing) of flight, and Ed Catmull and Amy Wallace, who made the Disney chapter happen.

Finally, my deepest thanks are to my family: Katy Duhigg, Jacquie

Jenkusky, David Duhigg, Dan Duhigg, Toni Martorelli, Alexandra Alter, and

Jake Goldstein have been wonderful friends. My sons, Oliver and Harry,

have been sources of inspiration and joy. My parents, John and Doris,

encouraged me from a young age to write.

And, of course, my wife, Liz, whose constant love, support, guidance,

intelligence, and friendship made this book possible.

—November 2015

A NOTE ON SOURCES

The reporting in this book is based on hundreds of interviews, papers, and

studies. Many of those sources are detailed in the text itself or the endnotes,

along with guides to additional resources for interested readers.

In most situations, individuals who provided major sources of information

or who published research that was integral to reporting were provided with

summaries of my reporting and offered the opportunity to review facts and

offer additional comments, address discrepancies, or register issues with how

information is portrayed. Many of their comments are reproduced in the

endnotes. (No source was given access to the book’s complete text; all

comments are based on summaries provided to sources.) Independent fact-

checkers also contacted major sources and reviewed documents to verify and

corroborate claims.

In a small number of cases, confidentiality was extended to sources who,

for a variety of reasons, did not wish to speak on a for-attribution basis. In three instances, some identifying characteristics have been withheld or

slightly modified to conform with patient privacy ethics or for other reasons.

NOTES

CHAPTER ONE: MOTIVATION

Ochsner Clinic in New Orleans The facility is now known as the Ochsner Medical Center.

Archives of Neurology Richard L. Strub, “Frontal Lobe Syndrome in a Patient with Bilateral Globus Pallidus Lesions,” Archives of Neurology 46, no. 9 (1989): 1024–27.

“to get up in the morning” Michel Habib, “Athymhormia and Disorders of Motivation in Basal Ganglia Disease,” The Journal of Neuropsychiatry and

Clinical Neurosciences 16, no. 4 (2004): 509–24.

movement and emotion emerge This is how Mauricio Delgado, a neurologist at Rutgers, describes the striatum: “The striatum is the input unit

of a larger structure, the basal ganglia. I say the input unit because it receives

connections from different brain areas which subserve distinct brain functions

—putting the striatum in a prime position to influence behavior. The basal

ganglia and in turn the striatum are very important in facets of behavior

related to motor (deficits in this structure is common in Parkinson’s patients),

cognitive and motivation. One line of thinking regarding the striatum and its

role in motivation and more specifically reward processing is that it is

involved in learning about rewards and using that information to make

decisions that help guide behavior, updating the brain along the way whether

a reward is better or worse than prior expectations.”

regulating our moods Oury Monchi et al., “Functional Role of the Basal Ganglia in the Planning and Execution of Actions,” Annals of Neurology 59,

no.2 (2006): 257–64; Edmund T. Rolls, “Neurophysiology and Cognitive

Functions of the Striatum,” Revue Neurologique 150 (1994): 648–60; Patricia

S. Goldman-Rakic, “Regional, Cellular, and Subcellular Variations in the

Distribution of D1 and D5 Dopamine Receptors in Primate Brain,” The

Journal of Neuroscience 15, no. 12 (1995): 7821–36; Bradley Voytek and

Robert T. Knight, “Prefrontal Cortex and Basal Ganglia Contributions to Working Memory,” Proceedings of the National Academy of Sciences of the

United States of America 107, no. 42 (2010): 18167–72.

motivation had disappeared For my understanding of how brain injuries influence behavior, I am indebted to Julien Bogousslavsky and Jeffrey L.

Cummings, Behavior and Mood Disorders in Focal Brain Lesions

(Cambridge: Cambridge University Press, 2000).

striatal injuries Parkinson’s frequently involves injuries to the substantia nigra, a region that communicates with the striatum. R. K. B. Pearce et al.,

“Dopamine Uptake Sites and Dopamine Receptors in Parkinson’s Disease

and Schizophrenia,” European Neurology 30, supplement 1 (1990): 9–14;

Philip Seeman et al., “Low Density of Dopamine D4 Receptors in

Parkinson’s, Schizophrenia, and Control Brain Striata,” Synapse 14, no. 4

(1993): 247–53; Philip Seeman et al., “Human Brain D1 and D2 Dopamine

Receptors in Schizophrenia, Alzheimer’s, Parkinson’s, and Huntington’s

Diseases,” Neuropsychopharmacology 1, no. 1 (1987): 5–15.

see a computer screen Mauricio R. Delgado et al., “Tracking the Hemodynamic Responses to Reward and Punishment in the Striatum,”

Journal of Neurophysiology 84, no. 6 (2000): 3072–77.

expectation and excitement In some versions of this experiment, participants were rewarded for guessing right and penalized for guessing

wrong with small financial winnings. In response to a fact-checking email,

Delgado provided further context for the experiments: “The goal of that

initial study was to investigate the human reward circuit. That is, we know

from animal research that certain brain regions were important for processing

information about reward. We knew less about how that translated to the

human brain and how it translated to more common human rewards such as

money, which had implications to behavioral addictions such as pathological

gambling. Thus, with the guessing game, our initial goal was to compare

what happened in the brain when participants received a monetary reward

(for a correct guess) and a monetary punishment or loss (for an incorrect

guess). The pattern we observe is very characteristic of a reward response.

We see activity in the striatum (both dorsal and ventral parts). The response is an initial increase at the beginning of the trial when the question mark

appears and they make a guess. We reasoned that it reflected anticipation of a

potential reward. Other work using this task (see Delgado et al. 2004, Leotti

and Delgado 2011) support that as does the work by Brian Knutson (2001).

They don’t know yet if their guess is correct and lead to a reward or incorrect

and lead to a loss. So the increase is common for both types of trials. Once

the outcome is revealed, we see an interesting pattern where the striatum

differentiates between a positive and negative outcome—a gain or a loss. It is

increased for a gain and decreased response for a loss. One interpretation of

this finding was that the striatum was coding for the value of an outcome. A

more global interpretation that takes into account all the neural inputs and

outputs of this structure is that it takes in information about the

outcome/reward, it matches up with the expectations (e.g., was the outcome

better or worse than expected—if you guessed high was the card high, or did

you make the wrong guess) and allows for the system to update and inform

the next decision (e.g., maybe try low next time).”

computer guessed for them In response to a fact-checking email, Delgado expanded his comments: “There were three experiments related to this….[In]

the first one (Tricomi et al. 2004), they were told that they would see two circles. Upon seeing the yellow circle for example they would guess as before

whether the correct answer was button 1 or 2 and were told that a correct

response would yield a monetary reward. If they saw a blue circle they were

told to press a button (motor control) but that the button had nothing to do with the reward, it was random. In truth, the reward was random in both

cases, but if the subjects believed that their button press mattered, as in the yellow circle condition, then they engaged the striatum response much more

than if it was a non-contingent reward. This experiment showed that if

participants felt they were in control that the reward response was more

prominent. The second experiment took this back to the card guessing game

(Delgado et al. 2005) and this time added a cue, like a circle, before each trial

that predicted if the card would be high or low. Participants had to learn via

trial and error what the cue predicted. This experiment showed that the signal

in the striatum was related to learning about the reward, rather than just

purely processing the reward value….In [the] third experiment (Leotti and

Delgado 2005) we presented subjects with let’s say two cues—a square and a circle. When they saw the square, they knew they would be faced with a

50/50 choice (a guess of sorts) and if they chose correctly, they would get a

reward (no losses in this experiment, either a reward or no reward). In this condition, they felt in ‘control.’ Much like my participant who felt they could

‘beat the game.’ The other condition was the no-choice condition. Here, they

saw a circle and were faced with the same choice. Except this time the

computer picked for them. And if the computer was right they got a reward.

So in both conditions one could get a reward (or no reward). But the key

difference was that participants either had a choice or the computer chose.

Interestingly, people preferred the choice condition, even though such

condition required more effort (the actual choice) and led to the same amount

of rewards. We also saw that the striatum activity was present to the square

(compared to the circle). That is, when participants found out they had a

choice, we saw activity in this reward area of the brain, suggesting that the mere opportunity for exerting one’s choice may be rewarding in and of

itself.”

believed they were in control For more on Delgado’s work, I recommend Elizabeth M. Tricomi, Mauricio R. Delgado, and Julie A. Fiez, “Modulation

of Caudate Activity by Action Contingency,” Neuron 41, no. 2 (2004): 281–

92; Mauricio R. Delgado, M. Meredith Gillis, and Elizabeth A. Phelps,

“Regulating the Expectation of Reward via Cognitive Strategies,” Nature

Neuroscience 11, no. 8 (2008): 880–81; Laura N. Martin and Mauricio R.

Delgado, “The Influence of Emotion Regulation on Decision-Making Under

Risk,” Journal of Cognitive Neuroscience 23, no. 9 (2011): 2569–81; Lauren

A. Leotti and Mauricio R. Delgado, “The Value of Exercising Control over

Monetary Gains and Losses,” Psychological Science 25, no. 2 (2014): 596–

604; Lauren A. Leotti and Mauricio R. Delgado, “The Inherent Reward of

Choice,” Psychological Science 22 (2011): 1310–18.

reported to a boss “Self-Employment in the United States,” Monthly Labor Review,

U.S.

Bureau

of

Labor

Statistics,

September

2010,

http://www.bls.gov/opub/mlr/2010/09/art2full.pdf.

otherwise transitory positions A 2006 study by the Government

Accountability Office found that 31 percent of workers were in temporary positions.

allocate their energy Michelle Conlin et al., “The Disposable Worker,”

Bloomberg Businessweek, January 7, 2010.

“The need for control” Lauren A Leotti, Sheena S. Iyengar, and Kevin N.

Ochsner, “Born to Choose: The Origins and Value of the Need for Control,”

Trends in Cognitive Sciences 14, no. 10 (2010): 457–63.

setbacks faster Diana I. Cordova and Mark R. Lepper, “Intrinsic Motivation and the Process of Learning: Beneficial Effects of Contextualization,

Personalization, and Choice,” Journal of Educational Psychology 88, no. 4

(1996): 715; Judith Rodin and Ellen J. Langer, “Long-Term Effects of a

Control-Relevant Intervention with the Institutionalized Aged,” Journal of

Personality and Social Psychology 35, no. 12 (1977): 897; Rebecca A. Henry

and Janet A. Sniezek, “Situational Factors Affecting Judgments of Future

Performance,” Organizational Behavior and Human Decision Processes 54, no. 1 (1993): 104–32; Romin W. Tafarodi, Alan B. Milne, and Alyson J.

Smith. “The Confidence of Choice: Evidence for an Augmentation Effect on

Self-Perceived Performance,” Personality and Social Psychology Bulletin 25,

no. 11 (1999): 1405–16; Jack W. Brehm, “Postdecision Changes in the

Desirability of Alternatives,” The Journal of Abnormal and Social

Psychology 52, no. 3 (1956): 384; Leon Festinger, A Theory of Cognitive Dissonance, vol. 2 (Stanford, Calif.: Stanford University Press, 1962); Daryl

J. Bem, “An Experimental Analysis of Self-Persuasion,” Journal of

Experimental Social Psychology 1, no. 3 (1965): 199–218; Louisa C. Egan, Laurie R. Santos, and Paul Bloom, “The Origins of Cognitive Dissonance:

Evidence from Children and Monkeys,” Psychological Science 18, no. 11

(2007): 978–83.

longer than their peers E. J. Langer and J. Rodin, “The Effects of Choice and Enhanced Personal Responsibility for the Aged: A Field Experiment in

an Institutional Setting,” Journal of Personality and Social Psychology 34, no. 2 (1976): 191–98.

food into their mouths Margaret W. Sullivan and Michael Lewis,

“Contextual Determinants of Anger and Other Negative Expressions in

Young Infants,” Developmental Psychology 39, no. 4 (2003): 693.

freedom to choose Leotti and Delgado, “Inherent Reward of Choice.”

Psychological Science in 2011 Ibid.

autonomy and self-determination Erika A. Patall, Harris Cooper, and Jorgianne Civey Robinson, “The Effects of Choice on Intrinsic Motivation

and Related Outcomes: A Meta-Analysis of Research Findings,”

Psychological Bulletin 134, no. 2 (2008): 270; Deborah J. Stipek and John R.

Weisz, “Perceived Personal Control and Academic Achievement,” Review of

Educational Research 51, no. 1 (1981): 101–37; Steven W. Abrahams,

“Goal-Setting and Intrinsic Motivation: The Effects of Choice and

Performance Frame-of-Reference” (PhD diss., Columbia University, 1989);

Teresa M. Amabile and Judith Gitomer, “Children’s Artistic Creativity

Effects of Choice in Task Materials,” Personality and Social Psychology

Bulletin 10, no. 2 (1984): 209–15; D’Arcy A. Becker, “The Effects of Choice

on Auditors’ Intrinsic Motivation and Performance,” Behavioral Research in

Accounting 9 (1997); Dan Stuart Cohen, “The Effects of Task Choice,

Monetary, and Verbal Reward on Intrinsic Motivation: A Closer Look at

Deci’s Cognitive Evaluation Theory” (PhD diss., Ohio State University,

1974); Diana I. Cordova and Mark R. Lepper, “Intrinsic Motivation and the

Process of Learning: Beneficial Effects of Contextualization, Personalization,

and Choice,” Journal of Educational Psychology 88, no. 4 (1996): 715; Hsiao

d’Ailly, “The Role of Choice in Children’s Learning: A Distinctive Cultural

and Gender Difference in Efficacy, Interest, and Effort,” Canadian Journal of

Behavioural Science 36, no. 1 (2004): 17; Edward L. Deci, The Psychology of Self-Determination (New York: Free Press, 1980); J. B. Detweiler, R. J.

Mendoza, and M. R. Lepper, “Perceived Versus Actual Choice: High

Perceived Choice Enhances Children’s Task Engagement,” 8th Annual

Meeting of the American Psychological Society, San Francisco, 1996; John J.

M. Dwyer, “Effect of Perceived Choice of Music on Exercise Intrinsic

Motivation,” Health Values: The Journal of Health Behavior, Education and

Promotion 19, no. 2 (1995): 18–26; Gregory G. Feehan and Michael E.

Enzle, “Subjective Control over Rewards: Effects of Perceived Choice of Reward Schedule on Intrinsic Motivation and Behavior Maintenance,”

Perceptual and Motor Skills 72, no. 3 (1991): 995–1006; Terri Flowerday, Gregory Schraw, and Joseph Stevens, “The Role of Choice and Interest in

Reader Engagement,” The Journal of Experimental Education 72, no. 2

(2004): 93–114; Claus A. Hallschmidt, “Intrinsic Motivation: The Effects of

Task Choice, Reward Magnitude and Reward Choice” (PhD diss., University

of Alberta, 1977); Sheena S. Iyengar and Mark R. Lepper, “Rethinking the

Value of Choice: A Cultural Perspective on Intrinsic Motivation,” Journal of

Personality and Social Psychology 76, no. 3 (1999): 349; Keven A. Prusak et

al., “The Effects of Choice on the Motivation of Adolescent Girls in Physical

Education,” Journal of Teaching in Physical Education 23, no. 1 (2004): 19–

29; Johnmarshall Reeve, Glen Nix, and Diane Hamm, “Testing Models of the

Experience of Self-Determination in Intrinsic Motivation and the Conundrum

of Choice,” Journal of Educational Psychology 95, no. 2 (2003): 375; Romin

W. Tafarodi, Alan B. Milne, and Alyson J. Smith, “The Confidence of

Choice: Evidence for an Augmentation Effect on Self-Perceived

Performance,” Personality and Social Psychology Bulletin 25, no. 11 (1999):

1405–16; Miron Zuckerman et al., “On the Importance of Self-Determination

for Intrinsically-Motivated Behavior,” Personality and Social Psychology

Bulletin 4, no. 3 (1978): 443–46.

on a new life In response to a fact-checking email, Colonel Robert Gruny, commanding officer, Recruit Training Regiment, MCRD San Diego, wrote:

“From the moment the recruits first step off of the bus onto the yellow

footprints they are exposed to a degree of collective shock and stress that is

designed to emphasize teamwork [and] obedience to orders and to reinforce

the fact that they are entering into a new phase of their life in which selfless

dedication to each other is coveted far more than individual achievement. In

addition to the medical processing and haircuts reference above night one

includes being checked for contraband, the very practical tasks of

administrative processing and clothing issue, and making an initial call home

to inform their parents or other designated individual that they have arrived

safely at the Recruit Depot.”

“their whole life” In response to a fact-checking email, Colonel Gruny wrote

regarding Krulak’s reforms: “The series of reforms were centered on the institution of values based training into recruit training and the introduction of the Crucible. While self-motivation and leadership were certainly

enhanced by these reforms they also focused on teamwork, followership, and

core values development (honor, courage, and commitment). Gen. Krulak

sought to embed a training philosophy that resulted in our Marines making

the right kind of values based decisions, in combat or in peacetime.”

best course of action For my understanding of USMC boot camp, I am indebted to General Krulak and Major Neil A. Ruggiero, director of public

affairs at MCRD San Diego/Western Recruiting Region. Additionally, I am

indebted to Thomas E. Ricks and his book Making the Corps (New York:

Scribner, 2007). I have also drawn upon Vincent Martino, Jason A.

Santamaria, and Eric K. Clemons, The Marine Corps Way: Using Maneuver

Warfare to Lead a Winning Organization (New York: McGraw-Hill, 2005);

James Woulfe, Into the Crucible: Making Marines for the 21st Century

(Novato, Calif.: Presidio Press, 2009); Jon R. Katzenbach, Peak

Performance: Aligning the Hearts and Minds of Your Employees (Boston:

Harvard Business Press, 2000); Megan M. Thompson and Donald R.

McCreary, Enhancing Mental Readiness in Military Personnel (Toronto:

Defense Research and Development, 2006); Ross R. Vickers Jr. and Terry L.

Conway, “Changes in Perceived Locus of Control During Basic Training”

(1984); Raymond W. Novaco et al., Psychological and Organizational

Factors Related to Attrition and Performance in Marine Corps Recruit

Training, no. AR-001 (Seattle: Washington University Department of

Psychology, 1979); Thomas M. Cook, Raymond W. Novaco, and Irwin G.

Sarason, “Military Recruit Training as an Environmental Context Affecting

Expectancies for Control of Reinforcement,” Cognitive Therapy and

Research 6, no. 4 (1982): 409–27.

since the 1950s Julian B. Rotter, “Generalized Expectancies for Internal Versus External Control of Reinforcement,” Psychological Monographs:

General and Applied 80, no. 1 (1966): 1; Timothy A. Judge et al., “Are Measures of Self-Esteem, Neuroticism, Locus of Control, and Generalized

Self-Efficacy Indicators of a Common Core Construct?” Journal of

Personality and Social Psychology 83, no. 3 (2002): 693; Herbert M.

Lefcourt, Locus of Control: Current Trends in Theory and Research (Hillsdale, N.J.: L. Erlbaum, 1982); Cassandra Bolyard Whyte, “High-Risk

College Freshmen and Locus of Control,” Humanist Educator 16, no. 1

(1977): 2–5; Angela Roddenberry and Kimberly Renk, “Locus of Control and

Self-Efficacy: Potential Mediators of Stress, Illness, and Utilization of Health

Services in College Students,” Child Psychiatry and Human Development 41,

no. 4 (2010): 353–70; Victor A. Benassi, Paul D. Sweeney, and Charles L.

Dufour, “Is There a Relation Between Locus of Control Orientation and

Depression?” Journal of Abnormal Psychology 97, no. 3 (1988): 357.

“Internal locus of control” Alexandra Stocks, Kurt A. April, and Nandani Lynton, “Locus of Control and Subjective Well-Being: A Cross-Cultural

Study,” Problems and Perspectives in Management 10, no. 1 (2012): 17–25.

difficult puzzles Claudia M. Mueller and Carol S. Dweck, “Praise for Intelligence Can Undermine Children’s Motivation and Performance,”

Journal of Personality and Social Psychology 75, no 1 (1998): 33.

that study, told me The specific experiment conducted by Professor Dweck described in this chapter was focused on her implicit theory of intelligence

rather than locus of control. In an interview, she drew comparisons between

that work and its implications for understanding locus of control.

“in control of themselves” For more on Professor Dweck’s fascinating research, I recommend Carol S. Dweck and Ellen L. Leggett, “A Social-Cognitive Approach to Motivation and Personality,” Psychological Review

95, no. 2 (1988): 256; Carol S. Dweck, “Motivational Processes Affecting

Learning,” American Psychologist 41, no 10 (1986): 1040; Carol S. Dweck,

Chi-yue Chiu, and Ying-yi Hong, “Implicit Theories and Their Role in

Judgments and Reactions: A Word from Two Perspectives,” Psychological

Inquiry 6, no. 4 (1995): 267–85; Carol Dweck, Mindset: The New Psychology

of Success (New York: Random House, 2006).

ketchup bottles In response to a fact-checking email, Colonel Jim Gruny, commanding officer, Recruit Training Regiment, MCRD San Diego, wrote

that “this sounds like a scenario that may have been accurate at the time the

Marine describing it experienced recruit training. Recruits no longer clean mess halls. That said, this scenario does accurately illustrate the methods

used by our drill instructors and the lessons they seek to impart on our

recruits.”

obstacle courses In response to a fact-checking email, a spokesman for the USMC stressed that recruits are under supervision during the entirety of the

Crucible, and that the area where the Crucible takes place is USMC property.

In California, the Crucible takes place within Camp Pendleton; in Parris

Island, South Carolina, it is an area around an old airstrip. Colonel Jim

Gruny, commanding officer, Recruit Training Regiment, MCRD San Diego,

wrote that “General Krulak pioneered the use of values-based training and a

crucible to cement it among recruits. Krulak said his original intent for the Crucible as a culminating event was threefold. First, it would be the drill

instructor’s last opportunity to give a ‘go or no go’ to the individual recruit.

Second, it would ‘emphasize and reinforce all the core values training that

was ongoing throughout recruit training’…Last, it would ‘bring the recruit

from an emphasis on self-discipline to where we want them to be in combat,

which is selflessness.’…Failure to complete the Crucible may require a

recruit to be recycled to another Company with which he can undergo the

Crucible again. He will only be dropped from the Marine Corps if he

repeatedly fails to complete the Crucible or if he suffers an injury that

prevents further military service.” Colonel Christopher Nash, commanding

officer, Weapons and Field Training Battalion, wrote: “The Crucible is a 54-

hour endurance event that marks the transformation from civilian to U.S.

Marine. Recruits, over a three day period, will travel approximately 68 km on

foot, eat no more than three MREs for the duration of the event and operate

with less than four hours of sleep a night. The focus of the Crucible is core

values and teamwork. Recruits must overcome 24 stations/obstacles,

participate in three core values discussions and two night endurance events

during the three days. No event can be completed alone. The Crucible

culminates with a 16 km ‘Reaper’ hike in which an emblem ceremony

occurs. During this event recruits earn the title Marine.”

during basic training Joey E. Klinger, “Analysis of the Perceptions of Training Effectiveness of the Crucible at Marine Corps Recruit Depot, San

Diego” (PhD diss., Naval Postgraduate School, 1999); S. P. Dynan, Updating Tradition: Necessary Changes to Marine Corps Recruit Training (Quantico,

Va.: Marine Corps Command and Staff College, 2006); M. C. Cameron,

Crucible Marine on Point: Today’s Entry-Level Infantry Marine (Quantico,

Va.: Marine Corps Command and Staff College, 2006); Michael D. Becker,

“ ‘We Make Marines’: Organizational Socialization and the Effects of ‘The

Crucible’ on the Values Orientation of Recruits During US Marine Corps

Training” (PhD diss., Indiana University of Pennsylvania, 2013); Benjamin

Eiseman, “Into the Crucible: Making Marines for the 21st Century,” Military

Review 80, no. 1 (2000): 94; Terry Terriff, “Warriors and Innovators:

Military Change and Organizational Culture in the US Marine Corps,”

Defense Studies 6, no. 2 (2006): 215–47; Antonio B. Smith, United States Marine Corps’ Entry-Level Training for Enlisted Infantrymen: The

Marginalization of Basic Warriors (Quantico, Va.: Marine Corps Command

and Staff College, 2001); William Berris, Why General Krulak Is the Marine

Corps’ Greatest Strategic Leader (Carlisle Barracks, Penn.: U.S. Army War

College, 2011); Terry Terriff, “Of Romans and Dragons: Preparing the US

Marine Corps for Future Warfare,” Contemporary Security Policy 28, no. 1

(2007): 143–62; Marie B. Caulfield, Adaptation to First Term Enlistment

Among Women in the Marine Corps (Boston: Veterans Administration

Medical Center, 2000); Craig M. Kilhenny, “An Organizational Analysis of

Marine Corps Recruit Depot, San Diego” (PhD diss., Naval Postgraduate

School, 2003); Larry Smith, The Few and the Proud: Marine Corps Drill

Instructors in Their Own Words (New York: W. W. Norton, 2007); Thomas

M. Cook, Raymond W. Novaco, and Irwin G. Sarason, “Military Recruit

Training as an Environmental Context Affecting Expectancies for Control of

Reinforcement,” Cognitive Therapy and Research 6, no. 4 (1982): 409–27;

Ross R. Vickers Jr. and Terry L. Conway, The Marine Corps Basic Training

Experience: Psychosocial Predictors of Performance, Health, and Attrition

(San Diego: Naval Health Research Center, 1983); Ross R. Vickers Jr. and

Terry L. Conway, “Changes in Perceived Locus of Control During Basic

Training” (paper presented at the Annual Meeting of the American

Psychological Association: Toronto, Canada, August 24–28 (1984); Thomas

M. Cook, Raymond W. Novaco, and Irwin G. Sarason, Generalized

Expectancies, Life Experiences, and Adaptation to Marine Corps Recruit

Training (Seattle: Washington University: Department of Psychology, 1980);

R. R. Vickers Jr. et al., The Marine Corps Training Experience: Correlates of Platoon Attrition Rate Differences (San Diego: Naval Health Research

Center, 1983).

force upon them Rosalie A. Kane et al., “Everyday Matters in the Lives of Nursing Home Residents: Wish for and Perception of Choice and Control,”

Journal of the American Geriatrics Society 45, no. 9 (1997): 1086–93;

Rosalie A. Kane et al., “Quality of Life Measures for Nursing Home

Residents,” The Journals of Gerontology Series A: Biological Sciences and

Medical Sciences 58, no. 3 (2003): 240–48; James R. Reinardy and Rosalie A. Kane, “Anatomy of a Choice: Deciding on Assisted Living or Nursing

Home Care in Oregon,” Journal of Applied Gerontology 22, no. 1 (2003):

152–74; Robert L. Kane and Rosalie A. Kane, “What Older People Want

from Long-Term Care, and How They Can Get It,” Health Affairs 20, no. 6

(2001): 114–27; William J. McAuley and Rosemary Blieszner, “Selection of

Long-Term Care Arrangements by Older Community Residents,” The

Gerontologist 25, no. 2 (1985): 188–93; Bart J. Collopy, “Autonomy in Long

Term Care: Some Crucial Distinctions,” The Gerontologist 28, supplement

(1988): 10–17; Elizabeth H. Bradley et al., “Expanding the Andersen Model:

The Role of Psychosocial Factors in Long-Term Care Use,” Health Services

Research 37, no. 5 (2002): 1221–42; Virginia G. Kasser and Richard M.

Ryan, “The Relation of Psychological Needs for Autonomy and Relatedness

to Vitality, Well-Being, and Mortality in a Nursing Home: Effects of Control

and Predictability on the Physical and Psychological Well-Being of the

Institutionalized Aged,” Journal of Applied Social Psychology 29, no. 5

(1999): 935–54; James F. Fries, “The Compression of Morbidity,” The

Milbank Memorial Fund Quarterly: Health and Society 83, no. 4 (2005):

801–23; Richard Schulz, “Effects of Control and Predictability on the

Physical and Psychological Well-Being of the Institutionalized Aged,”

Journal of Personality and Social Psychology 33, no. 5 (1976): 563.

They didn’t feel anything In response to a fact-checking email, Habib expanded upon his comments and said that rather than categorize the patients

as not understanding feelings, it might be more accurate to say “it is a matter

of expression of feelings, more than feeling itself. They can recall what they

felt before, and there is no evidence they cannot feel it anymore. Instead, it

seems that since they have no more manifestations of seeking satisfaction, they look like they had no feeling. This is also an intriguing observation,

since it suggests that the intensity of feelings is dependent upon the

individual’s capacity of seeking satisfaction or reward.”

CHAPTER TWO: TEAMS

school’s websites explained Alex Roberts, “What a Real Study Group Looks Like,” Yale School of Management, MBA Blog, August 31, 2010,

http://som.yale.edu/what-real-study-group-looks.

“didn’t gel.” In an email sent in response to fact-checking questions, Julia Rozovsky wrote: “There were a few members of my study group that I

developed close friendships with, however I was much closer to my case

study team.”

first in the nation “Yale SOM Team Wins National Net Impact Case Competition,” Yale School of Management, November 10, 2011,

http://som.yale.edu/news/news/yale-som-team-wins-national-net-impact-

case-competition.

were at Yale In an email sent in response to fact-checking questions, Julia Rozovsky wrote: “We chose to enter the competition each time. Each

competition was a separate team/entry/packet/process. I just happened to

work with the same team fairly consistently.”

spent their time In an email sent in response to fact-checking questions, a Google spokeswoman wrote that “People Analytics’ overarching theme is

that we study the key drivers of Health, Happiness and Productivity of

Googlers in a scientific and rigorous way….No one part of Google controls

or oversees hiring or promo, but rather it is shared with Googlers themselves,

with managers, etc.” For more on Google’s approach to human resources,

please see Thomas H. Davenport, Jeanne Harris, and Jeremy Shapiro,

“Competing on Talent Analytics,” Harvard Business Review 88, no. 10

(2010): 52–58; John Sullivan, “How Google Became the #3 Most Valuable

Firm by Using People Analytics to Reinvent HR,” ERE Media, February 25,

2013,

http://www.eremedia.com/ere/how-google-became-the-3-most-

valuable-firm-by-using-people-analytics-to-reinvent-hr/; David A. Garvin,

“How Google Sold Its Engineers on Management,” Harvard Business Review 91, no. 12 (2013): 74–82; Adam Bryant, “Google’s Quest to Build a Better

Boss,” The New York Times, March 12, 2011; Laszlo Bock, Work Rules!

Insights from Inside Google That Will Transform the Way You Live and Lead

(New York: Twelve, 2015).

America’s top workplaces In 2007, 2008, 2012, 2013, and 2014, Google was ranked number one by Fortune.

one of the effort’s researchers In an email sent in response to fact-checking questions, Julia Rozovsky wrote: “I worked on several other efforts prior to

joining the Project Aristotle team. Here’s a quick bio that I use internally:

‘Julia Rozovsky joined Google’s People Analytics team in August 2012.

During her time at Google, Julia has advised teams on workforce planning

and design strategies, analyzed the impact of workplace flexibility programs,

and conducted research on empowering leaders. She is currently the [project

manager] of Project Aristotle, which aims to improve team effectiveness at

Google. Prior to Google, Julia collaborated with Harvard Business School

academics on competitive strategy and organizational behavior research

focusing specifically on game theory, ethics and financial controls, and

organizational structure. Earlier in her career, Julia was a strategy consultant

with a boutique marketing analytics firm. Julia holds an MBA from the Yale

School of Management, and a BA in mathematics and economics from Tufts

University.’ ”

made a team effective In comments sent in response to fact-checking questions, a Google spokeswoman wrote: “The first thing we had to start with

was the definition of a team, and we arrived at groups of people collaborating

closely on projects and working toward a common goal. Then, since we knew

a hierarchical team definition would be too limiting in our environment

where people collaborate across reporting lines, we had to figure out how to

systematically identify intact teams and their accurate membership so we

could study them. In the end, we had to do it manually, by asking senior

leaders to identify teams in their orgs and ask the teams’ leads to confirm the

members.”

“appropriate behavior” David Lyle Light Shields et al., “Leadership, Cohesion, and Team Norms Regarding Cheating and Aggression,” Sociology

of Sport Journal 12 (1995): 324–36.

deference to the team For more on norms, please see Muzafer Sherif, The Psychology of Social Norms (London: Octagon Books, 1965); Jay Jackson,

“Structural Characteristics of Norms,” Current Studies in Social Psychology

301 (1965): 309; P. Wesley Schultz et al., “The Constructive, Destructive,

and Reconstructive Power of Social Norms,” Psychological Science 18, no. 5

(2007): 429–34; Robert B. Cialdini, “Descriptive Social Norms as

Underappreciated Sources of Social Control,” Psychometrika 72, no. 2

(2007): 263–68; Keithia L. Wilson et al., “Social Rules for Managing

Attempted Interpersonal Domination in the Workplace: Influence of Status

and Gender,” Sex Roles 44, nos. 3–4 (2001): 129–54; Daniel C. Feldman,

“The Development and Enforcement of Group Norms,” Academy of

Management Review 9, no. 1 (1984): 47–53; Deborah J. Terry, Michael A.

Hogg, and Katherine M. White, “The Theory of Planned Behaviour: Self-

Identity, Social Identity and Group Norms,” The British Journal of Social

Psychology 38 (1999): 225; Jolanda Jetten, Russell Spears, and Antony S. R.

Manstead, “Strength of Identification and Intergroup Differentiation: The

Influence of Group Norms,” European Journal of Social Psychology 27, no.

5 (1997): 603–9; Mark G. Ehrhart and Stefanie E. Naumann, “Organizational

Citizenship Behavior in Work Groups: A Group Norms Approach,” Journal

of Applied Psychology 89, no. 6 (2004): 960; Daniel C. Feldman, “The

Development and Enforcement of Group Norms,” Academy of Management

Review 9, no. 1 (1984): 47–53; Jennifer A. Chatman and Francis J. Flynn,

“The Influence of Demographic Heterogeneity on the Emergence and

Consequences of Cooperative Norms in Work Teams,” Academy of

Management Journal 44, no. 5 (2001): 956–74.

discouraged by our teammates Sigal G. Barsade, “The Ripple Effect: Emotional Contagion and Its Influence on Group Behavior,” Administrative

Science Quarterly 47, no. 4 (2002): 644–75; Vanessa Urch Druskat and

Steven B. Wolff, “Building the Emotional Intelligence of Groups,” Harvard

Business Review 79, no. 3 (2001): 80–91; Vanessa Urch Druskat and Steven

B. Wolff, “Group Emotional Intelligence and Its Influence on Group

Effectiveness,” in The Emotionally Intelligent Workplace: How to Select for, Measure, and Improve Emotional Intelligence in Individuals, Groups and

Organizations, ed. Cary Cherniss and Daniel Goleman (San Francisco:

Jossey-Bass, 2001), 132–55; Daniel Goleman, Richard Boyatzis, and Annie

McKee, “The Emotional Reality of Teams,” Journal of Organizational

Excellence 21, no. 2 (2002): 55–65; William A. Kahn, “Psychological

Conditions of Personal Engagement and Disengagement at Work,” Academy

of Management Journal 33, no. 4 (1990): 692–724; Tom Postmes, Russell

Spears, and Sezgin Cihangir, “Quality of Decision Making and Group

Norms,” Journal of Personality and Social Psychology 80, no. 6 (2001): 918;

Chris Argyris, “The Incompleteness of Social-Psychological Theory:

Examples from Small Group, Cognitive Consistency, and Attribution

Research,” American Psychologist 24, no. 10 (1969): 893; James R. Larson and Caryn Christensen, “Groups as Problem-Solving Units: Toward a New

Meaning of Social Cognition,” British Journal of Social Psychology 32, no. 1

(1993): 5–30; P. Wesley Schultz et al., “The Constructive, Destructive, and

Reconstructive Power of Social Norms,” Psychological Science 18, no. 5

(2007): 429–34.

put her on guard In an email sent in response to fact-checking questions, Julia Rozovsky wrote: “This is how the study group felt from time to time.

Not consistently.”

equally successful group In comments sent in response to fact-checking questions, a Google spokeswoman wrote: “We wanted to test many group

norms that we thought might be important. But at the testing phase we didn’t

know that the how was going to be more important than the who. When we

started running the statistical models, it became clear that not only were the

norms more important in our models but that 5 themes stood out from the

rest.”

Boston hospitals Amy C. Edmondson, “Learning from Mistakes Is Easier Said than Done: Group and Organizational Influences on the Detection and

Correction of Human Error,” The Journal of Applied Behavioral Science 32,

no. 1 (1996): 5–28; Druskat and Wolff, “Group Emotional Intelligence,”

132–55; David W. Bates et al., “Incidence of Adverse Drug Events and

Potential Adverse Drug Events: Implications for Prevention,” Journal of the American Medical Association 274, no. 1 (1995): 29–34; Lucian L. Leape et

al., “Systems Analysis of Adverse Drug Events,” Journal of the American

Medical Association 274, no. 1 (1995): 35–43.

“slip through the cracks” In an email sent in response to fact-checking questions, Edmondson wrote: “It’s not MY insight that mistakes occur

because of system complexity (and its challenging combination with patient

heterogeneity)….I am merely the messenger bringing that perspective to

certain audiences. But yes, the opportunities for slipping through are ever-

present, so the challenge is building awareness and teamwork that catch and

correct and prevent the slips.”

teammates behaved In an email sent in response to fact-checking questions, Edmondson wrote: “My goal was to figure out whether the interpersonal

climate that I’d found to differ in this setting would differ in other

organizations, especially in terms of differing between groups within the

same organization. Later I called this psychological safety (or team

psychological safety). I also wanted to discover whether, if it did differ,

whether that difference would be associated with differences in learning

behavior (and in performance).” For more on Edmondson’s work, please see

Amy C. Edmondson, “Psychological Safety and Learning Behavior in Work

Teams,” Administrative Science Quarterly 44, no. 2 (1999): 350–83; Ingrid M. Nembhard and Amy C. Edmondson, “Making It Safe: The Effects of

Leader Inclusiveness and Professional Status on Psychological Safety and

Improvement Efforts in Health Care Teams,” Journal of Organizational

Behavior 27, no. 7 (2006): 941–66; Amy C. Edmondson, Roderick M.

Kramer, and Karen S. Cook, “Psychological Safety, Trust, and Learning in

Organizations: A Group-Level Lens,” Trust and Distrust in Organizations:

Dilemmas and Approaches 10 (2004): 239–72; Amy C. Edmondson,

Managing the Risk of Learning: Psychological Safety in Work Teams

(Boston: Division of Research, Harvard Business School, 2002); Amy C.

Edmondson, Richard M. Bohmer, and Gary P. Pisano, “Disrupted Routines:

Team Learning and New Technology Implementation in Hospitals,”

Administrative Science Quarterly 46, no. 4 (2001): 685–716; Anita L. Tucker

and Amy C. Edmondson, “Why Hospitals Don’t Learn from Failures,”

California Management Review 45, no. 2 (2003): 55–72; Amy C.

Edmondson, “The Competitive Imperative of Learning,” Harvard Business

Review 86, nos. 7–8 (2008): 60; Amy C. Edmondson, “A Safe Harbor: Social

Psychological Conditions Enabling Boundary Spanning in Work Teams,”

Research on Managing Groups and Teams 2 (1999): 179–99; Amy C.

Edmondson and Kathryn S. Roloff, “Overcoming Barriers to Collaboration:

Psychological Safety and Learning in Diverse Teams,” Team Effectiveness in

Complex Organizations: Cross-Disciplinary Perspectives and Approaches 34

(2009): 183–208.

1999 paper Amy C. Edmondson, “Psychological Safety and Learning Behavior in Work Teams,” Administrative Science Quarterly 44, no. 2

(1999): 350–83.

her Google colleagues In an email responding to fact-checking questions, a Google spokeswoman wrote: “We found Edmondson’s papers on psych

safety very useful when trying to figure out how to cluster norms that we saw

popping up as important into meta-themes. When we reviewed the papers

about psych safety, we noticed that norms like allowing others to fail without

repercussions, respecting divergent opinions, feeling as if others aren’t trying

to undermine you are all part of psychological safety. This became one of our

five key themes, along with dependability, structure/clarity, job meaning, and

impact.”

would never stop For my understanding of the early days of Saturday Night Live, I am indebted to those writers and cast members who were willing to speak with me, as well as Tom Shales and James Andrew Miller, Live from

New York: An Uncensored History of “Saturday Night Live” (Boston: Back

Bay Books, 2008); Ellin Stein, That’s Not Funny, That’s Sick: The National

Lampoon and the Comedy Insurgents Who Captured the Mainstream (New

York: Norton, 2013); Marianne Partridge, ed., “Rolling Stone” Visits

“Saturday Night Live” (Garden City, N.Y.: Dolphin Books, 1979); Doug Hill

and Jeff Weingrad, Saturday Night: A Backstage History of “Saturday Night

Live” (San Francisco: Untreed Reads, 2011).

“never be heard from again” In an email sent in response to a fact-checking

question, Schiller wrote: “It was an intense experience for me since I had never lived in New York or worked on a comedy-variety show. A lot of us

were new to Manhattan and as such, hung out a lot together not only because

New York at that time was sort of dangerous and scary, but also we didn’t

know that many people and we were formulating the show. We were in our

midtwenties and early thirties. Yes, we’d eat at restaurants and go to bars

together even when out of the studio. We moved en masse, trying to make

each other laugh.”

“among the show’s cast” Malcolm Gladwell, “Group Think: What Does Saturday Night Live Have in Common with German Philosophy?” The New

Yorker, December 2, 2002.

team intensely bonds Donelson Forsyth, Group Dynamics (Boston: Cengage Learning, 2009).

“It was a stalag” Alison Castle, “Saturday Night Live”: The Book (Reprint, Cologne: Taschen, America, 2015).

“someone else was failing” In an email sent in response to a fact-checking question, Beatts wrote: “My Holocaust joke, which was certainly said in jest

because there is no other way to say a joke, had nothing whatsoever to do

with the show’s writers. The exact wording was ‘Imagine if Hitler hadn’t

killed six million Jews, how hard it would be to find an apartment in New

York.’ It was a joke about the difficulty of finding apartments in New York,

riffing off New York’s large Jewish population and general ethnic feeling, a

la ‘You don’t have to be Jewish to love Levy’s rye bread. But it wouldn’t

hurt.’ Zero to do with the writers. Marilyn Miller took offense at the mere

mention of Hitler and the Holocaust, which to her could not be a subject for

comedy….[Regarding] competition among the writers, not that it didn’t exist,

because it did, but…everyone always had a chance to come back swinging

the following week. Also the other writers and everyone in general, despite

the competition for airtime, Lorne’s approval, audience appreciation, etc.,

were always very supportive of other people’s efforts and sympathetic to

each other’s failures. No one went around rubbing their hands in glee and

going haha, your sketch was cut and mine wasn’t, so there! It was more an

attitude of ‘Better luck next time.’ I think everyone felt part of a family, maybe a dysfunctional family, but a close-knit family all the same. I would

say that there is more backstabbing and jealousy and rivalry and competition

and cliqueishness on the average middle school playground than there ever

was at SNL during the time I was there.”

“stuff for other people” In an email sent in response to fact-checking questions, Alan Zweibel wrote: “I wasn’t angry because of anything to do

with that character or the process in which it was written. She and I weren’t

speaking for reasons that I really can’t recall. But after about three shows

where I didn’t write with her (and for her) we both realized that our work was

suffering—that we were better as a team than we were individually—so we

buried the hatchet and began collaborating again.”

“it could be brutal” In an email sent in response to a fact-checking question, Schiller wrote: “I would say that some, not all, comedy writers and stand-up

comedians have some sadness or anger in their life that helped fuel their

comedy. They are fast with quips, and the stand-ups were used to hecklers

and had to be prepared with a quick comeback. So just as much as they can

say something sharply funny, they can also jab you with a quick, hostile (but

also funny) remark….The atmosphere at SNL, although we all liked each

other, could become highly competitive based on the fact that there were 10

writers and only so many sketches could go on the show, so we all did our

best to write the winning sketch or make (in my case) the best short film.”

58 percent The correct answers to the quiz are upset, decisive, skeptical, and cautious. These images come from Simon Baron-Cohen et al., “Another

Advanced Test of Theory of Mind: Evidence from Very High Functioning

Adults with Autism or Asperger Syndrome,” Journal of Child Psychology

and Psychiatry 38, no. 7 (1997): 813–22. And Simon Baron-Cohen et al.,

“The ‘Reading the Mind in the Eyes’ Test Revised Version: A Study with

Normal Adults, and Adults with Asperger Syndrome or High-Functioning

Autism,” Journal of Child Psychology and Psychiatry 42, no. 2 (2001): 241–

51.

Science in 2010 Anita Williams Woolley et al., “Evidence for a Collective

Intelligence Factor in the Performance of Human Groups,” Science 330, no.

6004 (2010): 686–88.

“individuals in it” Anita Woolley and Thomas Malone, “What Makes a Team Smarter? More Women,” Harvard Business Review 89, no. 6 (2011):

32–33; Julia B. Bear and Anita Williams Woolley, “The Role of Gender in

Team Collaboration and Performance,” Interdisciplinary Science Reviews 36,

no. 2 (2011): 146–53; David Engel et al., “Reading the Mind in the Eyes or

Reading Between the Lines? Theory of Mind Predicts Collective Intelligence

Equally Well Online and Face-to-Face,” PloS One 9, no. 12 (2014); Anita Williams Woolley and Nada Hashmi, “Cultivating Collective Intelligence in

Online Groups,” in Handbook of Human Computation, ed. Pietro Michelucci

(New York: Springer, 2013), 703–14; Heather M. Caruso and Anita Williams

Woolley, “Harnessing the Power of Emergent Interdependence to Promote

Diverse Team Collaboration,” Research on Managing Groups and Teams:

Diversity and Groups 11 (2008): 245–66; Greg Miller, “Social Savvy Boosts

the Collective Intelligence of Groups,” Science 330, no. 6000 (2010): 22; Anita Williams Woolley et al., “Using Brain-Based Measures to Compose

Teams: How Individual Capabilities and Team Collaboration Strategies

Jointly Shape Performance,” Social Neuroscience 2, no. 2 (2007): 96–105;

Peter Gwynne, “Group Intelligence, Teamwork, and Productivity,” Research

Technology Management 55, no. 2 (2012): 7.

University of Cambridge Baron-Cohen et al., “ ‘Reading the Mind in the Eyes’ Test Revised Version,” 241–51.

“more initials he sees” In an email sent in response to fact-checking questions, Alan Zweibel wrote: “[Michaels] had said that he likes when

there’s a lot of initials at the top of the page because it meant that it had a variety of input and sensibilities. I believe that the show has lasted 40 years

because Lorne is a genius when it comes to recognizing talent, rolling with

the changing times, and encouraging everyone (while developing their

individual voices) to work with each other so the total is greater than the sum

of its parts.”

“the pain!” In the script that made it to air, O’Donoghue says, “ ‘I know I

can! I know I can! I know I can! I know I can! Heart attack! Heart attack!

Heart attack! Heart attack! Oh, my God, the pain! Oh, my God, the pain! Oh,

my God, the pain!” It is worth noting that the original concept for depressing

children stories originated with O’Donoghue, not Garrett.

CHAPTER THREE: FOCUS

bound for Paris For my understanding of the details of Air France Flight 447, I am indebted to numerous experts, including William Langewiesche,

Steve Casner, Christopher Wickens, and Mica Endsley. I also drew heavily

on a number of publications: William Langewiesche, “The Human Factor,”

Vanity Fair, October 2014; Nicola Clark, “Report Cites Cockpit Confusion in

Air France Crash,” The New York Times, July 6, 2012; Nicola Clark,

“Experts Say Pilots Need More Air Crisis Training,” The New York Times, November 21, 2011; Kim Willsher, “Transcripts Detail the Final Moments of

Flight from Rio,” Los Angeles Times, October 16, 2011; Nick Ross and Neil

Tweedie, “Air France Flight 447: ‘Damn It, We’re Going to Crash,’ ” The

Daily Telegraph, May 1, 2012; “Air France Flight 447: When All Else Fails,

You Still Have to Fly the Airplane,” Aviation Safety, March 1, 2011;

“Concerns over Recovering AF447 Recorders,” Aviation Week, June 3, 2009;

Flight Crew Operating Manual, Airbus 330— Systems—Maintenance System;

Tim Vasquez, “Air France Flight 447: A Detailed Meteorological Analysis,”

Weather

Graphics,

June

3,

2009,

http://www.weathergraphics.com/tim/af447/;

Cooperative

Institute

for

Meteorological Satellite Studies, “Air France Flight #447: Did Weather Play

a Role in the Accident?” CIMSS Satellite Blog, June 1, 2009,

http://cimss.ssec.wisc.edu/goes/blog/archives/2601; Richard Woods and Matthew Campbell, “Air France 447: The Computer Crash,” The Times, June

7, 2009; “AF 447 May Have Come Apart Before Crash,” Associated Press,

June 3, 2009; Wil S. Hylton, “What Happened to Air France Flight 447?”

The New York Times Magazine, May 4, 2011; “Accident Description F-

GZC,” Flight Safety Foundation, Web; “List of Passengers Aboard Lost Air

France Flight,” Associated Press, June 4, 2009; “Air France Jet ‘Did Not

Break Up in Mid-Air,’ Air France Crash: First Official Airbus A330 Report

Due by Air Investigations and Analysis Office,” Sky News, July 2, 2009; Matthew Wald, “Clues Point to Speed Issues in Air France Crash,” The New

York Times, June 7, 2009; Air France, “AF 447 RIO-PARIS-CDG, Pitot

Probes,” October 22, 2011, http://corporate.airfrance.com/en/press/af-447-

rio-paris-cdg/pitot-probes/; Edward Cody, “Airbus Recommends Airlines

Replace Speed Sensors,” The Washington Post, July 31, 2009; Jeff Wise,

“What Really Happened Aboard Air France 447,” Popular Mechanics,

December 6, 2011; David Kaminski-Morrow, “AF447 Stalled but Crew

Maintained Nose-Up Attitude,” Flight International, May 27, 2011; David

Talbot, “Flight 447’s Fatal Attitude Problem,” Technology Review, May 27,

2011; Glenn Pew, “Air France 447—How Did This Happen?” AVweb, May

27, 2011; Bethany Whitfield, “Air France 447 Stalled at High Altitude,

Official BEA Report Confirms,” Flying, May 27, 2011; Peter Garrison, “Air

France 447: Was It a Deep Stall?” Flying, June 1, 2011; Gerald Traufetter,

“Death in the Atlantic: The Last Four Minutes of Air France Flight 447,”

Spiegel Online, February 25, 2010; Nic Ross and Jeff Wise, “How Plane

Crash Forensics Lead to Safer Aviation,” Popular Mechanics, December 18,

2009; Interim Report on the Accident on 1 June 2009 to the Airbus A330-203

Registered F-GZCP Operated by Air France Flight AF 447 Rio de Janeiro–

Paris (Paris: Bureau d’Enquêtes et d’Analyses pour la sécurité de l’aviation

civile [BEA], 2012); Interim Report No. 3 on the Accident on 1 June 2009 to

the Airbus A330-203 registered F-GZCP Operated by Air France Flight AF

447 Rio de Janeiro–Paris (Paris: BEA, 2011); Final Report on the Accident

on 1st June 2009 to the Airbus A330-203 Registered F-GZCP Operated by

Air France Flight AF 447 Rio de Janeiro–Paris (Paris: BEA, 2012);

“Appendix 1 to Final Report on the Accident on 1st June 2009 to the Airbus

A330-203 Registered F-GZCP Operated by Air France Flight AF 447 Rio de

Janeiro–Paris” (Paris: BEA, July 2012); Lost: The Mystery of Flight 447, BBC One, June 2010; “Crash of Flight 447,” Nova, 2010, produced by

Nacressa Swan; “Air France 447, One Year Out,” Nova, 2010, produced by

Peter Tyson.

flying them home Air France has argued that it is inappropriate to blame pilot error as the primary cause for the crash of Flight 447. (This perspective

is disputed by numerous aviation experts.) Air France was presented with a

complete list of questions regarding details discussed in this chapter. The

airline declined to comment on issues that fell outside of those topics

discussed in the official report regarding Air France Flight 447 published by

the Bureau d’Enquêtes et d’Analyses pour la sécurité de l’aviation civile, or

BEA, which is the French authority responsible for investigating aviation accidents. In a statement, a spokesman for Air France wrote: “It is essential to

remember that the BEA investigation report, the only official and public

investigation to date, discusses and develops many of the subjects mentioned

[in this chapter]. This report is available on the BEA website in English. We

can only direct the journalist to this report to supplement our answers.”

rotated responsibilities In response to questions, a spokesman for Air France noted that automation on long-haul aircraft preceded the A330 by

twenty years, and that at one time “the crew included a flight engineer, who

was responsible for monitoring all aircraft systems during the flight. On

modern aircraft, the flight engineer has disappeared, but the requirement of

monitoring aircraft systems remains. This is carried out by the pilots. Finally,

now as in the past, beyond a certain flight time the crew is reinforced by one

or more additional pilots to enable each pilot to take a rest period.”

crashed after takeoff Isabel Wilkerson, “Crash Survivor’s Psychic Pain May Be the Hardest to Heal,” The New York Times, August 22, 1987; Mike

Householder, “Survivor of 1987 Mich. Plane Crash Breaks Silence,”

Associated Press, May 15, 2013.

One hundred and one people Ninety-nine people were killed instantly in this crash. Two later died from complications.

into the Everglades Ken Kaye, “Flight 401 1972 Jumbo Jet Crash Was Worst Aviation Disaster in State History,” Sun Sentinel, December 29, 1992.

other human errors Aviation Safety Network, NTSB records.

ascended by three thousand feet In response to questions, a spokesman for Air France wrote: “It has not been shown by the BEA that the action to pitch

up is the result of the pilot’s actions faced with the rolling of the aircraft, but

rather the loss of altitude read, the vertical speed on descent of 600 ft per minute, the noise, the pitch that had diminished during the seconds before

etc.”

said Bonin In response to questions, a spokesman for Air France wrote:

“What is written is true, but does not throw light comprehensively on this phase because of the lack of some essential elements, such as the fact that the

STALL alarm went off twice at the beginning of the incident which may have

led the pilots to doubt its validity when it went off repeatedly. The BEA

report stated that audio alarms are not ‘unmissable’ and that on the contrary

they are often the first to be ignored.”

watching the kids Zheng Wang and John M. Tchernev, “The ‘Myth’ of Media Multitasking: Reciprocal Dynamics of Media Multitasking, Personal

Needs, and Gratifications,” Journal of Communication 62, no. 3 (2012): 493–

513; Daniel T. Willingham, Cognition: The Thinking Animal, 3rd ed. (Upper

Saddle River, N.J.: Pearson, 2007).

by automation Juergan Kiefer et al., “Cognitive Heuristics in Multitasking Performance,” Center of Human-Machine Systems, Technische Universität

Berlin,

2014,

http://www.prometei.de/fileadmin/prometei.de/publikationen/Kiefer_eurocogsci2007.pdf

automaticity and focus Barnaby Marsh et al., “Cognitive Heuristics: Reasoning the Fast and Frugal Way,” in The Nature of Reasoning, eds. J. P.

Leighton and R. J. Sternberg (New York: Cambridge University Press, 2004);

“Human

Performance,”

Aerostudents,

http://aerostudents.com/files/humanMachineSystems/humanPerformance.pdf.

misstep can be tragic For more on this topic, I particularly recommend Martin Sarter, Ben Givens, and John P. Bruno, “The Cognitive Neuroscience

of Sustained Attention: Where Top-Down Meets Bottom-Up,” Brain

Research Reviews 35, no. 2 (2001): 146–60; Michael I. Posner and Steven E.

Petersen, “The Attention System of the Human Brain,” Annual Review of

Neuroscience 13, no. 1 (1990): 25–42; Eric I. Knudsen, “Fundamental

Components of Attention,” Annual Review of Neuroscience 30 (2007): 57–

78; Steven E. Petersen and Michael I. Posner, “The Attention System of the

Human Brain: 20 Years After,” Annual Review of Neuroscience 35 (2012):

73; Raja Parasuraman, Robert Molloy, and Indramani L. Singh,

“Performance Consequences of Automation-Induced ‘Complacency,’ ” The

International Journal of Aviation Psychology 3, no. 1 (1993): 1–23;

Raymond S. Nickerson et al., Handbook of Applied Cognition, ed. Francis T.

Durso (Hoboken, N.J.: Wiley, 2007); Christopher D. Wickens, “Attention in

Aviation,” University of Illinois at Urbana-Champaign Institute of Aviation,

Research

Gate,

February

1987,

http://www.researchgate.net/publication/4683852_Attention_in_aviation;

Christopher D. Wickens, “The Psychology of Aviation Surprise: An 8 Year

Update Regarding the Noticing of Black Swans,” Proceedings of the 15th

International Symposium on Aviation Psychology, 2009.

critical than ever before Ludwig Reinhold Geissler, “The Measurement of Attention,” The American Journal of Psychology (1909): 473–529; William

A. Johnston and Steven P. Heinz, “Flexibility and Capacity Demands of

Attention,” Journal of Experimental Psychology: General 107, no. 4 (1978):

420; Robin A. Barr, “How Do We Focus Our Attention?” The American

Journal of Psychology (1981): 591–603.

panicked attention G. R. Dirkin, “Cognitive Tunneling: Use of Visual Information Under Stress,” Perceptual and Motor Skills 56, no. 1 (1983): 191–98; David C. Foyle, Susan R. Dowell, and Becky L. Hooey, “Cognitive

Tunneling in Head-Up Display (HUD) Superimposed Symbology: Effects of

Information Location” (2001); Adrien Mack and Irvin Rock, Inattentional

Blindness (Cambridge, Mass.: MIT Press, 2000); Steven B. Most, Brian J.

Scholl, Daniel J. Simons, and Erin R. Clifford, “What You See Is What You

Get: Sustained Inattentional Blindness and the Capture of Awareness,”

Psychological Review 112, no. 1 (2005): 217–42; Daniel J. Simons,

“Attentional Capture and Inattentional Blindness,” Trends in Cognitive

Sciences 4, no. 4 (2000): 147–55; Gustav Kuhn and Benjamin W. Tatler,

“Misdirected by the Gap: The Relationship Between Inattentional Blindness

and Attentional Misdirection,” Consciousness and Cognition 20, no. 2

(2011): 432–36; William J. Horrey and Christopher D. Wickens, “Examining

the Impact of Cell Phone Conversations on Driving Using Meta-Analytic

Techniques,” Human Factors: The Journal of the Human Factors and

Ergonomics Society 48, no. 1 (2006): 196–205.

red light ahead G. D. Logan, “An Instance Theory of Attention and Memory,” Psychological Review 109 (2002): 376–400; D. L. Strayer and F.

A. Drews, “Attention,” Handbook of Applied Cognition, ed. Francis T. Durso (Hoboken, N.J.: Wiley, 2007); A. D. Baddeley, “Selective Attention and

Performance in Dangerous Environments,” British Journal of Psychology 63

(1972): 537–46; E. Goldstein, Cognitive Psychology: Connecting Mind,

Research and Everyday Experience (Independence, Ky.: Cengage Learning,

2014).

of common sense In response to a fact-checking email, Strayer expanded his comments: “With automated systems, we may not focus or concentrate

attention on the task—we even mind wander in boring or repetitive settings.

It takes effort to concentrate attention and this can lead to high levels of mental workload and we see a ‘vigilance decrement’ where attention lapses

(and we make errors and miss critical events). This is often the case with

monitoring tasks (keep an eye on the autonomous system) and when things

go awry we may not notice or react on autopilot (even if this is not the correct

action—we refer to this as slips where autopilot took over).”

gauges and controls Airbus, Airbus A330 Aircraft Recovery Manual Airbus, 2005,

http://www.airbus.com/fileadmin/media_gallery/files/tech_data/ARM/ARM_A330_20091101.pdf

throughout the flight The automatic warning system of this A330 was programmed so that the stall warning would cease when the plane’s stall was

most severe. In some situations, when the pitch attitude was too high and the

airflow into the pitot tubes too low, the computer assumed the data it was

gathering was erroneous. So it sounded no alarms. Thus, a perverse situation

arose for Flight 447 after the pitot tubes thawed: At times, when Bonin did

something to make the stall worse, the alarm stopped. The computers worked

as programmed, but the result was information that might have been

confusing to the pilots.

“reactive thinking” Koji Jimura, Maria S. Chushak, and Todd S. Braver,

“Impulsivity and Self-Control During Intertemporal Decision Making Linked

to the Neural Dynamics of Reward Value Representation,” The Journal of

Neuroscience 33, no. 1 (2013): 344–57; Ayeley P. Tchangani, “Modeling for

Reactive Control and Decision Making in Uncertain Environment,” in

Control and Learning in Robotic Systems, ed. John X. Liu (New York: Nova Science Publishers, 2005), 21–58; Adam R. Aron, “From Reactive to

Proactive and Selective Control: Developing a Richer Model for Stopping

Inappropriate Responses,” Biological Psychiatry 69, no. 12 (2011): 55–68;

Veit Stuphorn and Erik Emeric, “Proactive and Reactive Control by the

Medial Frontal Cortex,” Frontiers in Neuroengineering 5 (2012): 9; Todd S.

Braver et al., “Flexible Neural Mechanisms of Cognitive Control Within

Human Prefrontal Cortex,” Proceedings of the National Academy of Sciences

106, no. 18 (2009): 7351–56; Todd S. Braver, “The Variable Nature of

Cognitive Control: A Dual Mechanisms Framework.” Trends in Cognitive

Sciences 16, no. 2 (2012): 106–13; Yosuke Morishima, Jiro Okuda, and

Katsuyuki Sakai, “Reactive Mechanism of Cognitive Control System,”

Cerebral Cortex 20, no. 11 (2010) 2675–83; Lin Zhiang and Kathleen

Carley, “Proactive or Reactive: An Analysis of the Effect of Agent Style on

Organizational Decision Making Performance,” Intelligent Systems in

Accounting, Finance and Management 2, no. 4 (1993): 271–87.

the psychologist, in 2009 Joel M. Cooper et al., “Shifting Eyes and Thinking Hard Keep Us in Our Lanes,” Human Factors and Ergonomics Society

Annual Meeting Proceedings 53, no. 23 (2009): 1753–56. For more on this topic, please see Frank A. Drews and David L. Strayer, “Chapter 11: Cellular

Phones and Driver Distraction,” in Driver Distraction: Theory, Effects, and

Mitigation, ed. Michael A. Regan, John D. Lee, and Kristie L. Young (Boca

Raton, Fla.: CRC Press, 2008): 169–90; Frank A. Drews, Monisha Pasupathi,

and David L. Strayer, “Passenger and Cell Phone Conversations in Simulated

Driving,” Journal of Experimental Psychology: Applied 14, no. 4 (2008):

392; Joel M. Cooper, Nathan Medeiros-Ward, and David L. Strayer, “The

Impact of Eye Movements and Cognitive Workload on Lateral Position

Variability in Driving,” Human Factors: The Journal of the Human Factors

and Ergonomics Society 55, no. 5 (2013): 1001–14; David B. Kaber et al.,

“Driver Performance Effects of Simultaneous Visual and Cognitive

Distraction and Adaptation Behavior,” Transportation Research Part F:

Traffic Psychology and Behaviour 15, no. 5 (2012): 491–501; I. J. Faulks et

al., “Update on the Road Safety Benefits of Intelligent Vehicle Technologies

—Research in 2008–2009,” 2010 Australasian Road Safety Research,

Policing and Education Conference, August 31–September 3, 2010,

Canberra, Australia.

announcement of any kind In a fact-checking conversation, Stephen Casner, a research psychologist at NASA, said that if a plane was falling at ten

thousand–plus feet per minute, the g-force would be pretty close to 1, and as

a result, it would be unlikely the passengers would have noticed that anything

was amiss. However, he added, “Actually, no one knows what that feels like.

Everyone who has felt what it’s like to lose 10,000 feet a minute dies pretty

soon after feeling it.”

ten thousand feet per minute In response to questions, a spokesman for Air France wrote: “A fundamental aspect is that the STALL alarm stopped when

the speed fell below 60 kts, leading the pilots to think they were out of the stall. Especially that every time they pushed on the stick to try and get out of

the stall situation, the STALL alarm started to work again, leading them to

cancel their pitching action! Also, during the last phase, vertical speed

indications were unstable, adding doubt and confusion in the pilots’ minds.”

Dayton, near where she lived In an email sent in reply to a fact-checking inquiry, Crandall wrote: “In 1986, I began working with Dr. Gary Klein at his

company Klein Associates Inc. The work you mention with firefighters and

military commanders had already begun when I joined the company. It

continued for many years, expanding well beyond firefighting and military

command and control, and was carried out by Gary and the Klein Associates

research team (who were an amazing bunch of very smart talented quirky

people). I had both research and management positions at Klein Associates,

and I was involved in some of those studies, not in others. As owner and

Chief Scientist, Gary led our efforts to describe how (some) people are able

to ‘keep their heads in chaotic environments’ and particularly how (some)

people are able to make effective decisions under conditions of stress, risk,

and time pressure….It is correct that in the interviews we conduct, when

asked about decision making and how a person knew to do X in a particular

situation, they often respond with, ‘experience’ or ‘gut feel’ or ‘intuition’ or

‘I just knew.’…These accounts of an intuitive basis for decision making

became a cornerstone of our research efforts….The studies we did in the

NICU confirmed what we were finding in other work domains—highly

experienced, highly skilled personnel become very good at paying attention to what’s most important (the critical cues) in a given situation, and not

getting distracted by less important information….Over time and repeated

experience with similar situations, they learn what matters and what doesn’t.

They learn to size up a situation very quickly and accurately. They see

connections across various cues (clusters; packages; linkages) that form a

meaningful pattern. Some people refer to this as a gestalt, and others as

‘mental models’ or schemas.” For more details, please see Beth Crandall and

Karen Getchell-Reiter, “Critical Decision Method: A Technique for Eliciting

Concrete Assessment Indicators from the Intuition of NICU Nurses,”

Advances in Nursing Science 16, no. 1 (1993): 42–51; B. Crandall and R.

Calderwood, “Clinical Assessment Skills of Experienced Neonatal Intensive

Care Nurses,” Contract 1 (1989): R43; B. Crandall and V. Gamblian, “Guide

to Early Sepsis Assessment in the NICU,” Instruction Manual Prepared for

the Ohio Department of Development Under the Ohio SBIR Bridge Grant

Program (Fairborn, Ohio: Klein Associates, 1991).

“a whole picture” In an email sent in reply to a fact-checking inquiry, Crandall wrote: “The other nurse was a preceptee—in training to provide

nursing care in a NICU. Darlene was her preceptor—helping her learn and

providing oversight and guidance as she learns how to care for premature

babies. So, the baby WAS Darlene’s responsibility in the sense that she was

supervising/precepting the nurse caring for the baby. You are correct, she

noticed that the baby didn’t look ‘good.’ Here is the incident account that we

wrote up based on our interview notes: ‘When this incident took place, I was

teaching, serving as a preceptor for a new nurse. We had been working

together for quite awhile and she was nearing the end of her orientation, so she was really doing primary care and I was in more of a supervisory

position. Anyway, we were nearing the end of a shift and I walked by this

particular isolette and the baby really caught my eye. The baby’s color was

off and its skin was mottled. Its belly looked slightly rounded. I looked at the

chart and it indicated the baby’s temp was unstable. I also noticed that the baby had had a heel stick for lab work several minutes ago and the stick was

still bleeding. When I asked my orientee how she thought the baby was

doing, she said that he seemed kind of sleepy to her. I went and got the

Doctor immediately and told him we were “in big trouble” with this baby. I

said the baby’s temp was unstable, that its color was funny, it seemed lethargic and it was bleeding from a heel stick. He reacted right away, put the

baby on antibiotics and ordered cultures done. I was upset with the orientee

that she had missed these cues, or that she had noticed them but not put them

together. When we talked about it later I asked about the baby’s temp

dropping over four readings. She had noticed it, but had responded by

increasing the heat in the isolette. She had responded to the ‘surface’

problem, instead of trying to figure out what might be causing the problem.”

“creating mental models” Thomas D. LaToza, Gina Venolia, and Robert DeLine, “Maintaining Mental Models: A Study of Developer Work Habits,”

Proceedings of the 28th International Conference on Software Engineering

(New York: ACM, 2006); Philip Nicholas Johnson-Laird, “Mental Models

and Cognitive Change,” Journal of Cognitive Psychology 25, no. 2 (2013): 131–38; Philip Nicholas Johnson-Laird, How We Reason (Oxford: Oxford

University Press, 2006); Philip Nicholas Johnson-Laird, Mental Models,

Cognitive Science Series, no. 6 (Cambridge, Mass.: Harvard University

Press, 1983); Earl K. Miller and Jonathan D. Cohen, “An Integrative Theory

of Prefrontal Cortex Function,” Annual Review of Neuroscience 24, no. 1

(2001): 167–202; J. D. Sterman and D. V. Ford, “Expert Knowledge

Elicitation to Improve Mental and Formal Models,” Systems Approach to

Learning and Education into the 21st Century, vol. 1, 15th International System Dynamics Conference, August 19–22, 1997, Istanbul, Turkey; Pierre

Barrouillet, Nelly Grosset, and Jean-François Lecas, “Conditional Reasoning

by Mental Models: Chronometric and Developmental Evidence,” Cognition

75, no. 3 (2000): 237–66; R. M. J. Byrne, The Rational Imagination: How

People Create Alternatives to Reality (Cambridge, Mass.: MIT Press, 2005);

P. C. Cheng and K. J. Holyoak, “Pragmatic Reasoning Schemas,” in

Reasoning: Studies of Human Inference and Its Foundations, eds. J. E. Adler

and L. J. Rips (Cambridge: Cambridge University Press, 2008), 827–42;

David P. O’Brien, “Human Reasoning Includes a Mental Logic,” Behavioral

and Brain Sciences 32, no. 1 (2009): 96–97; Niki Verschueren, Walter

Schaeken, and Gery d’Ydewalle, “Everyday Conditional Reasoning: A

Working Memory–Dependent Tradeoff Between Counterexample and

Likelihood Use,” Memory and Cognition 33, no. 1 (2005): 107–19.

the child’s bassinet In response to a fact-checking email, Crandall wrote:

“The key to this story (for me anyway) is that experts see meaningful patterns

that novices miss altogether. As an experienced NICU nurse, Darlene has

seen hundreds of babies. She is not reflecting on all of them…they have

merged into a sense of what is typical for a premie baby at X weeks. She has

also seen many babies with sepsis (it happens a lot in NICUs, for a variety of

reasons unrelated to quality of care). The combination of cues (bloody

bandaid, falling temp, distended belly, sleepiness/lethargy) brought with it

the recognition ‘this baby is in trouble’ and ‘probably septic.’ At least, that’s

what she told us in the interview….I agree that people often create narratives

to help explain what’s going on around them, and help them make sense—

particularly when they are having trouble figuring something out. In this

incident, Darlene was not having trouble figuring out what was going on—

she recognized immediately what was going on….I think of Darlene’s story

as being about expertise, and the difference between how experts and novices

view and understand a given situation….Storytelling takes time, and stories

are linear (this happened, then this, and then that). When experienced people

describe events such as this one, what happens is very fast: They ‘read’ the situation, they understand what’s going on, and they know what to do.”

“It’s even harder now” In response to a fact-checking email, Casner expanded his comments: “I wouldn’t say that pilots are ‘passive’ but that they

find it exceedingly difficult to maintain their attention on an automated

system that works so reliably well. Humans are not good at sitting and

staring….Humans have limited attentional resources (e.g., how our kids do

stuff behind our backs and get away with it). So we have to keep our

attention pointed in the direction that we think is most important at all times.

If a cockpit computer in front of me has worked impeccably for 100 hours in

a row, it’s hard to envision that as being the most important thing to think about. For example, my kid could be getting away with some insane stuff at

that very moment. In our study of mind wandering among pilots [ Thoughts in

Flight: Automation Use and Pilots’ Task-Related and Task-Unrelated

Thought], we found that the pilot flying was thinking ‘task-unrelated

thoughts’ about 30% of the time. The other pilot, the monitoring pilot, was

mind wandering about 50% of the time. Why wouldn’t they? If you don’t

give me something important or pressing to think about, I’ll come up with

something myself.”

people build mental models Sinan Aral, Erik Brynjolfsson, and Marshall Van Alstyne, “Information, Technology, and Information Worker

Productivity,” Information Systems Research 23, no. 3 (2012): 849–67; Sinan

Aral and Marshall Van Alstyne, “The Diversity-Bandwidth Trade-Off,”

American Journal of Sociology 117, no. 1 (2011): 90–171; Nathaniel Bulkley

and Marshall W. Van Alstyne, “Why Information Should Influence

Productivity” (2004); Nathaniel Bulkley and Marshall W. Van Alstyne, “An

Empirical Analysis of Strategies and Efficiencies in Social Networks,”

Boston U. School of Management research paper no. 2010-29, MIT Sloan

research

paper

no.

4682-08,

February

1,

2006,

http://ssrn.com/abstract=887406; Neil Gandal, Charles King, and Marshall Van Alstyne, “The Social Network Within a Management Recruiting Firm:

Network Structure and Output,” Review of Network Economics 8, no. 4

(2009): 302–24.

leveraged existing skills In response to a fact-checking email, Van Alstyne expanded upon his comments: “One of the original hypotheses attributed the

gains of the smaller project load to the efficacy associated with economies of

specialization. Doing a singular, focused activity can make you very good at

that activity. The idea goes all the way back to Adam Smith and the

efficiency associated with focused tasks at a pin factory. Generalization, or

pursuing diverse work in our context, meant spreading projects across

finance, education, and commercial IT. These are very different industries.

Running projects across them requires different knowledge and it also means

tapping different social networks. Specialization, in these consulting projects,

meant focusing on, say, just the finance projects. Knowledge could be

deepened within this focal area and the social network could be adapted to

finance contacts alone. At least this is one theory as to why specialization

might be better. Obviously, specialization can restrict the number of possible

projects—there might not be a new finance project when there does happen

to be one, or several, in education or IT. But perhaps if you wait, you’ll get

another finance project.”

deemed a success In response to a fact-checking email, Van Alstyne

identified other reasons why joining small numbers of projects, and a project at its start, had benefits: “The first is multitasking. Initially, taking on new projects strictly increases output, in this case revenues generated by these

consultants. Revenue growth can continue even past the point where the

productivity on a given project starts to fall. Consider a project as a collection

of tasks (assessing client needs, generating target candidates, selecting

candidates, vetting resumes, presenting options to clients, closing the

deal…).As a person takes on new work, its tasks displace some tasks of the

existing work. So an existing project can take longer when a person takes on

a new project, drawing out the period over which he/she gets paid. Total

throughput, however, can still rise for awhile as a person takes on new

projects. The stream of revenues brought in by a person juggling 6 projects

tends to be higher than the stream of revenues brought in by a person

juggling 4 even though each of the 6 projects takes longer than it would have

taken if it were only in a group of 4. At some point, however, this

relationship trends completely downward. New projects take too long and

revenues decline. Taking on another project strictly decreases productivity.

As one consultant put it, ‘There are too many balls in the air and then too many get dropped.’ It takes too long to complete tasks, some tasks are not

completed at all, and the flow of revenues dribbles out over a really long

period. So there is an optimal number of projects to take on and this is below

12. The second consideration, as you suggest, is access to rich information.

This exhibits a similar invert-U pattern. We were able to judge how much

novel information each person received by tracking their actual email

communication. We measured this both in a sense of ‘variance,’ i.e., how

unusual was a fact relative to other received facts, and also in terms of

‘volume,’ i.e., how many new facts a person received….Initially, greater access to more novel information strictly increased productivity. Superstars

did receive about 25% more novel information than their typical peer and this

access to novelty helped predict their success. Eventually, however, those

outlying people who received the absolute highest novelty—about twice that

of the superstars—were less productive than the superstars. Either excess

information was too weird, off-topic, and not actionable or excess

information was too much to process. A massive volume of novelty

introduces the white-collar worker’s equivalent of the ‘Where’s Waldo’

problem: You can’t find the important information in all the noise. Both of

these factors were statistically significant predictors of the superstars.”

bright morning sky Richard De Crespigny, QF32 (Sydney: Pan Macmillan Australia, 2012); Aviation Safety Investigation Report 089: In-Flight

Uncontained Engine Failure Airbus A380–842, VH-OQA (Canberra:

Australian Transport Safety Bureau, Department of Transport and Regional

Services, 2013); Jordan Chong, “Repaired Qantas A380 Arrives in Sydney,”

The Sydney Morning Herald, April 22, 2012; Tim Robinson, “Qantas QF32

Flight from the Cockpit,” The Royal Aeronautical Society, December 8,

2010; “Qantas Airbus A380 Inflight Engine Failure,” Australian Transport

Safety Bureau, December 8, 2010; “Aviation Occurrence Investigation AO-

2010–089 Interim-Factual,” Australian Transport Safety Bureau, May 18,

2011; “In-Flight Uncontained Engine Failure—Overhead Batam Island,

Indonesia, November 4, 2010, VH-OQA, Airbus A380–842,” Australian

Transport Safety Bureau, investigation no. AO-2010–089, Sydney.

de Crespigny later told me I am indebted to Captain de Crespigny for his time as well as his book, QF32. In an interview, de Crespigny emphasized that he is speaking for himself, and not for Qantas, in recalling and describing

these events.

“models they can use” In response to a fact-checking email, Burian expanded upon her comments and said that her comments should be read in

the light of “shifting focus from what was wrong/malfunctioning/not

available to what was working/functioning/available was a turning point. I

spoke of how this happened for him in this specific situation but generalized

to how this shift in mindset has been found to be quite helpful to pilots,

particularly when faced with multiple failure conditions….Modern aircraft

are highly technically advanced and their system designs are tightly coupled

and fairly opaque. This can make it quite difficult for pilots to understand the

whys and wherefores of some malfunctions and how multiple malfunctions

might be associated with each other. Instead of trying to sort through a

myriad of malfunctions and think about how they are connected and the

implications they have, shifting focus to an aircraft’s capabilities simplifies

the cognitive demands and can facilitate deciding how to do what is needing

to be done….Once a critical event has occurred, really good pilots do several

things—they try to determine what is most critical to be dealt with first (narrowing of attention) but also pull back from time to time (broadening of

attention) to do two things: 1) make sure they are not missing

cues/information that might contradict or alter their understanding of their

situation and 2) track the overall situation as part of their assessment of the most critical things to be attending to. For example, consider a catastrophic

emergency (requiring an emergency landing/ditching) that occurs at cruise

altitude. The crew will have some time to deal with the condition, but at some

point, their attention should shift from dealing directly with the

malfunction/condition to preparing for and executing a ditching/landing.

Good pilots are constantly assessing the actions being taken, their efficacy,

and needed actions relative to the overall status of the aircraft and phase of flight. Of course, good pilots also fully enlist the help of others in doing all

this (i.e., good CRM). Good pilots also do a lot of ‘what if’ exercises before

any event occurs, mentally running through a variety of scenarios to think

about what they might do, how the situation might unfold, circumstances that

would alter the way(s) in which they would respond, etc. General aviation

pilots are taught to do something similar during flight when they say to

themselves at various points along their route ‘If I were to lose my (only)

engine right now (i.e., engine dies), where would I land?’ ”

“land the plane” In response to a fact-checking email, de Crespigny expanded upon his comments: “Dave used [an onboard computer] program to

check the landing distance. His first pass resulted in NO SOLUTION because

there were too many failures for the program to come up with a landing

solution. Dave then simplified the entries for the failures. The LDPA program

[the landing distance performance application] then displayed a landing

distance margin of just 100 metres. Whilst Dave and the others were

calculating the performance (that turned out to be incorrect anyways because

of errors in the LDPA program and more extensive aircraft (brakes) damage

than what was reported), I kept a broad situation awareness of the entire

operation: aircraft, fuel, critical paths, pilot duties, cabin crew, passengers, air

traffic control, emergency services….Simplifying the A380 (with 4,000

parts) down to a Cessna (the flying version of the 1938 Ariel Red Hunter

motorcycle) kept things very simple for me, removing the complexity,

making each system simple to understand from a mechanical (not

mechatronic perspective), simplifying my mental model of the aircraft’s systems, freeing up mind-space to manage the entire event. It [is] vital in an

emergency that there is a structured hierarchy of responsibility and authority.

It’s even more important that pilots understand the roles, tasks, and teamwork

required in an autonomous team of just two pilots (more in our case on board

QF32), isolated from help but in charge of 469 lives.”

fail every time In response to a fact-checking email, de Crespigny explained that it is impossible to get a simulator to re-create the conditions of QF32, because the problems with the plane were so extreme.

CHAPTER FOUR: GOAL SETTING

about to attack For my understanding of the events leading up to the Yom Kippur War, I am indebted to Professor Uri Bar-Joseph, who was kind

enough to provide extensive written comments, as well as the following

sources: Abraham Rabinovich, The Yom Kippur War: The Epic Encounter

That Transformed the Middle East (New York: Schocken, 2007); Uri Bar-

Joseph, The Watchman Fell Asleep: The Surprise of Yom Kippur and Its

Sources (Albany: State University of New York Press, 2012); Uri Bar-

Joseph, “Israel’s 1973 Intelligence Failure,” Israel Affairs 6, no. 1 (1999): 11–35; Uri Bar-Joseph and Arie W. Kruglanski, “Intelligence Failure and

Need for Cognitive Closure: On the Psychology of the Yom Kippur

Surprise,” Political Psychology 24, no. 1 (2003): 75–99; Yosef Kuperwaser,

Lessons from Israel’s Intelligence Reforms (Washington, D.C.: Saban Center

for Middle East Policy at the Brookings Institution, 2007); Uri Bar-Joseph

and Jack S. Levy, “Conscious Action and Intelligence Failure,” Political

Science Quarterly 124, no. 3 (2009): 461–88; Uri Bar-Joseph and Rose

McDermott, “Personal Functioning Under Stress Accountability and Social

Support of Israeli Leaders in the Yom Kippur War,” Journal of Conflict

Resolution 52, no. 1 (2008): 144–70; Uri Bar-Joseph, “ ‘The Special Means of Collection’: The Missing Link in the Surprise of the Yom Kippur War,”

The Middle East Journal 67, no. 4 (2013): 531–46; Yaakov Lapin,

“Declassified Yom Kippur War Papers Reveal Failures,” The Jerusalem Post,

September 20, 2012; Hamid Hussain, “Opinion: The Fourth Round—A

Critical Review of 1973 Arab-Israeli War,” Defence Journal, November

2002,

http://www.defencejournal.com/2002/nov/4th-round.htm;

P.

R.

Kumaraswamy, Revisiting the Yom Kippur War (London: Frank Cass, 2000);

Charles Liebman, “The Myth of Defeat: The Memory of the Yom Kippur

War in Israeli Society,” Middle Eastern Studies 29, no. 3 (1993): 411; Simon

Dunstan, The Yom Kippur War: The Arab-Israeli War of 1973 (Oxford:

Osprey Publishing, 2007); Asaf Siniver, The Yom Kippur War: Politics,

Legacy, Diplomacy (Oxford: Oxford University Press, 2013).

“sharp as possible” Bar-Joseph, Watchman Fell Asleep.

nothing more than words In an email, the historian Uri Bar-Joseph wrote that the concept was “a set of assumptions that were based on documented

information that was passed to Israel by Ashraf Marwan, the son-in-law of

late president Nasser and a close advisor to Sadat, who since late 1970

worked for the Mossad. The main assumptions were: (1) Egypt cannot

occupy the Sinai without neutralizing the Israeli air-superiority. The way to

do it is by attacking the bases of the [Israeli Air Force] at the beginning of the

war. In order to do it, Egypt needs long-range attack aircraft which she won’t

have before 1975; (2) In order to deter Israel from attacking strategic targets

in Egypt, Egypt needs Scud missiles that will be able to hit Tel Aviv. Scuds

started arriving in Egypt in the summer of 1973 but were not expected to be

operational before February 1974. (3) Syria will not go to war without Egypt.

Zeira became an ardent believer in these assumptions and turned them into an

orthodox conception, which he kept until war started.”

within the next decade Bar-Joseph and Kruglanski, “Intelligence Failure and Need for Cognitive Closure,” 75–99.

need for cognitive closure For more on cognitive closure, please see Steven L. Neuberg and Jason T. Newsom, “Personal Need for Structure: Individual

Differences in the Desire for Simpler Structure,” Journal of Personality and

Social Psychology 65, no. 1 (1993): 113; Cynthia T. F. Klein and Donna M.

Webster, “Individual Differences in Argument Scrutiny as Motivated by

Need for Cognitive Closure,” Basic and Applied Social Psychology 22, no. 2

(2000): 119–29; Carsten K. W. De Dreu, Sander L. Koole, and Frans L.

Oldersma, “On the Seizing and Freezing of Negotiator Inferences: Need for

Cognitive Closure Moderates the Use of Heuristics in Negotiation,”

Personality and Social Psychology Bulletin 25, no. 3 (1999): 348–62; A.

Chirumbolo, A. Areni, and G. Sensales, “Need for Cognitive Closure and

Politics: Voting, Political Attitudes and Attributional Style,” International

Journal of Psychology 39 (2004): 245–53; Arie W. Kruglanski, The

Psychology of Closed Mindedness (New York: Psychology Press, 2013); Arie

W. Kruglanski et al., “When Similarity Breeds Content: Need for Closure

and the Allure of Homogeneous and Self-Resembling Groups,” Journal of

Personality and Social Psychology 83, no. 3 (2002): 648; Steven L. Neuberg and Jason T. Newsom, “Personal Need for Structure: Individual Differences

in the Desire for Simpler Structure,” Journal of Personality and Social

Psychology 65, no. 1 (1993): 113.

“confusion and ambiguity” Bar-Joseph, Watchman Fell Asleep; Donna M.

Webster and Arie W. Kruglanski, “Individual Differences in Need for

Cognitive Closure,” Journal of Personality and Social Psychology 67, no. 6

(1994): 1049.

“need for closure introduces a bias” Bar-Joseph and Kruglanski,

“Intelligence Failure and Need for Cognitive Closure,” 75–99.

Donna Webster, wrote in 1996 Arie W. Kruglanski and Donna M. Webster,

“Motivated Closing of the Mind: ‘Seizing’ and ‘Freezing,’ ” Psychological

Review 103, no. 2 (1996): 263.

it has been selected Ibid.; De Dreu, Koole, and Oldersma, “On the Seizing and Freezing of Negotiator Inferences,” 348–62.

we’re making a mistake In an email responding to fact-checking questions, Arie Kruglanski wrote: “People under high need for closure have trouble

appreciating others’ perspectives and points of view. People under high need

for closure also prefer hierarchical, autocratic, decision making structures in

groups because those provide better closure than horizontal or democratic

structures that tend to be more chaotic. People under high need for closure

are therefore intolerant of diversity, and of dissent in groups and aren’t very

creative. Politically, conservatives tend to be higher on need for closure than

liberals, but people with high need for closure tend to be more committed to

things and values than people low on need for closure.”

“should not expect promotion” Bar-Joseph and Kruglanski, “Intelligence Failure and Need for Cognitive Closure,” 75–99.

“outside the organization” Uri Bar-Joseph, “Intelligence Failure and Success in the War of Yom Kippur,” unpublished paper.

“before war broke out” Abraham Rabinovich, “Three Years Too Late, Golda Meir Understood How War Could Have Been Avoided,” The Times of

Israel, September 12, 2013.

Israelis were killed or wounded Zeev Schiff, A History of the Israeli Army, 1874 to the Present (New York: Macmillan, 1985).

“generation was nearly lost” Richard S. Lazarus, Fifty Years of the Research and Theory of RS Lazarus: An Analysis of Historical and Perennial

Issues (New York: Psychology Press, 2013).

“Even a quarter century later” Kumaraswamy, Revisiting the Yom Kippur War.

good at choosing goals For my understanding of General Electric, I am indebted to Joseph L. Bower and Jay Dial, “Jack Welch: General Electric’s

Revolutionary,” Harvard Business School case study no. 394-065, October

1993, revised April 1994; Francis Aguilar and Thomas W. Malnight,

“General Electric Co: Preparing for the 1990s,” Harvard Business School

case study no. 9-390, December 20, 1989; Francis J. Aguilar, R. Hamermesh,

and Caroline Brainard, “General Electric: Reg Jones and Jack Welch,”

Harvard Business School case study no. 9-391-144, June 29, 1991; Kirsten

Lungberg, “General Electric and the National Broadcasting Company: A

Clash of Cultures,” Harvard University John F. Kennedy School of

Government case study, 1989; Nitin Nohria, Anthony J. Mayo, and Mark

Benson, “General Electric’s 20th Century CEOs,” Harvard Business School

case study, December 2005; Jack Welch and John A. Byrne, Jack: Straight

from the Gut (New York: Warner, 2003); Larry Greiner, “Steve Kerr and His

Years with Jack Welch at GE,” Journal of Management Inquiry 11, no. 4

(2002): 343–50; Stratford Sherman, “The Mind of Jack Welch,” Fortune,

March 27, 1989; Marilyn Harris et al., “Can Jack Welch Reinvent GE?”

BusinessWeek, June 30, 1986; Mark Potts, “GE Chief Hopes to Shape Agile

Giant,” Los Angeles Times, June 1, 1988; Noel Tichy and Ram Charan,

“Speed Simplicity and Self-Confidence: An Interview with Jack Welch,”

Harvard Business Review, September 1989; Ronald Grover and Mark

Landler, “NBC Is No Longer a Feather in GE’s Cap,” BusinessWeek, June 2,

1991; Harry Bernstein, “The Two Faces of GE’s ‘Welchism,’ ” Los Angeles Times, January 12, 1988; “Jack Welch Reinvents General Electric. Again,”

The Economist, March 30, 1991; L. J. Dans, “They Call Him ‘Neutron,’ ”

Business Month, March 1988; Richard Ellsworth and Michael Kraft, “Jack

Welch at GE: 1981–1989,” Claremont Graduate School, Peter F. Drucker and

Masatoshi Ito Graduate School of Management case study; Peter Petre, “Jack

Welch: The Man Who Brought GE to Life,” Fortune, January 5, 1987; Peter

Petre, “What Welch Has Wrought at GE,” Fortune, July 7, 1986; Stephen W.

Quickel, “Welch on Welch,” Financial World, April 3, 1990; Monica

Roman, “Big Changes Are Galvanizing General Electric,” BusinessWeek,

December 18, 1989; Thomas Stewart, “GE Keeps Those Ideas Coming,”

Fortune, August 12, 1991.

“became the work ‘contract’ ” Nitin Nohria, Anthony J. Mayo, and Mark Benson, “General Electric’s 20th Century CEOs,” Harvard Business Review,

December 19, 2005, revised April 2011; John Cunningham Wood and

Michael C. Wood, Peter F. Drucker: Critical Evaluations in Business and

Management, vol. 1 (London: Routledge, 2005).

best way to set goals Gary P. Latham, Terence R. Mitchell, and Dennis L.

Dossett, “Importance of Participative Goal Setting and Anticipated Rewards

on Goal Difficulty and Job Performance,” Journal of Applied Psychology 63,

no. 2 (1978): 163; Gary P. Latham and Gerard H. Seijts, “The Effects of

Proximal and Distal Goals on Performance on a Moderately Complex Task,”

Journal of Organizational Behavior 20, no. 4 (1999): 421–29; Gary P.

Latham and J. James Baldes, “The ‘Practical Significance’ of Locke’s Theory

of Goal Setting,” Journal of Applied Psychology 60, no. 1 (1975): 122; Gary

P. Latham and Craig C. Pinder, “Work Motivation Theory and Research at

the Dawn of the Twenty-First Century,” Annual Review of Psychology 56

(2005): 485–516; Edwin A. Locke and Gary P. Latham, “Building a

Practically Useful Theory of Goal Setting and Task Motivation: A Thirty-

Five-Year Odyssey,” American Psychologist 57, no. 9 (2002): 705; A.

Bandura, “Self-Regulation of Motivation and Action Through Internal

Standards and Goal Systems,” in Goal Concepts in Personality and Social

Psychology, ed. L. A. Pervin (Hillsdale, N.J.: Erlbaum, 1989), 19–85; Travor

C. Brown and Gary P. Latham, “The Effects of Goal Setting and Self-

Instruction Training on the Performance of Unionized Employees,” Relations Industrielles/Industrial Relations 55, no. 1 (2000): 80–95; Judith F. Bryan and Edwin A. Locke, “Goal Setting as a Means of Increasing Motivation,”

Journal of Applied Psychology 51, no. 3 (1967): 274; Scott B. Button, John

E. Mathieu, and Dennis M. Zajac, “Goal Orientation in Organizational

Research: A Conceptual and Empirical Foundation,” Organizational

Behavior and Human Decision Processes 67, no. 1 (1996): 26–48; Dennis L.

Dossett, Gary P. Latham, and Terence R. Mitchell, “Effects of Assigned

Versus Participatively Set Goals, Knowledge of Results, and Individual

Differences on Employee Behavior When Goal Difficulty Is Held Constant,”

Journal of Applied Psychology 64, no. 3 (1979): 291; Elaine S. Elliott and Carol S. Dweck, “Goals: An Approach to Motivation and Achievement,”

Journal of Personality and Social Psychology 54, no. 1 (1988): 5; Judith M.

Harackiewicz et al., “Predictors and Consequences of Achievement Goals in

the College Classroom: Maintaining Interest and Making the Grade,” Journal

of Personality and Social Psychology 73, no. 6 (1997): 1284; Howard J.

Klein et al., “Goal Commitment and the Goal-Setting Process: Conceptual

Clarification and Empirical Synthesis,” Journal of Applied Psychology 84,

no. 6 (1999): 885; Gary P. Latham and Herbert A. Marshall, “The Effects of

Self-Set, Participatively Set, and Assigned Goals on the Performance of

Government Employees,” Personnel Psychology 35, no. 2 (June 1982): 399–

404; Gary P. Latham, Terence R. Mitchell, and Dennis L. Dossett,

“Importance of Participative Goal Setting and Anticipated Rewards on Goal

Difficulty and Job Performance,” Journal of Applied Psychology 63, no. 2

(1978): 163; Gary P. Latham and Lise M. Saari, “The Effects of Holding

Goal Difficulty Constant on Assigned and Participatively Set Goals,”

Academy of Management Journal 22, no. 1 (1979): 163–68; Don

VandeWalle, William L. Cron, and John W. Slocum, Jr., “The Role of Goal

Orientation Following Performance Feedback,” Journal of Applied

Psychology 86, no. 4 (2001): 629; Edwin A. Locke and Gary P. Latham, eds.,

New Developments in Goal Setting and Task Performance (London:

Routledge, 2013).

how fast they produced text Gary P. Latham and Gary A. Yukl, “Assigned Versus Participative Goal Setting with Educated and Uneducated Woods

Workers,” Journal of Applied Psychology 60, no. 3 (1975): 299.

“Making yourself break a goal” In an email responding to fact-checking questions, Latham wrote that achieving goals also requires access to the

necessary resources and feedback on goal progress. “For long-term/distal

goals, proximal/sub goals should be set. Sub goals do two things: maintain

motivation for attaining the distal goal as the attainment of one sub goal leads

to the desire to attain another sub goal. Second, feedback from pursuit of each

sub goal yields information as to whether you are on- or off-track.”

Latham wrote in 1990 Edwin A. Locke and Gary P. Latham, “New Directions in Goal-Setting Theory,” Current Directions in Psychological

Science 15, no. 5 (2006): 265–68.

“the right things,” said Latham In an email responding to fact-checking questions, Latham wrote: “When people lack the ability to attain a

performance goal, that is, a goal having to do with a specific desired result such as a golf score of 80 or a 23% increase in revenue, [improper focus or

tunnel vision] may occur. The solution is to set a specific, challenging

learning goal where the emphasis is on discovering/developing a process,

procedure, system that will enable you to improve your performance such as

[coming] up with 5 ways you can improve your putting as opposed to put the

ball in the cup in no more than 2 strokes.”

business school, for help Kerr was initially one of twenty-four consultants brought in by Jack Welch to expand Work-Outs throughout GE.

more long-term plans Noel M. Tichy and Stratford Sherman, “Walking the Talk at GE,” Training and Development 47, no. 6 (1993): 26–35; Ronald

Henkoff, “New Management Secrets from Japan,” Fortune, November 27,

1995; Ron Ashkenas, “Why Work-Out Works: Lessons from GE’s

Transformation Process,” Handbook of Business Strategy 4, no. 1 (2003): 15–

21; Charles Fishman, “Engines of Democracy,” Fast Company, October

1999, http://www.fastcompany.com/37815/engines-democracy; Thomas A.

Stewart, “GE Keeps Those Ideas Coming,” in Rosabeth Moss Kanter, Barry

A. Stein, and Todd D. Jick, The Challenge of Organizational Change: How

Companies Experience It and Leaders Guide It (New York: The Free Press,

1992): 474–482; Joseph P. Cosco, “General Electric Works It All Out,”

Journal of Business Strategy 15, no. 3 (1994): 48–50.

“turn out great” In an email responding to fact-checking questions, Kerr wrote: “I stressed to the leadership teams that ‘saying no to a bad idea is as

useful as saying yes to a good one,’ but that they couldn’t dismiss any

recommendation by saying things like: ‘We thought of that already,’ or ‘We

tried it before and it didn’t work.’ I always made the point that Work-Outs

present a terrific opportunity to teach people about the business, and that they

owed people a professional, courteous explanation as to why they didn’t

support a particular recommendation.”

SMART criteria In an email responding to fact-checking questions, Kerr wrote that he never encouraged people to submit proposals without a rough

plan and timeline. “The details of the plan would have to be sketched out

after approval,” he wrote.

“ideas are fair game” Cosco, “General Electric Works It All Out,” 48–50.

Japan’s railway system Ronald Henkoff, “New Management Secrets from Japan,” Fortune, November 27, 1995.

invent a faster train The story of Japan’s bullet train as it was told to Jack Welch (and has been repeated in popular nonfiction) differs slightly from the

historical record. The account given here reflects the story that was told to Welch, but there are some details that story did not include, such as the fact

that the concept for high-speed rail was explored but then abandoned by the

Japanese railway prior to World War II. In an email responding to fact-

checking questions, a representative of the Central Japan Railway Company

wrote that in the 1950s the “Tokaido Line, the main line of Japan, was very

crowded and [passengers had] been increasing because of the economical

growth after the war, and Japan had to meet the growing needs of passengers

to move between Tokyo (capital and largest city) and Osaka (second largest

city). Actually there was a concept of ‘Bullet train’ before the WWII, [in]

1939…but because of the war, that plan [had] been suspended. Japan

National Railway decided to build [a] new line by standard gauge (many of

Japanese conventional [lines adopted] narrow gauge) in 1957. The plan [was

accepted] in 1958 by the government and construction had started.” It is also worth noting that private efforts at developing faster trains were also

occurring at the same time in Japan. The Odakyu Electric Railway, for

instance, was developing a train capable of going ninety miles per hour. For a

better understanding of the history of the bullet train, I recommend Toshiji

Takatsu, “The History and Future of High-Speed Railways in Japan,” Japan

Railway and Transport Review 48 (2007): 6–21; Mamoru Taniguchi, “High

Speed Rail in Japan: A Review and Evaluation of the Shinkansen Train”

(working paper no. UCTC 103, University of California Transportation

Center, 1992); Roderick Smith, “The Japanese Shinkansen: Catalyst for the

Renaissance of Rail,” The Journal of Transport History 24, no. 2 (2003): 222–37; Moshe Givoni, “Development and Impact of the Modern High-Speed Train: A Review,” Transport Reviews 26, no. 5 (2006): 593–611.

120 miles per hour In an email responding to fact-checking questions, a representative of the Central Japan Railway Company wrote that “in Japan,

[a] JNR (Japan National Railway) engineer was considered [the] elite of

Japanese engineers at that time, and the engineer who designed Shinkansen

(Mr. Shima) was one of the engineers of JNR….He [had] been working in

JNR [a] long time already and had knowledge and experience about

railways.” Mr. Shima, the spokesperson noted, was asked, starting in 1955, to

oversee Tōkaidō Shinkansen. “At the time of the bullet train project in 1939 I

mentioned before, they were already planning to design trains which have [a

max speed of] 125 mph. [The] engineer of Shinkansen had the clear aim of

tying Tokyo to Osaka by 3 hours from the beginning, and [the] prototype

called ‘Series 1000’ achieved 256 km/h (160 mph) in 1963.”

into the 1980s Andrew B. Bernard, Andreas Moxnes, and Yukiko U. Saito, Geography and Firm Performance in the Japanese Production Network

(working paper no. 14034, National Bureau of Economic Research, 2014).

“bullet train thinking” S. Kerr and S. Sherman, “Stretch Goals: The Dark Side of Asking for Miracles,” Fortune, November 13, 1995; Sim B. Sitkin et

al., “The Paradox of Stretch Goals: Organizations in Pursuit of the Seemingly

Impossible,” Academy of Management Review 36, no. 3 (2011): 544–66;

Scott Jeffrey, Alan Webb, and Axel K-D. Schulz, “The Effectiveness of

Tiered Goals Versus Stretch Goals,” CAAA 2006 Annual Conference Paper

(2006); Kenneth R. Thompson, Wayne A. Hochwarter, and Nicholas J.

Mathys, “Stretch Targets: What Makes Them Effective?” The Academy of

Management Executive 11, no. 3 (1997): 48–60; S. Kerr and D. LePelley,

“Stretch Goals: Risks, Possibilities, and Best Practices,” New Developments

in Goal Setting and Task Performance (2013): 21–31; Steven Kerr and

Steffen Landauer, “Using Stretch Goals to Promote Organizational

Effectiveness and Personal Growth: General Electric and Goldman Sachs,”

The Academy of Management Executive 18, no. 4 (2004): 134–38; Kelly E.

See, “Motivating Individual Performance with Challenging Goals: Is It Better

to Stretch a Little or a Lot?” (manuscript presented for publication, Duke

University, June 2003); Adrian D. Manning, David B. Lindenmayer, and

Joern Fischer, “Stretch Goals and Backcasting: Approaches for Overcoming

Barriers to Large-Scale Ecological Restoration,” Restoration Ecology 14, no.

4 (2006): 487–92; Jim Heskett, “Has the Time Come for ‘Stretch’ in

Management?” Harvard Business School, Working Knowledge, August 1,

2008, http://hbswk.hbs.edu/item/5989.html.

their own workflow Fishman, “Engines of Democracy,” 33.

goal would have done that In an email responding to fact-checking questions, a spokesman for General Electric wrote that “the Durham plant

was created with the flexibility to make such dramatic change[s]. Many

adjustments were in process when the plant was opened in 1992. Durham

from its inception was created as an ‘incubator’ for new manufacturing

practices at GE Aviation. Yes, Jack [Welch] set the bar high—but given the

aggressive competition in the aviation business, these goals were a

requirement to be successful and to generate the kind of income necessary to

fund new engine developments at that time (namely the GE90).”

throughout the firm Thompson, Hochwarter, and Mathys, “Stretch Targets,” 48–60.

Thinsulate William E. Coyne, “How 3M Innovates for Long-Term Growth,”

Research-Technology Management 44, no. 2 (2001): 21–24.

“broad search, or playfulness” Sitkin et al., “Paradox of Stretch Goals,”

544–66.

the researchers wrote Jeffrey, Webb, and Schulz, “The Effectiveness of Tiered Goals Versus Stretch Goals.”

University of Waterloo Ibid.

University of Melbourne Thompson, Hochwarter, and Mathys, “Stretch Targets,” 48–60.

our in-box Gil Yolanda et al., “Capturing Common Knowledge About Tasks: Intelligent Assistance for To-Do Lists,” ACM Transactions on

Interactive Intelligent Systems (TiiS) 2, no. 3 (2012): 15; Victoria Bellotti et

al., “What a To-Do: Studies of Task Management Towards the Design of a

Personal Task List Manager,” Proceedings of the SIGCHI Conference on

Human Factors in Computing Systems (2004): 735–42; Gabriele Oettingen

and Doris Mayer, “The Motivating Function of Thinking About the Future:

Expectations Versus Fantasies,” Journal of Personality and Social

Psychology 83, no. 5 (2002): 1198; Anja Achtziger et al., “Metacognitive Processes in the Self-Regulation of Goal Pursuit,” in Social Metacognition, ed. Pablo Briñol and Kenneth DeMarree, Frontier of Social Psychology series

(New York: Psychology Press, 2012), 121–39.

throughout corporate America Critics of stretch goals say that, if unconstrained, they can negatively impact an organization. For more, please

see Lisa D. Ordóñez et al., “Goals Gone Wild: The Systematic Side Effects of

Overprescribing Goal Setting,” The Academy of Management Perspectives

23, no. 1 (2009): 6–16. And the response of Edwin A. Locke and Gary P.

Latham, “Has Goal Setting Gone Wild, or Have Its Attackers Abandoned

Good Scholarship?” The Academy of Management Perspectives 23, no. 1

(2009): 17–23.

investigators concluded The Commission of Inquiry, The Yom Kippur War, an Additional Partial Report: Reasoning and Complement to the Partial

Report of April 1, 1974, vol. 1 (Jerusalem: 1974).

all to blame Mitch Ginsberg, “40 Years On, Yom Kippur War Intel Chiefs Trade Barbs,” The Times of Israel, October 6, 2013; “Eli Zeira’s Mea Culpa,”

Haaretz, September 22, 2004; Lilach Shoval, “Yom Kippur War Intelligence

Chief Comes Under Attack 40 Years Later,” Israel Hayom, October 7, 2013.

“You are lying!” Ibid.

CHAPTER FIVE: MANAGING OTHERS

shoo them away As mentioned in the chapter, the Federal Bureau of Investigation and Frank, Christie, and Colleen Janssen were all provided with

summaries of this chapter and asked to respond to the details of this

reporting. The FBI declined to comment, except as specified below. The

Janssen family did not reply to repeated attempts to seek their comments by

telephone and mail. The sources used in reporting details of the Janssen case

include interviews as well as documents from United States of America v.

Kelvin Melton, Quantavious Thompson, Jakym Camel Tibbs, Tianna Daney

Maynard, Jenna Martin, Clifton James Roberts, Patricia Ann Kramer,

Jevante Price, and Michael Martell Gooden (nos. 5:14-CR-72–1; 5:14-CR-

72–2; 5:14-CR-72–3; 5:14-CR-72–4; 5:14-CR-72–5; 5:14-CR-72–6; 5:14-

CR-72–7; 5:14-CR-72–8; 5:14-CR-72–9), filed in the U.S. District Court for

the Eastern District of North Carolina Western Division; Affidavit in Support

of Application for a Court Order Approving Emergency Interceptions, in the

Matter of the Application of the United States of America for an Order

Authorizing the Interception of Wire and Electronic Communications, no.

5:14-MJ-1315-D, filed in the U.S. District Court Eastern District of North

Carolina Western Division; United States v. Kelvin Melton, Criminal Case

no. 5:14-MJ-1316, filed in the U.S. District Court Eastern District of North

Carolina; United States v. Clifton James Roberts, Criminal Case no. 5:14-MJ-

1313, filed in the U.S. District Court Eastern District of North Carolina;

United States v. Chason Renee Chase, a/k/a “Lady Jamaica,” Criminal Case

no. 3:14-MJ-50, filed in the U.S. District Court for the District of South

Carolina, and other court filings related to the alleged Janssen abduction.

Details also came from Alan G. Breed and Michael Biesecher, “FBI: NC

Inmate Helped Orchestrate Kidnapping,” Associated Press, April 11, 2014;

Kelly Gardner, “FBI Now Investigating Wake Forest Man’s Disappearance,”

WRAL.com, April 8, 2014; Alyssa Newcomb, “FBI Rescued Kidnap Victim

as Suspects Discussed Killing Him, Feds Say,” Good Morning America,

April 10, 2014; Anne Blythe and Ron Gallagher, “FBI Rescues Wake Forest

Man; Abduction Related to Daughter’s Work as Prosecutor, Investigators Say,” The Charlotte Observer, April 10, 2014; Michael Biesecher and Kate Brumbach, “NC Inmate Charged in Kidnapping of DA’s Father,” Associated

Press, April 12, 2014; Lydia Warren and Associated Press, “Bloods Gang

Member Who Is Serving Life Sentence ‘Masterminded Terrifying Kidnap of

Prosecutor’s Father Using a Cell Phone He’d Smuggled in to Prison,’ ” Daily

Mail, April 11, 2014; Lydia Warren and Associated Press, “Gang Members

Who ‘Kidnapped Prosecutor’s Father and Held Him Captive for Days Had

Meant to Capture HER—But They Went to Wrong Address,’ ” Daily Mail,

April 23, 2014; Ashley Frantz and AnneClaire Stapleton, “Prosecutor’s Dad

Kidnapped in ‘Elaborate’ Plot; FBI Rescues Him,” CNN.com, April 10,

2014; Shelley Lynch, “Kidnapping Victim Rescued by FBI Reunited with

Family,”

FBI

press

release,

April

10,

2014,

https://www.fbi.gov/charlotte/press-releases/2014/kidnapping-victim-

rescued-by-fbi-reunited-with-family; Scott Pelley and Bob Orr, “FBI Told How Its Agents Rescued a North Carolina Man Who Was Kidnapped by

Gang Members and Terrorized for Five Days,” CBS Evening News, April 10,

2014; Marcus K. Garner, “Indictment: Kidnapping Crew Had Wrong

Address, Took Wrong Person,” Atlanta Journal Constitution, April 22, 2014;

Andrew Kenney, “Prisoner Charged in Kidnap Conspiracy May Have Had

Phone for Weeks,” The Charlotte Observer, April 11, 2014; “Criminal

Complaint Filed Against Kelvin Melton in Kidnapping Case,” FBI press

release,

April

11,

2014,

https://www.fbi.gov/charlotte/press-

releases/2014/criminal-complaint-filed-against-kelvin-melton-in-kidnapping-

case; Colleen Jenkins and Bernadette Baum, “Two More Charged in Gang-

Linked Kidnapping of N.C. Prosecutor’s Father,” Reuters, April 16, 2014;

“McDonald’s Receipt Leads to Arrest in Wake Forest Kidnapping,” The

News and Observer, April 17, 2014; “Prosecutor—Not Her Father—Was

Intended Victim in Wake Forest Kidnapping, Officials Say,” The News and

Observer, April 22, 2014; Patrik Jonsson, “N.C. Prosecutor Kidnap Plot:

Home Attacks on Justice Officials on the Upswing,” The Christian Science

Monitor, April 23, 2014; “NC Kidnapping Victim Writes Thank-You Letter,”

Associated Press, April 29, 2014; Thomas McDonald, “Documents Detail

Kidnapping Plot of Wake Prosecutor’s Father,” The Charlotte Observer, July

23, 2014; Daniel Wallis, “Alleged Gangster Admits Lying in North Carolina

Kidnap Probe,” Reuters, August 29, 2014; Spink John, “FBI Team Rescues a

North Carolina Kidnapping Victim,” Atlanta Journal Constitution, April 11, 2014.

Melton’s daughters Some observers of the Janssen case have suggested that authorities used a device known as a “stingray,” which can identify the

precise location of a cellphone, in this investigation. The FBI, when asked

about use of a stingray in this case, replied with a response the agency has provided about cell site simulators to other media requests: “Location

information is a vital component of law enforcement investigations at the

federal, state and local levels. As a general matter, the FBI does not discuss

specific techniques used by law enforcement to obtain location information,

as they are considered Law Enforcement Sensitive, the public release of

which could harm law enforcement efforts at all levels by compromising

future use of the technique. The FBI only collects and maintains information

that has investigative value and relevance to a case, and such data [are]

retained in accordance with controlling federal law and Attorney General

policy. The FBI does not keep repositories of cell tower data for any purpose

other than in connection with a specific investigation. The collection of cell

tower records is only performed after required FBI approvals are received in

the specific investigation, and only after the appropriate order is obtained

from a court. If the records obtained are deemed relevant, the specific records

are made part of the investigative case file. The FBI retains investigative case

files in accordance with NARA-approved file retention schedules. If the FBI

believes the use of any technology or technique may provide information on

an individual where case law dictates that person has a reasonable

expectation of privacy, it is FBI policy to obtain a search warrant.”

directed by Melton himself As noted in the chapter, the details regarding Kelvin Melton, Tianna Brooks (who also allegedly goes by the name Tianna

Maynard), and other alleged kidnappers or those allegedly connected to the

Janssen kidnapping are contained in court documents or interviews. At the

time of writing, Melton, Brooks, and others implicated in this crime have

been indicted, but have not gone to trial. Until a trial is conducted and a verdict rendered, allegations remain just that, allegations, and the crimes

described in this chapter have not been proven in a court of law. In January

2016, Melton told a court that he was not responsible for the Janssen

kidnapping. Other alleged kidnappers are also expected to deny responsibility or guilt. Melton’s attorneys, as well as Brooks’s attorney, were presented

with synopses of all details in this chapter and asked to inquire if their clients,

who are incarcerated on other charges or awaiting trial, wished to respond.

Brooks’s lawyer did not reply. Melton’s lawyer, Ryan D. Stump, in an email

wrote: “We are under a court order not to discuss the details of Mr. Melton’s

case and what is contained in the discovery. Unfortunately, due to the

restrictions, we are not able to make any comments on the case.”

predecessors decades before In response to a fact-checking email, a spokeswoman for the FBI said that the bureau’s system prior to Sentinel, in

addition to using index cards, also used an electronic indexing system.

Interviews with agents confirmed this, but said that the electronic system was

often incomplete and thus unreliable.

rolled out Sentinel In response to a fact-checking email, a spokeswoman for the FBI detailed Sentinel this way: “Sentinel is a tool that manages records; it

documents case activities and investigations, the information we own and

produce. Sentinel provides a piece of the puzzle. It documents the FBI’s work

products and is used in conjunction with information we collect or access

through other partnerships in order to further data.”

“agile programming” The words “lean” and “agile” have come to mean different things in different settings. There is, for example, lean product

development, lean start-ups, agile management, and agile construction. Some

of these definitions or methodologies are very specific. In this chapter, I

generally use the phrases in their most global sense. However, for more

detailed explanations of the various implementations of these philosophies, I

recommend Rachna Shah and Peter T. Ward, “Lean Manufacturing: Context,

Practice Bundles, and Performance,” Journal of Operations Management 21,

no. 2 (2003): 129–49; Jeffrey K. Liker, Becoming Lean: Inside Stories of U.S.

Manufacturers (Portland, Ore.: Productivity Press, 1997); J. Ben Naylor,

Mohamed M. Naim, and Danny Berry, “Leagility: Integrating the Lean and

Agile Manufacturing Paradigms in the Total Supply Chain,” International

Journal of Production Economics 62, no. 1 (1999): 107–18; Robert Cecil

Martin, Agile Software Development: Principles, Patterns, and Practices

(Upper Saddle River, N.J.: Prentice Hall, 2003); Paul T. Kidd, Agile Manufacturing: Forging New Frontiers (Reading, Mass.: Addison-Wesley,

1995); Alistair Cockburn, Agile Software Development: The Cooperative

Game (Upper Saddle River, N.J.: Addison-Wesley, 2006); Pekka

Abrahamsson, Outi Salo, and Jussi Ronkainen, Agile Software Development

Methods: Review and Analysis (Oulu, Finland: VTT Publications, 2002).

“aphrodisiac in Northern California” Rick Madrid passed away in 2012.

For my understanding of Mr. Madrid, NUMMI, and General Motors, I am

deeply indebted to Frank Langfitt of National Public Radio, Brian Reed of

This American Life, and other reporters from various newspapers and media

organizations who were kind enough to share notes and transcripts with me,

as well as Madrid’s former colleagues, who shared memories of him. Details

on Madrid, including his quotes, draw on a variety of sources, including tapes

of interviews with him, notes and transcripts from interviews he gave to other

reporters, and recollections of colleagues. In addition, I relied upon Harry

Bernstein, “GM Workers Proud of Making the Team,” Los Angeles Times,

June 16, 1987; Clara Germani, “GM-Toyota Venture in California Breaks

Tradition, Gets Results,” The Christian Science Monitor, December 21, 1984;

Michelle Levander, “The Divided Workplace: Exhibit Traces Battle for

Control of Factory,” Chicago Tribune, September 17, 1989; Victor F.

Zonana, “Auto Venture at Roadblock: GM-Toyota Fremont Plant Produces

Happy Workers, High-Quality Product—and a Glut of Unsold Chevrolet

Novas,” Los Angeles Times, December 21, 1987; “NUMMI,” This American

Life, WBEZ Chicago, March 26, 2010; Charles O’Reilly III, “New United

Motors Manufacturing, Inc. (NUMMI),” Stanford Business School Case

Studies, no. HR-11, December 2, 1998; Maryann Keller, Rude Awakening:

The Rise, Fall, and Struggle for Recovery of General Motors (New York: William Morrow, 1989); Joel Smith and William Childs, “Imported from

America: Cooperative Labor Relations at New United Motor Manufacturing,

Inc.,” Industrial Relations Law Journal (1987): 70–81; John Shook, “How to

Change a Culture: Lessons from NUMMI,” MIT Sloan Management Review

51, no. 2 (2010): 42–51; Michael Maccoby, “Is There a Best Way to Build a

Car?” Harvard Business Review, November 1997; Daniel Roos, James P.

Womack, and Daniel Jones, The Machine That Changed the World: The

Story of Lean Production (New York: HarperPerennial, 1991); Jon Gertner,

“From 0 to 60 to World Domination,” The New York Times, February 18, 2007; Ceci Connolly, “Toyota Assembly Line Inspires Improvements at

Hospital,” The Washington Post, June 3, 2005; Andrew C. Inkpen, “Learning

Through Alliances: General Motors and NUMMI,” Strategic Direction 22,

no. 2 (2006); Paul Adler, “The ‘Learning Bureaucracy’: New United Motor

Manufacturing, Inc.” Research in Organizational Behavior 15 (1993); “The

End of the Line For GM-Toyota Joint Venture,” All Things Considered,

NPR, March 2010; Martin Zimmerman and Ken Basinger, “Toyota Considers

Halting Operations at California’s Last Car Plant,” Los Angeles Times, July 24, 2009; Soyoung Kim and Chang-ran Kim, “UPDATE 1—Toyota May

Drop U.S. Joint Venture with GM,” Reuters, July 10, 2009; Alan Ohnsman

and Kae Inoue, “Toyota Will Shut California Plant in First Closure,”

Bloomberg, August 28, 2009; Jeffrey Liker, The Toyota Way: 14

Management Principles from the World’s Greatest Manufacturer (New

York: McGraw-Hill, 2003); Steven Spear and H. Kent Bowen, “Decoding the

DNA of the Toyota Production System,” Harvard Business Review 77

(1999): 96–108; David Magee, How Toyota Became #1: Leadership Lessons

from the World’s Greatest Car Company (New York: Penguin, 2007).

covered his tattoos Keller, Rude Awakening, chapter 6.

the Fremont plant In a statement sent in response to fact-checking questions, a spokesman for Toyota wrote: “Toyota can’t speak to any of the

descriptions of the Fremont facility while it operated prior to the independent

joint venture with GM. While the broad descriptions of Toyota’s philosophy

and certain historical facts are consistent with our approach and

understanding of events—such as the use of the andon cord, the trip for

former GM workers to Japan and the improvement in product quality

following the formation of NUMMI—we are unfortunately unable to confirm

or provide any other feedback on the specific accounts you provide.

However, we can provide the following statement from the company on the

NUMMI joint venture, which you are welcome to use if you so choose:

‘NUMMI was a groundbreaking model of Japan-U.S. industry collaboration,

and we are proud of all its considerable achievements. We remain grateful to

all of those involved with NUMMI, including the suppliers, the local

community and, most of all, the talented team members who have contributed

to the success of this pioneering joint venture.’ ” In a statement, a spokeswoman for General Motors wrote: “I can’t comment on the specific

points you shared re the experience at Fremont and NUMMI in the early

1980s, but I can absolutely confirm that is not the experience in GM plants today….GM’s Global Manufacturing System is a single, common

manufacturing system that aligns and engages all employees to use best

processes, practices and technologies to eliminate waste throughout the

enterprise….While it is true that GMS has its roots in the Toyota Production

System (TPS) that was implemented at NUMMI in 1984, many components

of GMS grew out of our efforts to benchmark lean manufacturing around the

world….While all principles and elements are considered crucial to the

successful implementation of GMS, one principle is key to GMS’s

adaptability, and that is Continuous Improvement. By engaging our

employees, we have seen them use GMS to improve our production systems,

ensure a safer work environment and improve product quality for our

customers.”

low costs in Japan In a fact-checking email, Jeffrey Liker, who has studied and written extensively about Toyota, wrote: “Toyota realized that to be a

global company they needed to set up operations overseas and they had little

experience doing it outside of sales. They believed that the Toyota

Production System was vital to their success and it was highly dependent on

people deeply understanding the philosophy and continuously improving in

an environment of trust. They saw NUMMI as a grand experiment to test

whether they could make TPS work in the United States with American

workers and managers. In fact, in the original agreement with GM they

planned on only making Chevy vehicles and when these did not sell because

of the negative image of the Chevy brand they brought over the Toyota

Corolla. For GM the main attraction was to get some small cars built of good

quality profitably and learn how to do this. They seemed to have a passing

interest in TPS. For Toyota NUMMI was considered a critical milestone to

their future and they studied what was happening every single day to learn as

much as they possibly could about operating in the US and developing the

Toyota culture overseas.”

prove their assertion right In response to a fact-checking email, Baron

wrote: “Our focus was a bit broader than ‘culture.’ We were interested in how founders’ early choices about organizational design and structuring of

employment relationships affected the evolution of their nascent enterprises.”

“answer a questionnaire” In response to a fact-checking email, Baron wrote that the sources they turned to exceeded just the San Jose Mercury News:

“We scoured a variety of sources, including the ‘Merc,’ to try to identify

evidence of new foundings. That was supplemented by industry listings from

companies like CorpTech (which focuses on marketing targeted to small tech

companies). From these sources we put together listings of companies by

subsector (biotechnology, semiconductors, etc.). Then we sampled from

those listings, seeking to get a representative sampling of firms in terms of age, venture-backed versus not, etc. Somewhat later, after ‘the Internet’ had

emerged as a discernible sector, we replicated the research design focusing

specifically on that sector, to see if things were similar or different among the

new net companies from the others that we had been studying, and we found

the patterns were the same.”

close to two hundred firms James N. Baron and Michael T. Hannan, “The Economic Sociology of Organizational Entrepreneurship: Lessons from the

Stanford Project on Emerging Companies,” in The Economic Sociology of

Capitalism, ed. Victor Nee and Richard Swedberg (New York: Russell Sage,

2002), 168–203; James N. Baron and Michael T. Hannan, “Organizational

Blueprints for Success in High-Tech Start-Ups: Lessons from the Stanford

Project on Emerging Companies,” Engineering Management Review, IEEE

31, no. 1 (2003): 16; James N. Baron, M. Diane Burton, and Michael T.

Hannan, “The Road Taken: Origins and Evolution of Employment Systems

in Emerging Companies,” Articles and Chapters (1996): 254; James N.

Baron, Michael T. Hannan, and M. Diane Burton, “Building the Iron Cage:

Determinants of Managerial Intensity in the Early Years of Organizations,”

American Sociological Review 64, no. 4 (1999): 527–47.

collected enough data In response to a fact-checking email, Baron wrote:

“Perhaps this is nit-picking, but what we were looking at were firms whose

founders had similar cultural ‘blueprints’ or premises underlying their

creation. I emphasize this because we were not using observable practices as

the basis for differentiation, but instead the way in which founders thought and spoke about their nascent enterprises.”

one of five categories There were also a sizable number of firms that did not fit neatly into any of the five categories.

“on the same path” In response to a fact-checking email, Baron said that he should not be considered an expert on Facebook, and that participants in the

study were promised anonymity. He added: “We found that engineering firms

fairly frequently evolved, either into bureaucracies or into commitment firms.

Those transitions were much less disruptive than others, suggesting that one

reason for the popularity of the engineering blueprint at start-up is that it is amenable to being ‘morphed’ into a different model as the firm matures.”

“ ‘You get paid,’ ” Baron said Baron, in response to a fact-checking email, said that the bureaucratic and the autocratic models have differences but are

similar in that “(1) they are both quite infrequent within this sector among

start-ups; and (2) they are both unpopular with scientific and technical

personnel.”

successful companies in the world The researchers promised confidentiality to companies that participated in the study, and would not divulge specific

firms they had studied.

culture came through James N. Baron, Michael T. Hannan, and M. Diane Burton, “Labor Pains: Change in Organizational Models and Employee

Turnover in Young, High-Tech Firms,” American Journal of Sociology 106,

no. 4 (2001): 960–1012.

California Management Review Baron and Hannan, “Organizational Blueprints for Success in High-Tech Start-Ups,” 16.

“strong advantage” In response to a fact-checking email, Baron expanded upon his comments: “What this doesn’t explicitly capture is that commitment

firms tended to compete based on superior relationships with their customers

over the longer term. It is not just relationships with salespeople, but rather that stable teams of technical personnel, working interdependently with

customer-facing personnel, enable these companies to develop technologies that met the needs of their long-term customers.”

“viability of the Company” Steve Babson, ed., Lean Work: Empowerment and Exploitation in the Global Auto Industry (Detroit: Wayne State

University Press, 1995).

preserve their jobs In a fact-checking email, Jeffrey Liker wrote that Toyota’s head of human resources had told a UAW representative that

“before laying off any workers they would insource work, then management

would take a payout and then they would cut back hours before considering

layoffs. In return he said the union needed to agree on three things: 1)

competence would be the basis for workers advancing, not seniority, 2) there

had to be a minimum of job classifications so they had the flexibility to do multiple jobs, and 3) management and the union would work together on

productivity improvements. Within the first year the Chevy Nova was not

selling well and they had about 40% too many workers and they kept them all

employed in training and doing kaizen for several months until they could get

the Corolla into production.”

Harvard researchers wrote Paul S. Adler, “Time-and-Motion Regained,”

Harvard Business Review 71, no. 1 (1993): 97–108.

shared power It is important to note that, despite NUMMI’s success, the company was not perfect. Its fortunes were tied to the automotive industry,

and so when overall car sales declined, NUMMI’s profits dipped as well. The

NUMMI factory was more expensive to operate than some low-cost foreign

competitors, and so there were stretches when the firm was undersold. And

when GM tried to export NUMMI’s culture to other plants, they found, in

some places, it wouldn’t take. Enmities between union leaders and managers

were simply too deep. Some executives refused to believe that workers, if

empowered, would use their authority responsibly. Some employees were

unwilling to give GM the benefit of the doubt.

“devoted to each other” When the Great Recession hit the automotive industry, NUMMI was one of the casualties. GM, headed toward bankruptcy

because of liabilities in other parts of the company, pulled out of the NUMMI partnership in 2009. Toyota concluded it couldn’t continue to operate the

plant on its own. NUMMI closed in 2010, after manufacturing nearly eight

million vehicles.

no end in sight Details on development of the Sentinel system come from interviews and Glenn A. Fine, The Federal Bureau of Investigation’s Pre-Acquisition Planning for and Controls over the Sentinel Case Management

System, Audit Report 06-14 (Washington, D.C.: U.S. Department of Justice,

Office of the Inspector General, Audit Division, March 2006); Glenn A. Fine,

Sentinel Audit II: Status of the Federal Bureau of Investigation’s Case

Management System, Audit Report 07-03 (Washington, D.C.: U.S.

Department of Justice, Office of the Inspector General, Audit Division,

December 2006); Glenn A. Fine, Sentinel Audit III: Status of the Federal

Bureau of Investigation’s Case Management System, Audit Report 07-40

(Washington, D.C.: U.S. Department of Justice, Office of the Inspector

General, Audit Division, August 2007); Raymond J. Beaudet, Sentinel Audit

IV: Status of the Federal Bureau of Investigation’s Case Management

System, Audit Report 09-05 (Washington, D.C.: U.S. Department of Justice,

Office of the Inspector General, Audit Division, December 2008); Glenn A.

Fine, Sentinel Audit V: Status of the Federal Bureau of Investigation’s Case

Management System, Audit Report 10-03 (Washington, D.C.: U.S.

Department of Justice, Office of the Inspector General, Audit Division,

November 2009); Status of the Federal Bureau of Investigation’s

Implementation of the Sentinel Project, Audit Report 10-22 (Washington,

D.C.: U.S. Department of Justice, Office of the Inspector General, March

2010); Thomas J. Harrington, “Response to OIG Report on the FBI’s Sentinel

Project,”

FBI

press

release,

October

20,

2010,

https://www.fbi.gov/news/pressrel/press-releases/mediaresponse_102010;

Cynthia A. Schnedar, Status of the Federal Bureau of Investigation’s

Implementation of the Sentinel Project, Report 12-08 (Washington, D.C.:

U.S. Department of Justice, Office of the Inspector General, December

2011); Michael E. Horowitz, Interim Report on the Federal Bureau of

Investigation’s Implementation of the Sentinel Project, Report 12-38

(Washington, D.C.: U.S. Department of Justice, Office of the Inspector

General, September 2012); Michael E. Horowitz, Audit of the Status of the

Federal Bureau of Investigation’s Sentinel Program, Report 14-31

(Washington, D.C.: U.S. Department of Justice, Office of the Inspector

General, September 2014); William Anderson et al., Sentinel Report

(Pittsburgh: Carnegie Mellon Software Engineering Institute, September

2010); David Perera, “Report Questions FBI’s Ability to Implement Agile

Development for Sentinel,” FierceGovernmentIT, December 5, 2010,

http://www.fiercegovernmentit.com/story/report-questions-fbis-ability-

implement-agile-development-sentinel/2010-12-05; David Perera, “FBI: We’ll Complete Sentinel with $20 Million and 67 Percent Fewer Workers,”

FierceGovernmentIT,

October

20,

2010,

http://www.fiercegovernmentit.com/story/fbi-well-complete-sentinel-20-

million-and-67-percent-fewer-workers/2010-10-20; Jason Bloomberg, “How the FBI Proves Agile Works for Government Agencies,” CIO, August 22,

2012,

http://www.cio.com/article/2392970/agile-development/how-the-fbi-

proves-agile-works-for-government-agencies.html; Eric Lichtblau, “FBI

Faces New Setback in Computer Overhaul,” The New York Times, March 18,

2010; “More Fallout from Failed Attempt to Modernize FBI Computer

System,” Office of Senator Chuck Grassley, July 21, 2010; “Technology

Troubles Plague FBI, Audit Finds,” The Wall Street Journal, October 20,

2010; “Audit Sees More FBI Computer Woes,” The Wall Street Journal,

October 21, 2010; “FBI Takes Over Sentinel Project,” Information

Management Journal 45, no. 1 (2011); Curt Anderson, “FBI Computer

Upgrade Is Delayed,” Associated Press, December 23, 2011; Damon Porter,

“Years Late and Millions over Budget, FBI’s Sentinel Finally On Line,” PC

Magazine, July 31, 2012; Evan Perez, “FBI Files Go Digital, After Years of

Delays,” The Wall Street Journal, August 1, 2012.

Toyota Production System philosophy to other industries For more on lean and agile management and methodologies, please see Craig Larman,

Agile and Iterative Development: A Manager’s Guide (Boston: Addison-

Wesley Professional, 2004); Barry Boehm and Richard Turner, Balancing

Agility and Discipline: A Guide for the Perplexed (Boston: Addison-Wesley

Professional, 2003); James Shore, The Art of Agile Development (Farnham,

UK: O’Reilly Media, 2007); David Cohen, Mikael Lindvall, and Patricia

Costa, “An Introduction to Agile Methods,” Advances in Computers 62

(2004): 1–66; Matthias Holweg, “The Genealogy of Lean Production,”

Journal of Operations Management 25, no. 2 (2007): 420–37; John F.

Krafcik, “Triumph of the Lean Production System,” MIT Sloan Management

Review 30, no. 1 (1988): 41; Jeffrey Liker and Michael Hoseus, Toyota Culture: The Heart and Soul of the Toyota Way (New York: McGraw-Hill,

2007); Steven Spear and H. Kent Bowen, “Decoding the DNA of the Toyota

Production System,” Harvard Business Review 77 (1999): 96–108; James P.

Womack and Daniel T. Jones, Lean Thinking: Banish Waste and Create

Wealth in Your Corporation (New York: Simon & Schuster, 2010); Stephen

A. Ruffa, Going Lean: How the Best Companies Apply Lean Manufacturing

Principles to Shatter Uncertainty, Drive Innovation, and Maximize Profits

(New York: American Management Association, 2008); Julian Page,

Implementing Lean Manufacturing Techniques: Making Your System Lean

and Living with It (Cincinnati: Hanser Gardner, 2004).

how software was created “What Is Agile Software Development?” Agile

Alliance, June 8, 2013, http://www.agilealliance.org/the-alliance/what-is-

agile/; Kent Beck et al., “Manifesto for Agile Software Development,” Agile

Manifesto, 2001, http://www.agilemanifesto.org/.

among many tech firms Dave West et al., “Agile Development: Mainstream Adoption Has Changed Agility,” Forrester Research 2 (2010): 41.

“fix what’s broken?” Ed Catmull and Amy Wallace, Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration

(New York: Random House, 2014).

wrote in 2005 J. P. Womack and D. Miller, Going Lean in Health Care (Cambridge, Mass.: Institute for Healthcare Improvement, 2005).

get Sentinel working Jeff Stein, “FBI Sentinel Project Is over Budget and Behind Schedule, Say IT Auditors,” The Washington Post, October 20, 2010.

plan everything in advance This method of planning is often known as a

“waterfall approach,” because it is a sequential design methodology in which

progress “flows” downward from conception to initiation, analysis, design,

construction, testing, production/implementation, and maintenance. At the

core of this approach is the belief that each stage can be anticipated and

scheduled.

unfettered themselves In response to a fact-checking email, Fulgham expanded his comments: “I assigned the CTO (Jeff Johnson) as the day to

day executive for oversight. We hired an Agile Scrum Master (Mark

Crandall) to serve as a coach and mentor (not as a project manager). We

created an open physical workspace in the basement that allowed

collaborative communications between team members. We assigned three

Cyber Special Agents as the front end development leads, and the Director,

Deputy Director and I empowered them to recommend any process

improvements and/or form consolidations (in order not to just digitize any

potentially outdated processes/forms). I worked with the CEOs of our top

vendors for the products that were going to make up Sentinel to get their

support and their best cleared personnel. The team adopted (under Mark’s

coaching) the agile methodology. All FBI stakeholders were part of the

business side of the Sentinel team to ensure their needs were met. The

technical team conducted self directed two-week sprints. We had nightly

automated builds. A dedicated QA team was located with the development

team, and I held a meeting every two weeks to view fully functional code (no

mockups) and personally signed off on requirements. All stakeholders, the

DOJ, the DOJ IG, the White House and other interested government

agencies, attended these demo days to observe our progress and process.”

solve thousands of crimes In response to a fact-checking email, a spokeswoman for the FBI wrote, regarding Sentinel: “We are not predicting

crime. We may identify trends and threats.”

“is capable of” Jeff Sutherland, Scrum: The Art of Doing Twice the Work in Half the Time (New York: Crown Business, 2014).

“cultural mindset” Robert S. Mueller III, “Statement Before the House Permanent Select Committee on Intelligence,” Washington, D.C., October 6,

2011, https://www.fbi.gov/news/testimony/the-state-of-intelligence-reform-

10-years-after-911.

CHAPTER SIX: DECISION MAKING

worth $450,000 Throughout this chapter, chips are referred to by their notional dollar value. However, it is important to note that in tournaments

like this one, chips are tokens that are collected to determine winners—they

are not traded in for cash on a one-to-one basis. Rather, prize money is paid

out based on how someone places in the competition. So someone could have

$200,000 in chips and take fifth place in a tournament and win $300,000, for

instance. In this particular tournament, the prize was $2 million and, by

coincidence, the total number of chips was also $2 million.

prize for second place The 2004 Tournament of Champions is described in slightly different chronological order than what occurred in order to highlight

the salient points of each hand. Beyond describing hands out of order, no

other facts have been changed. For my understanding of the 2004

Tournament of Champions as well as poker more generally, I am indebted to

Annie Duke, Howard Lederer, and Phil Hellmuth for their time and advice. In

addition, this account relies upon the taped version of the 2004 TOC,

provided by ESPN; Annie Duke, with David Diamond, How I Raised,

Folded, Bluffed, Flirted, Cursed and Won Millions at the World Series of

Poker (New York: Hudson Street Press, 2005); “Annie Duke: The Big

Things You Don’t Do,” The Moth Radio Hour, September 13, 2012,

http://themoth.org/posts/stories/the-big-things-you-dont-do; “Annie Duke: A House

Divided,”

The

Moth

Radio

Hour,

July

20,

2011,

http://themoth.org/posts/stories/a-house-divided; “Dealing with Doubt,”

Radiolab, season 11, episode 4, http://www.radiolab.org/story/278173-

dealing-doubt/; Dina Cheney, “Flouting Convention, Part II: Annie Duke

Finds Her Place at the Poker Table,” Columbia College Today, July 2004,

http://www.college.columbia.edu/cct_archive/jul04/features4.php;

Ginia

Bellafante, “Dealt a Bad Hand? Fold ’Em. Then Raise,” The New York Times,

January 19, 2006; Chuck Darrow, “Annie Duke, Flush with Success,” The

Philadelphia Inquirer, June 8, 2010; Jamie Berger, “Annie Duke, Poker Pro,”

Columbia

Magazine,

March

4,

2013,

http://www.columbia.edu/cu/alumni/Magazine/Spring2002/Duke.html;

“Annie Duke Profile,” The Huffington Post, February 21, 2013; Del Jones,

“Know Yourself, Know Your Rival,” USA Today, July 20, 2009; Richard

Deitsch, “Q&A with Annie Duke,” Sports Illustrated, May 26, 2005; Mark Sauer, “Annie Duke Found Her Calling,” San Diego Union-Tribune, October

9, 2005; George Sturgis Coffin, Secrets of Winning Poker (Wilshire, 1949);

Richard D. Harroch and Lou Krieger, Poker for Dummies (New York: Wiley,

2010); David Sklansky, The Theory of Poker (Two Plus Two Publishers,

1999); Michael Bowling et al., “Heads-Up Limit Hold’em Poker Is Solved,”

Science 347, no. 6218 (2015): 145–49; Darse Billings et al., “The Challenge

of Poker,” Artificial Intelligence 134, no. 1 (2002): 201–40; Kevin B. Korb,

Ann E. Nicholson, and Nathalie Jitnah, “Bayesian Poker,” Proceedings of the

Fifteenth Conference on Uncertainty in Artificial Intelligence (San Francisco:

Morgan Kaufmann, 1999).

she was going to win Gerald Hanks, “Poker Math and Probability,”

Pokerology, http://www.pokerology.com/lessons/math-and-probability/.

win a Nobel Prize Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica: Journal of the

Econometric Society 47, no. 2 (1979): 263–91.

a million television viewers The tournament drew an estimated 1.5 million viewers.

She’s not sure Annie, in a phone call to check facts in this chapter, expanded upon her thinking: “If Greg had jacks or better, I was in a bad situation. I was

very undecided about the hand he could be holding, and I was in a situation

where I really did have to create more certainty for myself. I really needed to

decide if he had aces or kings, and then fold. Also, Greg Raymer, at that

point, was an unknown quantity, but my brother and I had been watching

videotapes of him play, and we had seen what we thought was a “tell,”

something he did physically when he had a good hand, and I saw him do this

particular thing that suggested to me that he had a strong hand. That’s not a

certain thing, you don’t know if a tell is 100 percent, but it helped tip me into

thinking he had a strong hand.”

“intelligence forecasts” “Aggregative Contingent Estimation,” Office of the Director of National Intelligence (IARPA), 2014, Web.

some fresh ideas For my understanding of the Good Judgment Project, I am indebted to Barbara Mellers et al., “Psychological Strategies for Winning a

Geopolitical Forecasting Tournament,” Psychological Science 25, no. 5

(2014): 1106–15; Daniel Kahneman, “How to Win at Forecasting: A

Conversation

with

Philip

Tetlock,”

Edge,

December

6,

2012,

https://edge.org/conversation/how-to-win-at-forecasting; Michael D. Lee, Mark Steyvers, and Brent Miller, “A Cognitive Model for Aggregating

People’s Rankings,” PloS One 9, no. 5 (2014); Lyle Ungar et al., “The Good

Judgment Project: A Large Scale Test” (2012); Philip Tetlock, Expert

Political Judgment: How Good Is It? How Can We Know? (Princeton, N.J.:

Princeton University Press, 2005); Jonathan Baron et al., “Two Reasons to

Make Aggregated Probability Forecasts More Extreme,” Decision Analysis

11, no. 2 (2014): 133–45; Philip E. Tetlock et al., “Forecasting Tournaments

Tools for Increasing Transparency and Improving the Quality of Debate,”

Current Directions in Psychological Science 23, no. 4 (2014): 290–95; David

Ignatius, “More Chatter than Needed,” The Washington Post, November 1,

2013; Alex Madrigal, “How to Get Better at Predicting the Future,” The

Atlantic, December 11, 2012; Warnaar et al., “Aggregative Contingent

Estimation System”; Uriel Haran, Ilana Ritov, and Barbara A. Mellers, “The

Role of Actively Open-Minded Thinking in Information Acquisition,

Accuracy, and Calibration,” Judgment and Decision Making 8, no. 3 (2013):

188–201; David Brooks, “Forecasting Fox,” The New York Times, March 21,

2013; Philip Tetlock and Dan Gardner, Seeing Further (New York: Random

House, 2015).

A group of At various points during the GJP, the precise number of researchers involved fluctuated.

questions as the experts In response to a fact-checking email, Barbara Mellers and Philip Tetlock, another of the GJP leaders, wrote: “We had two

different types of training in the first year of the tournament. One was

probabilistic reasoning and the other was scenario training. Probabilistic

reasoning worked somewhat better, so in subsequent years, we implemented

only the probabilistic training. Training was revised each year. As it evolved, there was a section on geopolitical reasoning and another on probabilistic

reasoning….Here is a section that describes the training: We constructed

educational modules on probabilistic-reasoning training and scenario training

that drew on state-of-the-art recommendations. Scenario training taught

forecasters to generate new futures, actively entertain more possibilities, use

decision trees, and avoid biases such as over-predicting change, creating

incoherent scenarios, or assigning probabilities to mutually exclusive and

exhaustive outcomes that exceed 1.0. Probability training guided forecasters

to consider reference classes, average multiple estimates from existing

models, polls, and expert panels, extrapolate over time when variables were

continuous, and avoid judgmental traps such as overconfidence, the

confirmation bias, and base-rate neglect. Each training module was

interactive with questions and answers to check participant understanding.”

abilities to forecast the future In response to a fact-checking email, Don Moore wrote: “On average, those with training did better. But not everyone

who got trained did better than all the people who did not get it.”

“tremendously useful” Brooks, “Forecasting Fox.”

“things you aren’t sure about” In response to a fact-checking email, Don Moore wrote: “What makes our forecasters good is not just their high level of

accuracy, but their well-calibrated humility. They are no more confident than

they deserve to be. It’s ideal to know when you have forecast the future with

accuracy and when you haven’t.”

or roughly 20 percent In an email, Howard Lederer, a two-time World Series of Poker champion, explained the further nuances required in

analyzing this hand: “The hand you use as an example is MUCH more

complicated than it appears.” Given what’s known, Lederer said, there is

actually a better than 20 percent chance of winning. “Here’s why. If you

KNOW your opponent has an A or a K, then you know seven cards. Your

two [cards], your opponent’s one card, and the four [communal cards] on the

board. This means there are 45 unknown cards (you have no information on

your opponent’s other card). This would mean you have nine hearts to win,

and 36 non-hearts to lose. The odds would be 4 to 1, or 1 in 5. The percentages are 20%. As long as you are not putting more than 20% of the

money into the pot, it’s a good call. Here’s where you might ask: if I am only

20% to win against an A or K, then how can I be better than [20%] to win?

Your opponent might not have an A or K! He could have a spade flush draw

without an A or K, he could have a straight draw with a 5–6. He could have a

lower heart draw. That would be great for you! There’s also a chance he just

has garbage and is trying to bluff you with nothing. In general, I’d calculate

the chances that your opponent has one of these drawing or bluffing hands at

about 30% (given how many of these possibilities there are). So let’s do some

probabilistic math: 70% of the time he has an A or K, and you win 20% of

those times. 25% of the time he has a draw and you win about 82% of those

hands (I’m combining various possible odds given his range of holdings

when he is drawing). And 5% of the time he has a total bluff and you win

89% of the time when he has garbage. Your total chances of winning are: (.7

× .2) + (.25 × .82) + (.05 × .89) = 39%! This is a simple ‘expected value’

calculation. You can see that the .7, .25 and .05 part of the calculation adds

up to 1. Meaning we have covered all the possible holdings and assigned

them probabilities. And we are making our best guess as to our chances

against each holding. At the table, you don’t have time to do all the math, but

‘in your gut’ you can feel the odds and make the easy call. One other note, if

you miss your flush and your opponent bets, you should seriously consider

calling anyway. You will be getting well over 10–1, and the chances he is

bluffing are probably higher than that. This is just a simple taste of the

complexity of poker.”

they’ll quit For more on calculating odds in poker, please see Pat Dittmar, Practical Poker Math: Basic Odds and Probabilities for Hold’em and

Omaha (Toronto: ECW Press, 2008); “Poker Odds for Dummies,”

CardsChat, https://www.cardschat.com/odds-for-dummies.php; Kyle Siler,

“Social and Psychological Challenges of Poker,” Journal of Gambling

Studies 26, no. 3 (2010): 401–20.

“odds work for you” In response to a fact-checking email, Howard Lederer wrote: “It’s more complex than that. Amateurs players make many different

kinds of errors. Some play too loose. They crave the uncertainty and favor

action over prudence. Some players are too conservative, favoring a small loss in a hand over taking the chance to win, but also the chance to take a large loss. Your job as a poker pro is to simply play your best each hand. In

the long run, your superior decisions will defeat your opponent’s poor

decisions, whatever they may be. The societal value of poker is that it is a great training ground for learning sound decision-making under conditions of

uncertainty. Once you get the hang of playing poker, you develop the skills

necessary to make probabilistic decisions in life.”

Annie’s brother, Howard Though it does not bear on the events described in this chapter, disclosure compels mentioning that Lederer was a founder and

board member of Tiltware, LLC, the company behind Full Tilt Poker, a

popular website that was accused of bank fraud and illegal gambling by the

U.S. Department of Justice. In 2012, Lederer settled a civil lawsuit with the

Department of Justice related to Full Tilt Poker. He admitted no wrongdoing,

but did agree to forfeit more than $2.5 million.

winning this hand Technically, Howard has an 81.5 percent chance of winning—however, because it is hard to win half a hand of poker, this has

been rounded up to 82 percent.

remaining cards on the table In response to a fact-checking email, Howard Lederer wrote: “I would say that in a 3 handed situation, [a pair of sevens] is

close to 90% to be best before the flop. This is the hand where I agree anyone

would have played her hand and my hand the same way; all in before the

flop. After we had all the money in, I am not a slight favorite, but instead a

large favorite. This [is] a unique feature of hold’em. If you have a slightly better hand than your opponent, you are often a big favorite. 7–7 is about

81% to beat 6–6.”

“they tell you might occur” In response to a fact-checking email, Howard Lederer wrote: “It’s not an easy thing to choose a profession where you lose

more often than you win. One has to focus on the long run, and realize that if

you get offered 10–1, on enough 5–1 shots, you will come out ahead, while

also realizing that you will lose 5 out of 6 times.”

humans process information Tenenbaum, in an email responding to fact-checking questions, described his research this way: “Often we start with

what looks like a gap between humans and computers, where humans are

outperforming standard computers with intuitions that may not look like

computations….But then we try to close that gap, by understanding how

human intuitions actually have a subtle computational basis, which then can

be engineered in a machine, to make the machine smarter in more human-like

ways.”

“seeing just a few examples” Joshua B. Tenenbaum et al., “How to Grow a Mind: Statistics, Structure, and Abstraction,” Science 331, no. 6022 (2011): 1279–85.

“examples of each?” Ibid.

(which has no strong pattern) In an email responding to fact-checking questions, Tenenbaum said that many of the examples they used were fairly

complex, and “the reasons for the prediction functions having these shapes

are the combination of (1) the priors, plus (2) a certain assumption about

when an event is likely to be sampled (the ‘likelihood’), (3) Bayesian

updating from priors to posteriors, and (4) using the 50th percentile of the

posterior as the basis for prediction. What’s correct about what you have is

that in our simple model, only (1) varies across domains—between movies,

representatives, life spans, etc.—while (2–4) are the same for all the tasks.

But [it’s] because of these causal processes (which vary across domains)

together with the rest of the statistical computations (which are the same

across domains) that the prediction functions have the shape they do.” It is

important to note that the graphs in this text do not represent accurate

empirical results, but rather patterns of predictions—the estimations that

represent the 50th percentile of being right or wrong.

You read about a movie These are summaries of the questions asked. The direct wording of each question was: “Imagine you hear about a movie that

has taken in 60 million dollars at the box office, but don’t know how long it

has been running. What would you predict for the total amount of box office

intake for that movie?” “Insurance agencies employ actuaries to make

predictions about people’s life spans—the age at which they will die—based upon demographic information. If you were assessing an insurance case for a

39-year-old man, what would you predict for his life span?” “Imagine you are

in somebody’s kitchen and notice that a cake is in the oven. The timer shows

that it has been baking for 14 minutes. What would you predict for the total

amount of time the cake needs to bake?” “If you heard a member of the

House of Representatives had served for 11 years, what would you predict his

total term in the House would be?”

variation of Bayes’ rule In an email responding to fact-checking questions, Tenenbaum wrote that “the most natural way to make these kinds of

predictions in computers is to run algorithms which effectively implement the

logic of Bayes’ rule. The computers typically don’t explicitly ‘use’ Bayes’

rule, because the direct computations of Bayes’ rule are typically intractable

to carry out except in simple cases. Rather the programmers give the

computers prediction algorithms whose predictions are made to be

approximately consistent with Bayes’ rule in a wide range of cases, including

these.”

data and your assumptions Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists (San Diego: Academic Press,

2004).

skewed, as well “Base rate” typically refers to a yes-or-no question. In the Tenenbaum experiment, participants were asked to make numerical

predictions, rather than answer a binary question, and so it’s most accurate to

refer to this assumption as a “prior distribution.”

failures we’ve overlooked In an email responding to fact-checking questions, Tenenbaum wrote that “It’s not clear from our work that

predictions for events in a certain class improve progressively with more

experience with events of that type. Sometimes they might, sometimes they

don’t. And this is not the only way to acquire a prior. As the pharaohs

example shows, and other projects by us and other researchers, people can

acquire a prior in various ways beyond direct experience with a class of

events, including being told things, making analogies to other classes of

events, forming analogies, and so on.”

“the Poker Brat” Eugene Kim, “Why Silicon Valley’s Elites Are Obsessed with

Poker,”

Business

Insider,

November

22,

2014,

http://www.businessinsider.com/best-poker-players-in-silicon-valley-2014-

11.

“bluff when it matters” In response to a fact-checking email, Hellmuth wrote: “Annie is a great poker player, and she has stood the test of time. I respect her, and I respect her Hold’em game.”

He folds In response to a fact-checking email, Hellmuth wrote: “I think she was trying to tilt me (get me emotional and upset) by showing a nine in that

situation. A lot of players would have gone broke with my hand there (top

pair) w[ith] a ‘Safe’ turn card, but I’ve made a living deviating from the norm

and trusting my instincts (my white magic, my reading ability). I trusted it

and folded.”

middle of the table In response to a fact-checking email, Hellmuth wrote:

“With the chips I had at that time I had to go all in w[ith] 10–8 on that flop (I

had top pair and there were flush draws, and straight draws possible).

Completely standard. If you’re trying to imply that I put the money because I

was emotionally tilted, you’re wrong. Nothing I could do there.”

Phil is out In response to a fact-checking email, Hellmuth contends that he and Annie had struck a deal when the tournament came down to the two of

them in which they pledged to guarantee each other $750,000 regardless of

the winner, and play for the last $500,000. Annie Duke confirmed this deal.

CHAPTER SEVEN: INNOVATION

movie everyone is talking about For my understanding of Frozen’s development, I am particularly indebted to Ed Catmull, Jennifer Lee, Andrew

Millstein, Peter Del Vecho, Kristen Anderson-Lopez, Bobby Lopez, Amy

Wallace, and Amy Astley, as well as other Disney employees, some of whom

wished to remain anonymous, who were generous with their time.

Additionally, I relied upon Charles Solomon, The Art of Frozen (San

Francisco: Chronicle Books, 2015); John August, “Frozen with Jennifer

Lee,” Scriptnotes, January 28, 2014, http://johnaugust.com/2014/frozen-with-

jennifer-lee; Nicole Laporte, “How Frozen Director Jennifer Lee Reinvented

the Story of the Snow Queen,” Fast Company, February 28, 2014; Lucinda

Everett, “Frozen: Inside Disney’s Billion-Dollar Social Media Hit,” The Telegraph, March 31, 2014; Jennifer Lee, “Frozen, Final Shooting Draft,”

Walt

Disney

Animation

Studios,

September

23,

2013,

http://gointothestory.blcklst.com/wp-content/uploads/2014/11/Frozen.pdf;

“Frozen: Songwriters Kristen Anderson-Lopez and Robert Lopez Official

Movie

Interview,”

YouTube,

October

31,

2013,

https://www.youtube.com/watch?v=mzZ77n4Ab5E; Susan

Wloszczyna,

“With Frozen, Director Jennifer Lee Breaks Ice for Women Directors,”

Indiewire,

November

26,

2013,

http://blogs.indiewire.com/womenandhollywood/with-frozen-director-

jennifer-lee-breaks-the-ice-for-women-directors; Jim Hill, “Countdown to Disney Frozen: How One Simple Suggestion Broke the Ice on the Snow

Queen’s Decades-Long Story Problems,” Jim Hill Media, October 18, 2013,

http://jimhillmedia.com/editor_in_chief1/b/jim_hill/archive/2013/10/18/countdown-

to-disney-quot-frozen-quot-how-one-simple-suggestion-broke-the-ice-on-the-

quot-snow-queen-quot-s-decades-long-story-problems.aspx;

Brendon

Connelly, “Inside the Research, Design, and Animation of Walt Disney’s

Frozen with Producer Peter Del Vecho,” Bleeding Cool, September 25, 2013,

http://www.bleedingcool.com/2013/09/25/inside-the-research-design-and-

animation-of-walt-disneys-frozen-with-producer-peter-del-vecho/;

Ed

Catmull and Amy Wallace, Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration (New York: Random House,

2014); Mike P. Williams, “Chris Buck Reveals True Inspiration Behind

Disney’s Frozen (Exclusive),” Yahoo! Movies, April 8, 2014; Williams

College, “Exploring the Songs of Frozen with Kristen Anderson-Lopez ’94,”

YouTube,

June

30,

2014,

https://www.youtube.com/watch?

v=ftddAzabQMM; Dan Sarto, “Directors Chris Buck and Jennifer Lee Talk

Frozen,” Animation World Network, November 7, 2013; Jennifer Lee,

“Oscars 2014: Frozen’ s Jennifer Lee on Being a Female Director,” Los Angeles Times, March 1, 2014; Rob Lowman, “Unfreezing Frozen: The Making of the Newest Fairy Tale in 3D by Disney,” Los Angeles Daily News,

November 19, 2013; Jill Stewart, “Jennifer Lee: Disney’s New Animation

Queen,” LA Weekly, May 15, 2013; Simon Brew, “A Spoiler-Y, Slightly

Nerdy Interview About Disney’s Frozen,” Den of Geek! , December 12,

2013,

http://www.denofgeek.com/movies/frozen/28567/a-spoiler-y-nerdy-

interview-about-disneys-frozen; Sean Flynn, “Is It Her Time to Shine?” The

Newport Daily News, February 17, 2014; Mark Harrison, “Chris Buck and

Jennifer Lee Interview: On Making Frozen,” Den of Geek! December 6,

2013,

http://www.denofgeek.com/movies/frozen/28495/chris-buck-and-

jennifer-lee-interview-on-making-frozen; Mike Fleming, “Jennifer Lee to Co-

Direct Disney Animated Film Frozen,” Deadline Hollywood, November 29,

2012; Rebecca Keegan, “Disney Is Reanimated with Frozen, Big Hero 6,”

Los Angeles Times, May 9, 2013; Lindsay Miller, “On the Job with Jennifer

Lee,

Director

of

Frozen,”

Popsugar,

February

28,

2014,

http://www.popsugar.com/celebrity/Frozen-Director-Jennifer-Lee-Interview-

Women-Film-33515997; Trevor Hogg, “Snowed Under: Chris Buck Talks

About

Frozen,”

Flickering

Myth,

March

26,

2014,

http://www.flickeringmyth.com/2014/03/snowed-under-chris-buck-talks-

about.html; Jim Hill, “Countdown to Disney Frozen: The Flaky Design Idea Behind the Look of Elsa’s Ice Palace,” Jim Hill Media, October 9, 2013,

http://jimhillmedia.com/editor_in_chief1/b/jim_hill/archive/2013/10/09/countdown-

to-disney-quot-frozen-quot-the-flaky-design-idea-behind-the-look-of-elsa-s-

ice-palace.aspx; Rebecca Keegan, “Husband-Wife Songwriting Team’s

Emotions Flow in Frozen,” Los Angeles Times, November 1, 2013; Heather Wood Rudulph, “Get That Life: How I Co-Wrote the Music and Lyrics for

Frozen,” Cosmopolitan, April 27, 2015; Simon Brew, “Jennifer Lee and

Chris Buck Interview: Frozen, Statham, Frozen 2,” Den of Geek! , April 4,

2014,

http://www.denofgeek.com/movies/frozen/29346/jennifer-lee-chris-

buck-interview-frozen-statham-frozen-2; Carolyn Giardina, “Oscar: With

Frozen, Disney Invents a New Princess,” The Hollywood Reporter,

November 27, 2013; Steve Persall, “Review: Disney’s Frozen Has a Few

Cracks in the Ice,” Tampa Bay Times, November 26, 2013; Kate Muir,

“Jennifer Lee on Her Disney Hit Frozen: We Wanted the Princess to Kick Ass,” The Times, December 12, 2013; “Out of the Cold,” The Mail on

Sunday, December 29, 2013; Kathryn Shattuck, “Frozen Directors Take Divide-and-Conquer Approach,” The New York Times, January 16, 2014;

Ma’ayan Rosenzweig and Greg Atria, “The Story of Frozen: Making a

Disney Animated Classic,” ABC News Special Report, September 2, 2014,

http://abcnews.go.com/Entertainment/fullpage/story-frozen-making-disney-

animated-classic-movie-25150046; Amy Edmondson et al., “Case Study: Teaming at Disney Animation,” Harvard Business Review, August 27, 2014.

207 surprised by all the criticisms In an email sent in response to fact-

checking questions, Andrew Millstein, president of Disney Animation

Studios, wrote: “These are the kind of notes that fuel our creative process and

help propel the forward progress of all of our films in production. The

creative leadership on any film often gets too close to their films and loses objectivity. Our Story Trust functions like a highly critical and skilled

audience that can point to flaws in the story-telling and, more important,

provide potential solutions….You’re describing a process of experimentation,

exploration and discovery that are key components of all our films. It’s not a

question of if this will happen, but to what degree. This is a constant part of

our process and the expectation [of] every filmmaking team. It is what

contributes to the high standards that our films set.”

Book of Mormon In an email sent in response to fact-checking questions, Bobby Lopez made clear that Kristen was a sounding board for him in

writing Avenue Q and Book of Mormon but was not formally credited on those shows.

dozens of others popped up In an email sent in response to fact-checking questions, a spokeswoman for Walt Disney Animation Studios wrote that the

studio wished to emphasize “how typical this process is for every film at

Disney Animation since John [Lasseter] and Ed [Catmull] have become our

studio leaders—the screening process, the notes sessions, the taking apart of

the film and putting it back together. This is typical, not atypical.”

“good ideas are suffocated” In an email sent in response to fact-checking

questions, Ed Catmull, president of Disney Animation, wrote that the various

anecdotes in this chapter are “viewpoints of different snapshots in time as the

film developed….In truth, you could substitute different words and it would

pretty much describe how every film goes through searching and change.

This is worth emphasizing so that people don’t have the impression that

Frozen was different in that way.”

Frozen was winding down In an email sent in response to fact-checking

questions, Millstein wrote: “Creativity needs time, space and support to fully

explore multiple ideas simultaneously. Our creative leadership has to have

the confidence and trust in each other to experiment, fail and try again and

again until the answers to story questions and problems get better and more

refined. There also needs to be a relentless focus on finding the best solutions

to difficult and thorny problems and never settling for sub-optimum solutions

because of time issues. Our creative teams need to trust that the executive

management fundamentally believes in and supports this process.”

avant-garde on Broadway Amanda Vaill, Somewhere: The Life of Jerome

Robbins (New York: Broadway Books, 2008); “Q&A with Producer Director

Judy Kinberg, ‘Jerome Robbins: Something to Dance About,’ ” directed by

Judy

Kinberg,

American

Masters,

PBS,

January

28,

2009,

http://www.pbs.org/wnet/americanmasters/jerome-robbins-q-a-with-

producerdirector-judy-kinberg/1100/; Sanjay Roy, “Step-by-Step Guide to

Dance: Jerome Robbins,” The Guardian, July 7, 2009; Sarah Fishko, “The

Real Life Drama Behind West Side Story,” NPR, January 7, 2009,

http://www.npr.org/2011/02/24/97274711/the-real-life-drama-behind-west-

side-story; Jeff Lundun and Scott Simon, “Part One: Making a New Kind of

Musical,”

NPR,

September

26,

2007,

http://www.npr.org/templates/story/story.php?storyId=14730899;

Jeff

Lundun and Scott Simon, “Part Two: Casting Calls and Out of Town Trials,”

NPR, September 26, 2007, http://www.npr.org/templates/story/story.php?

storyId=14744266; Jeff Lundun and Scott Simon, “Part Three: Broadway to

Hollywood—and

Beyond,”

NPR,

September

26,

2007,

http://www.npr.org/templates/story/story.php?storyId=14749729; “West Side Story Film Still Pretty, and Witty, at 50,” NPR, October 17, 2011,

http://www.npr.org/templates/story/story.php?storyId=14749729; Jesse

Green, “When You’re a Shark You’re a Shark All the Way,” New York

Magazine, March 15, 2009; Larry Stempel, “The Musical Play Expands,”

American Music 10, no. 2 (1992): 136–69; Beth Genné, “ ‘Freedom

Incarnate’: Jerome Robbins, Gene Kelly, and the Dancing Sailors as an Icon

of American Values in World War II,” Dance Chronicle 24, no. 1 (2001): 83–103; Bill Fischer and Andy Boynton, “Virtuoso Teams,” Harvard

Business Review, July 1, 2005; Otis L. Guernsey, ed., Broadway Song and Story: Playwrights/Lyricists/Composers Discuss Their Hits (New York:

Dodd Mead, 1985); Larry Stempel, Showtime: A History of the Broadway

Musical Theater (New York: W. W. Norton, 2010); Robert Emmet Long,

“West Side Story,” in Broadway, the Golden Years: Jerome Robbins and the

Great Choreographer-Directors: 1940 to the Present (New York:

Continuum, 2001); Leonard Bernstein, “A West Side Log” (1982); Terri

Roberts, “West Side Story: ‘We Were All Very Young,’ ” The Sondheim

Review 9, no. 3 (Winter 2003); Steven Suskin, Opening Night on Broadway:

A Critical Quotebook of the Golden Era of the Musical Theatre, Oklahoma!

(1943) to Fiddler on the Roof (1964) (New York: Schirmer Trade Books,

1990); Amanda Vaill, “Jerome Robbins—About the Artist,” American

Masters,

PBS,

January

27,

2009,

http://www.pbs.org/wnet/americanmasters/jerome-robbins-about-the-

artist/1099/.

actor on the stage There are a few outliers to this musical formula, most notably Oklahoma! , in which dance was used to express plot and emotional

moments.

“me a ballet?” Tim Carter, “Leonard Bernstein: West Side Story. By Nigel Simeone,” Music and Letters 92, no. 3 (2011): 508–10.

would be West Side Story West Side Story went through numerous names before the final title was chosen.

musical’s main characters Excerpts of letters come from the Leonard Bernstein Collection at the Library of Congress as well as from records made

available by various authors and the New York Public Library system.

“jitterbugging” This was written by Leonard Bernstein, as quoted in The Leonard Bernstein Letters (New Haven, Conn.: Yale University Press, 2013).

“we’re boring the audience” Jerome Robbins, as quoted in The Leonard Bernstein Letters (New Haven, Conn.: Yale University Press, 2013).

“two intermissions” Vaill, Somewhere.

“Shakespeare standing behind you” Ibid.

“Forget Anita” Deborah Jowitt, Jerome Robbins: His Life, His Theater, His Dance (New York: Simon & Schuster, 2004).

Science in 2013 Brian Uzzi et al., “Atypical Combinations and Scientific Impact,” Science 342, no. 25 (2013): 468–72.

Brian Uzzi and Ben Jones For more on Uzzi and Jones’s work, please see Stefan Wuchty, Benjamin F. Jones, and Brian Uzzi, “The Increasing

Dominance of Teams in Production of Knowledge,” Science 316, no. 5827

(2007): 1036–39; Benjamin F. Jones, Stefan Wuchty, and Brian Uzzi, “Multi-

University Research Teams: Shifting Impact, Geography, and Stratification in

Science,” Science 322, no. 5905 (2008): 1259–62; Holly J. Falk-Krzesinski et

al., “Advancing the Science of Team Science,” Clinical and Translational

Science 3, no. 5 (2010): 263–66; Ginger Zhe Jin et al., The Reverse Matthew

Effect: Catastrophe and Consequence in Scientific Teams (working paper

19489, National Bureau of Economic Research, 2013); Brian Uzzi and Jarrett

Spiro, “Do Small Worlds Make Big Differences? Artist Networks and the

Success of Broadway Musicals, 1945–1989” (unpublished manuscript,

Evanston, Ill., 2003); Brian Uzzi, and Jarrett Spiro, “Collaboration and

Creativity: The Small World Problem,” American Journal of Sociology 111,

no. 2 (2005): 447–504; Brian Uzzi, “A Social Network’s Changing Statistical

Properties and the Quality of Human Innovation,” Journal of Physics A:

Mathematical and Theoretical 41, no. 22 (2008); Brian Uzzi, Luis A.N.

Amaral, and Felix Reed-Tsochas, “Small-World Networks and Management

Science Research: A Review,” European Management Review 4, no. 2

(2007): 77–91.

creative and important In response to a fact-checking email, Uzzi wrote:

“The other thing is that teams are more likely to get this sweet spot of

creativity right. They are more likely than individuals to put together atypical

combinations of prior sources. Also, a paper with the right mix of

conventional and atypical ideas by a team does better than a single author,

given the same mix of conventional and atypical ideas. This means teams are

better than individuals at sourcing and deriving insights from atypical

combinations.”

bought lottery tickets Amos Tversky and Daniel Kahneman, “Availability: A Heuristic for Judging Frequency and Probability,” Cognitive Psychology 5,

no. 2 (1973): 207–32; Daniel Kahneman and Amos Tversky, “Prospect

Theory: An Analysis of Decision Under Risk,” Econometrica: Journal of the

Econometric Society 47, no. 2 (1979): 263–91; Amos Tversky and Daniel

Kahneman, “Judgment Under Uncertainty: Heuristics and Biases,” Science

185, no. 4157 (1974): 1124–31; Amos Tversky and Daniel Kahneman, “The

Framing of Decisions and the Psychology of Choice,” Science 211, no. 4481

(1981): 453–58; Daniel Kahneman and Amos Tversky, “Choices, Values,

and Frames,” American Psychologist 39, no. 4 (1984): 341; Daniel

Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux,

2011); Daniel Kahneman and Amos Tversky, “On the Psychology of

Prediction,” Psychological Review 80, no. 4 (1973): 237.

how genes evolve Qiong Wang et al., “Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy,” Applied

and Environmental Microbiology 73, no. 16 (2007): 5261–67; Jun S. Liu,

“The Collapsed Gibbs Sampler in Bayesian Computations with Applications

to a Gene Regulation Problem,” Journal of the American Statistical

Association 89, no. 427 (1994): 958–66.

“railway and mining” Andrew Hargadon and Robert I. Sutton, “Technology Brokering and Innovation in a Product Development Firm,” Administrative

Science Quarterly 42, no. 4 (1997): 716–49.

gambling techniques René Carmona et al., Numerical Methods in Finance: Bordeaux, June 2010, Springer Proceedings in Mathematics, vol. 12 (Berlin:

Springer Berlin Heidelberg, 2012); René Carmona et al., “An Introduction to

Particle Methods with Financial Application,” in Numerical Methods in

Finance, 3–49; Pierre Del Moral, Mean Field Simulation for Monte Carlo Integration (Boca Raton, Fla.: CRC Press, 2013); Roger Eckhardt, “Stan

Ulam, John von Neumann, and the Monte Carlo Method,” Los Alamos

Science, special issue (1987): 131–37.

in the shape of a hat Andrew Hargadon and Robert I. Sutton, “Technology Brokering and Innovation in a Product Development Firm,” Administrative

Science Quarterly 42, no. 4 (1997): 716–49; Roger P. Brown, “Polymers in Sport and Leisure,” Rapra Review Reports 12, no. 3 (November 2, 2001);

Melissa Larson, “From Bombers to Bikes,” Quality 37, no. 9 (1998): 30.

child-rearing techniques Benjamin Spock, The Common Sense Book of Baby and Child Care (New York: Pocket Books, 1946).

“evaluated as valuable” Ronald S. Burt, “Structural Holes and Good Ideas,”

American Journal of Sociology 110, no. 2 (2004): 349–99.

succeeded somewhere else In an email sent in response to fact-checking questions, Burt wrote: “Managers offered their best idea for improving the

value of their function to the company. The two senior executives in the

function evaluated each idea (stripped of personal identification). The

summary evaluation of each idea turned out to be primarily predicted by the

extent to which the person who articulated the idea had a network that

reached across boundaries (structural holes) between network groups,

functions, divisions in the company.”

pushed the right way For more on the concept of brokerage, please see Ronald S. Burt, Structural Holes: The Social Structure of Competition

(Cambridge, Mass.: Harvard University Press, 2009); Ronald S. Burt, “The

Contingent Value of Social Capital,” Administrative Science Quarterly 42,

no. 2 (1997): 339–65; Ronald S. Burt, “The Network Structure of Social

Capital,” in B. M. Staw and R. I. Sutton, Research in Organizational Behavior, vol. 22 (New York: Elsevier Science JAI, 2000), 345–423; Ronald

S. Burt, Brokerage and Closure: An Introduction to Social Capital (New

York: Oxford University Press, 2005); Ronald S. Burt, “The Social Structure

of Competition,” Explorations in Economic Sociology 65 (1993): 103; Lee

Fleming, Santiago Mingo, and David Chen, “Collaborative Brokerage,

Generative Creativity, and Creative Success,” Administrative Science

Quarterly 52, no. 3 (2007): 443–75; Satu Parjanen, Vesa Harmaakorpi, and Tapani Frantsi, “Collective Creativity and Brokerage Functions in Heavily

Cross-Disciplined Innovation Processes,” Interdisciplinary Journal of

Information, Knowledge, and Management 5, no. 1 (2010): 1–21; Thomas

Heinze and Gerrit Bauer, “Characterizing Creative Scientists in Nano-S&T:

Productivity, Multidisciplinarity, and Network Brokerage in a Longitudinal

Perspective,” Scientometrics 70, no. 3 (2007): 811–30; Markus Baer, “The

Strength-of-Weak-Ties Perspective on Creativity: A Comprehensive

Examination and Extension,” Journal of Applied Psychology 95, no. 3

(2010): 592; Ajay Mehra, Martin Kilduff, and Daniel J. Brass, “The Social

Networks of High and Low Self-Monitors: Implications for Workplace

Performance,” Administrative Science Quarterly 46, no. 1 (2001): 121–46.

plot’s central tensions I am indebted to the New York Public Library for making an early draft version of the West Side Story script available to me.

This is an abridgment of that script, shortened for ease of representation.

communicated through dance This text is a combination of finished versions of the West Side Story script, Robbins’s notes, and interviews providing a description of the choreography from the first staging of the show

and other sources.

“essential dramatic information” Larry Stempel, “The Musical Play Expands,” American Music (1992): 136–69.

the original Maria Fishko, “Real Life Drama Behind West Side Story.”

coffee cups and to-do lists The Frozen core team included Buck, Lee, Del Vecho, Bobby Lopez and Kristen Anderson-Lopez, Paul Briggs, Jessica

Julius, Tom MacDougall, Chris Montan, and, at times, others from various departments.

upstate New York In an email sent in response to fact-checking questions, a spokeswoman for Walt Disney Animation Studios wrote that Lee “and her

sister fought, as kids do; they grew together as they grew older. They were

never estranged….In college, they became close. They lived together in NYC

for a while, even.”

“ourselves on the screen” In an email sent in response to fact-checking questions, Millstein wrote: “Solutions to story issues [are often] connected to

personal emotional experiences. We draw from our own stories, history and

emotional lives as a wellspring of inspiration….We also draw on the

experiences of others throughout the studio and deep research into specific

areas that a film may attempt to explore. In the case of Frozen, we had a built-in research group at Disney Animation: employees who are sisters.

They can describe firsthand what it’s like to have a sister as a sibling and the

life experiences they’ve had. This is wonderful firsthand source material.”

“their experiences than other people” Gary Wolf, “Steve Jobs: The Next Insanely Great Thing,” Wired, April 1996.

“pushed to use it sometimes” In an email sent in response to fact-checking questions, Catmull wrote: “It is too simple to say that people need to be

pushed. Yes, they do, but they also need to be allowed to create, and we must

make it safe for them to find something new. Andrew and I both need to be a

force to make things move along, while at the same time, trying to keep fear

from slowing them down or getting stuck. This is what makes the job so

hard.”

make them stay put Art Fry, “The Post-it note: An Intrapreneurial Success,”

SAM Advanced Management Journal 52, no. 3 (1987): 4.

from wine spills P. R. Cowley, “The Experience Curve and History of the Cellophane Business,” Long Range Planning 18, no. 6 (1985): 84–90.

middle of the night Lewis A. Barness, “History of Infant Feeding Practices,”

The American Journal of Clinical Nutrition 46, no. 1 (1987): 168–70; Donna A. Dowling, “Lessons from the Past: A Brief History of the Influence of

Social, Economic, and Scientific Factors on Infant Feeding,” Newborn and

Infant Nursing Reviews 5, no. 1 (2005): 2–9.

psychologist Gary Klein Gary Klein, Seeing What Others Don’t: The Remarkable Ways We Gain Insights (New York: PublicAffairs, 2013).

of people’s expectations In an email sent in response to fact-checking questions, Bobby Lopez wrote: “From our perspective—we hit ‘send’ on an

email with our mp3 attached, and then count the minutes, hours, or

sometimes days before we hear back from them. Sometimes it means

something and sometimes it doesn’t. We didn’t hear back right away, so we

began to doubt the song, but when they did call us it was clear they were very

excited.”

“feel like one of us” In an email sent in response to fact-checking questions, a spokeswoman for Walt Disney Animation Studios wrote that Lee “had

written a draft of the script already in April [2012 in] which Elsa was a more

sympathetic character but there was still a plan for her to turn evil halfway through the film. [“Let It Go”] first appeared in [an] August 2012 screening.

“Let It Go” helped shift the tone of the Elsa character. It should be noted that

John Lasseter felt a personal tie to this as well—when thinking of Elsa, he

thought of his son, Sam, and his juvenile diabetes. When Sam was getting

poked and prodded as a child, he turned to John and said, ‘Why me?’ It

wasn’t Sam’s fault he had diabetes, just as it is not Elsa’s fault she has these

icy powers.”

“It had to feel real” In an email sent in response to fact-checking questions, a spokeswoman for Walt Disney Animation Studios wrote that Chris Buck

had a vision for how the film would end. “The ending—making it work

emotionally[—]was a puzzle. By October 2012, Jennifer had the ending

envisioning the four main characters in a blizzard of fear, which story artist

John Ripa boarded. Ripa’s boards received a standing ovation from John

Lasseter in the room. As Jennifer says, ‘We knew the end, we just needed to

earn it.’ ”

pressure that comes from deadlines Teresa M. Amabile et al., “Assessing the Work Environment for Creativity,” Academy of Management Journal 39,

no. 5 (1996): 1154–84; Teresa M. Amabile, Constance N. Hadley, and Steven

J. Kramer, “Creativity Under the Gun,” Harvard Business Review 80, no. 8

(2002): 52–61; Teresa M. Amabile, “How to Kill Creativity,” Harvard

Business Review 76, no. 5 (1998): 76–87; Teresa M. Amabile, “A Model of

Creativity and Innovation in Organizations,” Research in Organizational

Behavior 10, no. 1 (1988): 123–67.

“Lee a second director” In an email sent in response to fact-checking questions, Catmull wrote that it is important to emphasize that Lee was a

second director, not a “codirector,” which has multiple meanings in

Hollywood. “There is an actual title of ‘Co-director’ which is at a lower level

than ‘director.’ At Disney we frequently have two directors who both have

the title of ‘director.’ In this case, both Jenn and Chris were equal

directors….Jenn was made director along with Chris.”

spinning in place In an email sent in response to fact-checking questions, Millstein wrote: “Jenn’s promotion to an equal directing partner with Chris

provided an opportunity to alter the team dynamics in a positive way and

their receptivity to potential new ideas….Jenn is a very sensitive and

emphatic filmmaker. Her sensitivity to team dynamics, her role and voice and

deep need to maintain a deep collaboration is what helped make Frozen

successful.” One additional factor influencing the decision to promote Lee to

director, according to Buck, was that at that time, one of his children had a health issue that required attention, and as a result “John and Ed and Andrew

saw my personal need, and they asked me, right before, what would you

think of having Jenn as a co-director? And I said yes, I said absolutely, I would love that.”

ecologically bland I am indebted to the help of Stephen Palumbi of Stanford’s Hopkins Marine Station and Elizabeth Alter of the City University

of New York for their assistance in my understanding of the intermittent

disturbance hypothesis.

distributed so unevenly Joseph H. Connell, “Diversity in Tropical Rain

Forests and Coral Reefs,” Science, n.s. 199, no. 4335 (1978): 1302–10.

intermediate disturbance hypothesis Like many scientific theories, the intermediate disturbance hypothesis has many parents. For a more complete

history, please see David M. Wilkinson, “The Disturbing History of

Intermediate Disturbance,” Oikos 84, no. 1 (1999): 145–47.

“nor too frequent” John Roth and Mark Zacharias, Marine Conservation Ecology (London: Routledge, 2011).

staple of biology For more on the intermediate disturbance hypothesis, including the perspectives of those who challenge the theory, please see

Wilkinson, “The Disturbing History of Intermediate Disturbance”; Jane A.

Catford et al., “The Intermediate Disturbance Hypothesis and Plant

Invasions: Implications for Species Richness and Management,” Perspectives

in Plant Ecology, Evolution and Systematics 14, no. 3 (2012): 231–41; John

Vandermeer et al., “A Theory of Disturbance and Species Diversity:

Evidence from Nicaragua After Hurricane Joan,” Biotropica 28, no. 4 (1996):

600–613; Jeremy W. Fox, “The Intermediate Disturbance Hypothesis Should

Be Abandoned,” Trends in Ecology and Evolution 28, no. 2 (2013): 86–92.

Lee sat down with John Lasseter In an email sent in response to fact-checking questions, Catmull wrote that figuring out Frozen’ s ending was a team effort. John Ripa, an animator at Disney, storyboarded the ending. “This

was a powerful and influential part of the development of the story….[In

addition] there was a particularly impactful offsite where a great deal of

progress was made.”

“tell the team,” said Lasseter In an email sent in response to fact-checking questions, a spokeswoman for Walt Disney Animation Studios wrote:

“Jennifer feels this is very, very important: This was a story Jennifer and

Chris did together. This was a partnership. [The emails] Kristen shared were

based on conversations Jennifer was having with Chris daily. Chris is just as

much a part of these conversations as Jennifer, Kristen and Bobby….This is

[Chris Buck’s] film, first and foremost.”

CHAPTER EIGHT: ABSORBING DATA

multiplication quiz “Dante Williams” is a pseudonym used to protect the privacy of a student who was a minor when these events occurred.

“Peace Bowl” Ben Fischer, “Slaying Halts ‘Peace Bowl,’ ” Cincinnati Enquirer, August 13, 2007.

guide Cincinnati’s efforts Marie Bienkowski et al., Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An

Issue Brief (Washington, D.C.: U.S. Department of Education, Office of

Technology,

October

2012),

https://tech.ed.gov/wp-

content/uploads/2014/03/edm-la-brief.pdf.

“we were on board” For more on Elizabeth Holtzapple’s research and Cincinnati Public Schools’ approach to data usage, I recommend Elizabeth

Holtzapple, “Criterion-Related Validity Evidence for a Standards-Based

Teacher Evaluation System,” Journal of Personnel Evaluation in Education

17, no. 3 (2003): 207–19; Elizabeth Holtzapple, Report on the Validation of

Teachers Evaluation System Instructional Domain Ratings (Cincinnati:

Cincinnati Public Schools, 2001).

basic

educational

benchmarks

“South

Avondale

Elementary:

Transformation Model,” Ohio Department of Education, n.d.

the “Elementary Initiative” Information on the EI and other Cincinnati Public Schools reforms came from various sources, including Kim McGuire,

“In Cincinnati, They’re Closing the Achievement Gap,” Star Tribune, May

11, 2004; Alyson Klein, “Education Week, Veteran Educator Turns Around

Cincinnati Schools,” Education Week, February 4, 2013; Nolan Rosenkrans,

“Cincinnati Offers Toledo Schools a Road Map to Success,” The Blade, May

13, 2012; Gregg Anrig, “How to Turn an Urban School District Around—

Without Cheating,” The Atlantic, May 9, 2013; John Kania and Mark

Kramer, “Collective Impact,” Stanford Social Innovation Review 9, no. 1

(Winter 2011): 36–41; Lauren Morando Rhim, Learning How to Dance in the

Queen City: Cincinnati Public Schools’ Turnaround Initiative, Darden/Curry

Partnership for Leaders in Education (Charlottesville: University of Virginia,

2011); Emily Ayscue Hassel and Bryan C. Hassel, “The Big U Turn,”

Education Next 9, no. 1 (2009): 20–27; Rebecca Herman et al., Turning Around Chronically Low-Performing Schools: A Practice Guide

(Washington, D.C.: National Center for Education Evaluation and Regional

Assistance, Institute of Education Sciences, U.S. Department of Education,

2008); Guide to Understanding Ohio’s Accountability System, 2008–2009

(Columbus: Ohio Department of Education, 2009), Web; Daniela Doyle and

Lyria Boast, 2010 Annual Report: The University of Virginia School

Turnaround Specialist Program, Darden/Curry Partnership for Leaders in

Education, Public Impact (Charlottesville: University of Virginia, 2011);

Dana Brinson et al., School Turnarounds: Actions and Results, Public Impact

(Lincoln, Ill.: Center on Innovation and Improvement, 2008); L. M. Rhim

and S. Redding, eds., The State Role in Turnaround: Emerging Best Practices

(San Francisco: WestEd, 2014); William S. Robinson and LeAnn M.

Buntrock, “Turnaround Necessities,” The School Administrator 68, no. 3

(March 2011): 22–27; Susan McLester, “Turnaround Principals,” District

Administration (May 2011); Daniel Player and Veronica Katz, “School

Improvement in Ohio and Missouri: An Evaluation of the School Turnaround

Specialist Program” (CEPWC Working Paper Series no. 10, University of

Virginia, Curry School of Education, June 2013), Web; Alison Damast,

“Getting Principals to Think Like Managers,” Bloomberg Businessweek,

February 16, 2012; “CPS ‘Turnaround Schools’ Lift District Performance,”

The Cincinnati Herald, August 21, 2010; Dakari Aarons, “Schools Innovate

to Keep Students on Graduation Track,” Education Week, June 2, 2010;

“Facts at a Glance,” Columbia Public Schools K–12, n.d., Web.

how to use it The Cincinnati Public School system’s Elementary Initiative had other components in addition to instructing teachers in how to use data.

Those included using data and analysis to guide evidence-based decisions;

implementing a new principal evaluation system aligned to the district’s

strategic plan that included student performance scores; expanding school-

site learning teams of teachers to build capacity in all schools; training

primary and intermediate content specialists in core subjects; and becoming more family friendly and community engaged. “Using data and evidence, we

will improve practice, differentiate instruction, and track learning results for

every student,” the district wrote in a summary of the initiative. “Our goal is

to create a collaborative learning culture that involves families, is embraced

in schools and is supported by the Board, central office and the community.

Such a culture is at the heart of the elementary school initiative….Just as the

medical community uses diagnostics to determine treatment for critical care

patients, so are we using data and analysis with 15 critical care schools to reshape training, support and delivery of services aligned to the academic,

social and emotional needs of the students.” (“Elementary Initiative: Ready

for High School,” Cincinnati Public Schools, 2014, http://www.cps-

k12.org/academics/district-initiatives/elementary-initiative.) It is also worth

noting that, though everyone spoken to in reporting this chapter credits a

data-driven approach with fueling South Avondale’s transformation, they

also noted that such changes were possible only because of strong leadership

at the school and commitment from teachers.

inner-city reform “Elementary Initiative: Ready for High School.”

state math exam Ibid.; South Avondale Elementary School Ranking,”

School

Digger,

2014,

http://www.schooldigger.com/go/OH/schools/0437500379/school.aspx;

“South Avondale Elementary School Profile,” Great Schools, 2013, Web.

the school district read “School Improvement, Building Profiles, South Avondale,” Ohio Department of Education, 2014, Web.

but more useful For more on the role of data in classroom improvement, please see Thomas J. Kane et al., “Identifying Effective Classroom Practices

Using Student Achievement Data,” Journal of Human Resources 46, no 3

(2011): 587–613; Pam Grossman et al., “Measure for Measure: A Pilot Study

Linking English Language Arts Instruction and Teachers’ Value-Added to

Student Achievement” (CALDER Working Paper no. 45, Calder Urban

Institute, May 2010); Morgaen L. Donaldson, “So Long, Lake Wobegon?

Using Teacher Evaluation to Raise Teacher Quality,” Center for American

Progress, June 25, 2009, Web; Eric Hanushek, “Teacher Characteristics and

Gains in Student Achievement: Estimation Using Micro-Data,” The

American Economic Review 61, no. 2 (1971): 280–88; Elizabeth Holtzapple,

“Criterion-Related Validity Evidence for a Standards-Based Teacher

Evaluation System,” Journal of Personnel Evaluation in Education 17, no. 3

(2003): 207–19; Brian A. Jacob and Lars Lefgren, Principals as Agents:

Subjective Performance Measurement in Education (working paper no.

w11463, National Bureau of Economic Research, 2005); Brian A. Jacob,

Lars Lefgren, and David Sims, The Persistence of Teacher-Induced Learning

Gains (working paper no. w14065, National Bureau of Economic Research,

2008); Thomas J. Kane and Douglas O. Staiger, Estimating Teacher Impacts

on Student Achievement: An Experimental Evaluation (working paper no.

w14607, National Bureau of Economic Research, 2008); Anthony

Milanowski, “The Relationship Between Teacher Performance Evaluation

Scores and Student Achievement: Evidence from Cincinnati,” Peabody

Journal of Education 79, no. 4 (2004): 33–53; Richard J. Murnane and

Barbara R. Phillips, “What Do Effective Teachers of Inner-City Children

Have in Common?” Social Science Research 10, no. 1 (1981): 83–100;

Steven G. Rivkin, Eric A. Hanushek, and John F. Kain, “Teachers, Schools,

and Academic Achievement,” Econometrica 73, no. 2 (2005): 417–58.

less stressful Jessica L. Buck, Elizabeth McInnis, and Casey Randolph, The

New Frontier of Education: The Impact of Smartphone Technology in the

Classroom, American Society for Engineering Education, 2013 ASEE

Southeast Section Conference; Neal Lathia et al., “Smartphones for Large-

Scale Behavior Change Interventions,” IEEE Pervasive Computing 3 (2013):

66–73; “Sites That Help You Track Your Spending and Saving,” Money

Counts: Young Adults and Financial Literacy, NPR, May 18, 2011; Shafiq

Qaadri, “Meet a Doctor Who Uses a Digital Health Tracker and Thinks You

Should Too,” The Globe and Mail, September 4, 2014; Claire Cain Miller,

“Collecting Data on a Good Night’s Sleep,” The New York Times, March 10,

2014; Steven Beasley and Annie Conway, “Digital Media in Everyday Life:

A Snapshot of Devices, Behaviors, and Attitudes,” Museum of Science and

Industry, Chicago, 2011; Adam Tanner, “The Web Cookie Is Dying. Here’s

the Creepier Technology That Comes Next,” Forbes, June 17, 2013,

http://www.forbes.com/sites/adamtanner/2013/06/17/the-web-cookie-is-

dying-heres-the-creepier-technology-that-comes-next/.

harder to decide For more on information overload and information blindness, please see Martin J. Eppler and Jeanne Mengis, “The Concept of

Information Overload: A Review of Literature from Organization Science,

Accounting, Marketing, MIS, and Related Disciplines,” The Information

Society 20, no. 5 (2004): 325–44; Pamela Karr-Wisniewski and Ying Lu,

“When More Is Too Much: Operationalizing Technology Overload and

Exploring Its Impact on Knowledge Worker Productivity,” Computers in

Human Behavior 26, no. 5 (2010): 1061–72; Joseph M. Kayany,

“Information

Overload

and

Information

Myths,”

Itera,

n.d.,

http://www.itera.org/wordpress/wp-

content/uploads/2012/09/ITERA12_Paper15.pdf; Marta Sinclair and Neal M.

Ashkanasy, “Intuition Myth or a Decision-Making Tool?” Management

Learning 36, no. 3 (2005): 353–70.

blanket of powder Snow blindness can also refer to a burn of the cornea, which is the front surface of the eye, by ultraviolet B rays.

enroll in 401(k) plans Sheena S. Iyengar, Gur Huberman, and Wei Jiang,

“How Much Choice Is Too Much? Contributions to 401(k) Retirement

Plans,” Pension Design and Structure: New Lessons from Behavioral

Finance (Philadelphia: Pension Research Council, 2004): 83–95.

more than thirty plans In an email sent in response to fact-checking questions, Tucker Kuman, a colleague of the paper’s lead author, Sheena

Sethi-Iyengar, wrote: “What was observed in the analysis was that,

everything else being equal, every ten funds added was associated with a 1.5

percent to 2 percent drop in employee participation rate (peak participation—

75%—occurred when 2 funds were offered)….As the offerings increased in

number, the decline in participation rates is exacerbated. If you look at the graphic representation [Figure 5–2 in the paper] of the relationship between

participation and number of funds offered, you’ll notice we begin to see a

steeper decline in participation rates when the number of funds hits about 31.”

information overload Jeanne Mengis and Martin J. Eppler, “Seeing Versus Arguing the Moderating Role of Collaborative Visualization in Team

Knowledge Integration,” Journal of Universal Knowledge Management 1,

no. 3 (2006): 151–62; Martin J. Eppler and Jeanne Mengis, “The Concept of

Information Overload: A Review of Literature from Organization Science,

Accounting, Marketing, MIS, and Related Disciplines,” The Information

Society 20, no. 5 (2004): 325–44.

“winnowing” or “scaffolding” Fergus I. M. Craik and Endel Tulving,

“Depth of Processing and the Retention of Words in Episodic Memory,”

Journal of Experimental Psychology: General 104, no. 3 (1975): 268;

Monique Ernst and Martin P. Paulus, “Neurobiology of Decision Making: A

Selective Review from a Neurocognitive and Clinical Perspective,”

Biological Psychiatry 58, no. 8 (2005): 597–604; Ming Hsu et al., “Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making,”

Science 310, no. 5754 (2005): 1680–83.

hardly aware it’s occurring For more on the decision-making aspect of scaffolding and cognition, please see Gerd Gigerenzer and Wolfgang

Gaissmaier, “Heuristic Decision Making,” Annual Review of Psychology 62

(2011): 451–82; Laurence T. Maloney, Julia Trommershäuser, and Michael

S. Landy, “Questions Without Words: A Comparison Between Decision

Making Under Risk and Movement Planning Under Risk,” Integrated Models

of Cognitive Systems (2007): 297–313; Wayne Winston, Decision Making

Under Uncertainty (Ithaca, N.Y.: Palisade Corporation, 1999); Eric J.

Johnson and Elke U. Weber, “Mindful Judgment and Decision Making,”

Annual Review of Psychology 60 (2009): 53; Kai Pata, Erno Lehtinen, and Tago Sarapuu, “Inter-Relations of Tutor’s and Peers’ Scaffolding and

Decision-Making Discourse Acts,” Instructional Science 34, no. 4 (2006):

313–41; Priscilla Wohlstetter, Amanda Datnow, and Vicki Park, “Creating a

System for Data-Driven Decision Making: Applying the Principal-Agent

Framework,” School Effectiveness and School Improvement 19, no. 3 (2008):

239–59; Penelope L. Peterson and Michelle A. Comeaux, “Teachers’

Schemata for Classroom Events: The Mental Scaffolding of Teachers’

Thinking During Classroom Instruction,” Teaching and Teacher Education 3,

no. 4 (1987): 319–31; Darrell A. Worthy et al., “With Age Comes Wisdom:

Decision Making in Younger and Older Adults,” Psychological Science 22, no. 11 (2011): 1375–80; Pat Croskerry, “Cognitive Forcing Strategies in

Clinical Decisionmaking,” Annals of Emergency Medicine 41, no. 1 (2003):

110–20; Brian J. Reiser, “Scaffolding Complex Learning: The Mechanisms

of Structuring and Problematizing Student Work,” The Journal of the

Learning Sciences 13, no. 3 (2004): 273–304; Robert Clowes and Anthony F.

Morse, “Scaffolding Cognition with Words,” in Proceedings of the Fifth

International Workshop on Epigenetic Robotics: Modeling Cognitive

Development in Robotic Systems (Lund, Sweden: Lund University Cognitive

Studies, 2005), 101–5.

make a choice For more on disfluency, please see Adam L. Alter, “The Benefits of Cognitive Disfluency,” Current Directions in Psychological

Science 22, no. 6 (2013): 437–42; Adam L. Alter et al., “Overcoming

Intuition: Metacognitive Difficulty Activates Analytic Reasoning,” Journal

of Experimental Psychology: General 136, no. 4 (2007): 569; Adam L. Alter,

Drunk Tank Pink: And Other Unexpected Forces That Shape How We Think,

Feel, and Behave (New York: Penguin, 2013); Adam L. Alter et al.,

“Overcoming Intuition: Metacognitive Difficulty Activates Analytic

Reasoning,” Journal of Experimental Psychology: General 136, no. 4 (2007):

569; Adam L. Alter and Daniel M. Oppenheimer, “Effects of Fluency on

Psychological Distance and Mental Construal (or Why New York Is a Large

City, but New York Is a Civilized Jungle),” Psychological Science 19, no. 2

(2008): 161–67; Adam L. Alter and Daniel M. Oppenheimer, “Uniting the

Tribes of Fluency to Form a Metacognitive Nation,” Personality and Social

Psychology Review 13, no. 3 (2009): 219–35; John Hattie and Gregory C. R.

Yates, Visible Learning and the Science of How We Learn (London:

Routledge, 2013); Nassim Nicholas Taleb, Antifragile: Things That Gain

from Disorder (New York: Random House, 2012); Daniel M. Oppenheimer,

“The Secret Life of Fluency,” Trends in Cognitive Sciences 12, no. 6 (2008):

237–41; Edward T. Cokely and Colleen M. Kelley, “Cognitive Abilities and

Superior Decision Making Under Risk: A Protocol Analysis and Process

Model Evaluation,” Judgment and Decision Making 4, no. 1 (2009): 20–33;

Connor Diemand-Yauman, Daniel M. Oppenheimer, and Erikka B. Vaughan,

“Fortune Favors the Bold (and the Italicized): Effects of Disfluency on

Educational Outcomes,” Cognition 118, no. 1 (2011): 111–15; Hyunjin Song

and Norbert Schwarz, “Fluency and the Detection of Misleading Questions: Low Processing Fluency Attenuates the Moses Illusion,” Social Cognition

26, no. 6 (2008): 791–99; Anuj K. Shah and Daniel M. Oppenheimer, “Easy

Does It: The Role of Fluency in Cue Weighting,” Judgment and Decision

Making 2, no. 6 (2007): 371–79. In an email sent in response to fact-checking

questions, Adam Alter, a professor at NYU who has studied disfluency,

explained disfluency as “the sense of mental difficulty that people experience

when they try to process (make sense of) certain pieces of information—

complex words; text printed in ornate fonts; text printed against background

of a similar color; drawing dimly remembered ideas from memory; struggling

to remember a phone number; etc. You don’t have to be manipulating or

using data, per se, for an experience to be disfluent. Some of this turns on how you define data—it sounds like you’re defining it very broadly, so

perhaps your definition comes close to mine if you think of every cognitive

process as ‘using data.’ ”

easier to digest Alter wrote in an email that some recent work “challenges the disfluency literature….Some of my friends/colleagues have written

another piece [“Disfluent Fonts Don’t Help People Solve Math Problems”]

that shows how finicky the effect is; [and] how hard it can be to replicate at

least one of the effects (the cognitive reflection test effects).”

“using it in conversations” In an email sent in response to fact-checking questions, Adam Alter expanded on his quote to note that disfluency causes

learning to be “longer lasting, perhaps, but certainly deeper. We don’t

comment much on decay rates—how long the information is retained—but it

probably follows that ideas last longer when they’re processed more

deeply….The more they elaborate on that information, the more they tend to

remember it. That’s a general principle from cognitive psychology. If I ask

you to remember the word ‘balloon,’ you’ll remember it more easily if, at the

point of storing it in memory, you imagine a red balloon floating into the sky,

or you think of a baboon carrying a balloon, or you otherwise do more than

just trying to cram the word into your already overstuffed memory bank.”

pay their credit card bills Chase Manhattan Bank, now known as JPMorgan Chase, was provided with a summary of all facts contained in this chapter. A

representative for the company wrote: “Given that more than 15 years have passed [since] the merger of Bank One and J. P. Morgan Chase in 2004, it’s

been difficult to find the right internal sources for this.”

“pick up on things” In an email sent in response to fact-checking questions, Fludd wrote that there were other elements to her management style that she

believes contributed to her success: “I also was able to identify that the

collectors had different learning styles that caused them to interpret the data

in different ways that could either negatively or positively impact their

performance….Management would accuse me of spoiling my collectors

because sometimes I would cook them breakfast on the weekends. Food

always helped. Being a minister often helped me relate to the collectors and

assist them in ways that other managers couldn’t. I would visit family

members in the hospital, perform marriages, prayer requests. Collectors knew

I was a no nonsense manager, but they also knew I cared about

them….Knowing how to interpret data and explaining it in a way that is

meaningful and relevant is important. The collectors having access to data

that was relevant to their performance was important. However if you cannot

give the employee a road map on how to take the data that they are receiving

and show them how to get to their desired performance destination, then it

means nothing. How you relay that data is just as important. The important

thing every manager needs to remember is not to forget the human side of the

data they are relating.”

various experiments In an email sent in response to fact-checking questions, Niko Cantor wrote: “It is also true that Charlotte was a better manager than

most peers, more engaging, more enrolling her people in a quest to become

better. She did make the job feel more like a game. I think some of the effects

of the collectors listening better and therefore connecting better because the

collectors were more engaged were important.”

“So you’re Ms. Johnson” Johnson started her teaching career at Pleasant Hill Elementary, and then later joined South Avondale, serving as a teacher

coach.

over the PA system The “Hot Pencil Drills” were unique to South Avondale,

and not done in all of the schools participating in the Elementary Initiative.

Delia Morris was a “Delia Morris” is a pseudonym used to protect the privacy of a student who was a minor when these events occurred.

“the engineering design process” Yousef Haik and Tamer Shahin, Engineering Design Process (Independence, Ky.: Cengage Learning, 2010);

Clive L. Dym et al., Engineering Design: A Project-Based Introduction (New

York: Wiley, 2004); Atila Ertas and Jesse C. Jones, The Engineering Design

Process (New York: Wiley, 1996); Thomas J. Howard, Stephen J. Culley,

and Elies Dekoninck, “Describing the Creative Design Process by the

Integration of Engineering Design and Cognitive Psychology Literature,”

Design Studies 29, no. 2 (2008): 160–80.

teacher’s manual explained “What is the Engineering Design Process?”

Innovation

First

International,

http://curriculum.vexrobotics.com/curriculum/intro-to-engineering/what-is-

the-engineering-design-process.

their own experiences Stephen J. Hoch, “Availability and Interference in Predictive Judgment,” Journal of Experimental Psychology: Learning,

Memory, and Cognition 10, no. 4 (1984): 649.

question was framed In an email sent in response to fact-checking questions, the author of this study, Stephen Hoch, wrote: “The only other

thing that I might add is that old ideas can get in the way of new ideas, creating interference and essentially blocking the thought process. One way

to overcome the interference is to take a break so that the old ideas die down

in terms of their salience.”

hard to dislodge Irwin P. Levin, Sandra L. Schneider, and Gary J. Gaeth,

“All Frames Are Not Created Equal: A Typology and Critical Analysis of

Framing Effects,” Organizational Behavior and Human Decision Processes

76, no. 2 (1998): 149–88; Hilary A. Llewellyn-Thomas, M. June McGreal,

and Elaine C. Thiel, “Cancer Patients’ Decision Making and Trial-Entry

Preferences: The Effects of ‘Framing’ Information About Short-Term

Toxicity and Long-Term Survival,” Medical Decision Making 15, no. 1

(1995): 4–12; David E. Bell, Howard Raiffa, and Amos Tversky, Decision Making: Descriptive, Normative, and Prescriptive Interactions (Cambridge:

Cambridge University Press, 1988); Amos Tversky and Daniel Kahneman,

“Rational Choice and the Framing of Decisions,” The Journal of Business 59,

no. 4, part 2 (1986): S251–78.

“inside their heads” In response to a fact-checking email, Johnson wrote:

“The idea is that we think of a subset of the relevant information.”

program named “Gen-1” Lekan Oguntoyinbo, “Hall Sweet Home,” Diverse Issues in Higher Education 27, no. 25 (2011): 8; Dana Jennings, “Second Home for First Gens,” The New York Times, July 20, 2009.

the difference between students Pam A. Mueller and Daniel M.

Oppenheimer, “The Pen Is Mightier Than the Keyboard: Advantages of

Longhand over Laptop Note Taking,” Psychological Science 25, no. 6

(2014).

verbatim phrases In a note sent in response to fact-checking questions, the first author of this study, Pam Mueller of Princeton, wrote: “Only because a

lot of people (on the Internet) seem to assume that we didn’t randomly assign

participants to groups, and therefore the conclusions are invalid, it might be

worth mentioning that the two groups were, in fact, randomly assigned. We

did ask students about their underlying note-taking preference, but due to

small numbers of participants in certain conditions (e.g., longhand-preferring

students at Princeton assigned to the laptop condition) we can’t draw strong

conclusions about any interactions there. There is some suggestion that those

who preferred longhand in their regular note taking were more effective than

others when using a laptop (i.e., continuing to take shorter, non-verbatim

notes). One thing to note is that a strong majority of students at Princeton reported that they generally took notes on a laptop, while a majority of

UCLA students reported that they took notes longhand. It is heartening that

our second study (run at UCLA) did replicate our first study (run at

Princeton).”

the lecture’s content In a note sent in response to fact-checking questions,

Mueller wrote: “Laptop note-takers had far more content in their notes. Thus, we thought that the laptop note-takers’ performance would rebound when

they had a chance to look back on their notes—the laptop note takers just had

so much more information available at the time of study. However (as we

were quite surprised to find), it seems that if they didn’t process the

information at the time of encoding (i.e., during the lecture), the increased

quantity of notes didn’t help, or at least didn’t help within a short study

period. Perhaps with a longer time to study, they could piece together the

content of the lecture, but at that point, the process is pretty inefficient, and it

would be better to have taken ‘better’ (i.e., longhand-style, with less verbatim

overlap) notes the first time around.”

BY CHARLES DUHIGG

Smarter Faster Better

The Power of Habit

ABOUT THE AUTHOR

CHARLES DUHIGG is a Pulitzer Prize–winning investigative

reporter for The New York Times and the author of The Power of

Habit. He is a winner of the National Academies of Sciences,

National Journalism, and George Polk awards. A graduate of

Harvard Business School and Yale College, he lives in Brooklyn

with his wife and two children.

charlesduhigg.com

Facebook.com/charlesduhigg

@cduhigg

To inquire about booking Charles Duhigg for a speaking

engagement, please contact the Penguin Random House

Speakers Bureau at speakers@penguinrandomhouse.com.

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Document Outline

Title Page

Copyright

Contents

Introduction

1. Motivation: Reimagining Boot Camp, Nursing Home Rebellions, and the Locus of Control

2. Teams: Psychological Safety at Google and Saturday Night Live

3. Focus: Cognitive Tunneling, Air France Flight 447, and the Power of Mental Models

4. Goal Setting: Smart Goals, Stretch Goals, and the Yom Kippur War

5. Managing Others: Solving a Kidnapping with Lean and Agile Thinking and a Culture of Trust

6. Decision Making: Forecasting the Future (and Winning at Poker) with Bayesian Psychology

7. Innovation: How Idea Brokers and Creative Desperation Saved Disney’s Frozen

8. Absorbing Data: Turning Information into Knowledge in Cincinnati’s Public Schools

Appendix: A Reader’s Guide to Using These Ideas

Dedication

Acknowledgments

A Note on Sources

Notes

Other Titles

About the Author