# Thinking in Bets

**Author:** Annie Duke
**Year:** 2018
**Pages:** 227

> Full text extracted from PDF for on-demand reference. Use grep/search to locate concepts.


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Portfolio/Penguin
An imprint of Penguin Random House LLC
375 Hudson Street
New York, New York 10014
Copyright © 2018 by Annie Duke
Penguin supports copyright. Copyright fuels creativity, encourages diverse voices, promotes free speech,
and creates a vibrant culture. Thank you for buying an authorized edition of this book and for complying
with copyright laws by not reproducing, scanning, or distributing any part of it in any form without
permission. You are supporting writers and allowing Penguin to continue to publish books for every reader.
Library of Congress Cataloging-in-Publication Data Names: Duke, Annie, 1965-author.
Title: Thinking in bets : making smarter decisions when you don’t have all the facts / Annie Duke.
Description: New York : Portfolio, 2018. | Includes bibliographical references and index.
Identifiers: LCCN 2017042666 | ISBN 9780735216358 (hardback) | ISBN 9780735216365 (epub)
Subjects: LCSH: Management games. | Decision making. | BISAC: BUSINESS & ECONOMICS /
Decision-Making & Problem Solving. | PSYCHOLOGY
Cognitive Psychology. | BUSINESS &
ECONOMICS
Strategic Planning.
Classification: LCC HD30.6 .D85 2018 | DDC 658.4/0353—dc23 LC record available at
https://lccn.loc.gov/2017042666
Version_1

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To Lila and Henry Gleitman, generous of heart and
intellect

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CONTENTS
Title Page
Copyright
Dedication
INTRODUCTION
Why This Isn’t a Poker Book
CHAPTER 1
Life Is Poker, Not Chess
Pete Carroll and the Monday Morning Quarterbacks
The hazards of resulting
Quick or dead: our brains weren’t built for rationality
Two-minute warning
Dr. Strangelove
Poker vs. chess
A lethal battle of wits
“I’m not sure”: using uncertainty to our advantage
Redefining wrong
CHAPTER 2
Wanna Bet?
Thirty days in Des Moines
We’ve all been to Des Moines
All decisions are bets
Most bets are bets against ourselves
Our bets are only as good as our beliefs
Hearing is believing
“They saw a game”

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The stubbornness of beliefs
Being smart makes it worse
Wanna bet?
Redefining confidence
CHAPTER 3
Bet to Learn: Fielding the Unfolding Future
Nick the Greek, and other lessons from the Crystal Lounge
Outcomes are feedback
Luck vs. skill: fielding outcomes
Working backward is hard: the SnackWell’s Phenomenon
“If it weren’t for luck, I’d win every one”
All-or-nothing thinking rears its head again
People watching
Other people’s outcomes reflect on us
Reshaping habit
“Wanna bet?” redux
The hard way
CHAPTER 4
The Buddy System
“Maybe you’re the problem, do you think?”
The red pill or the blue pill?
Not all groups are created equal
The group rewards focus on accuracy
2
“One Hundred White Castles . . . and a large chocolate shake”: how accountability improves decision-
making
The group ideally exposes us to a diversity of viewpoints
Federal judges: drift happens
Social psychologists: confirmatory drift and Heterodox Academy
Wanna bet (on science)?
CHAPTER 5
Dissent to Win
CUDOS to a magician
Mertonian communism: more is more
Universalism: don’t shoot the message
Disinterestedness: we all have a conflict of interest, and it’s contagious
Organized skepticism: real skeptics make arguments and friends
Communicating with the world beyond our group

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CHAPTER 6
Adventures in Mental Time Travel
Let Marty McFly run into Marty McFly
Night Jerry
Moving regret in front of our decisions
A flat tire, the ticker, and a zoom lens
“Yeah, but what have you done for me lately?”
Tilt
Ulysses contracts: time traveling to precommit
Decision swear jar
Reconnaissance: mapping the future
Scenario planning in practice
Backcasting: working backward from a positive future
Premortems: working backward from a negative future
Dendrology and hindsight bias (or, Give the chainsaw a rest)
ACKNOWLEDGMENTS
NOTES
SELECTED BIBLIOGRAPHY AND RECOMMENDATIONS
FOR FURTHER READING
INDEX

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W
INTRODUCTION
Why This Isn’t a Poker Book
hen I was twenty-six, I thought I had my future mapped out. I had grown
up on the grounds of a famous New Hampshire prep school, where my
father chaired the English department. I had graduated from Columbia
University with degrees in English and psychology. I had attended graduate
school at the University of Pennsylvania, where I won a fellowship from the
National Science Foundation, earning a master’s and completing my doctoral
course work in cognitive psychology.
But I got sick right before finishing my dissertation. I took a leave of
absence, left Penn, got married, and moved to a small town in Montana. Not
surprisingly, my NSF fellowship didn’t cover my cross-country experiment in
adulting, so I needed money. My brother Howard, a professional poker player
who had already made the final table of the World Series of Poker by this time,
suggested I check out the legal poker games in Billings. This suggestion wasn’t
as random as it might sound. I grew up in a competitive, games-playing family,
and Howard had brought me out to Las Vegas a few times for vacations I
couldn’t otherwise
afford on my stipend. I had watched him play, and played in
a few low-stakes games myself.
I fell in love with poker right away. It wasn’t the bright lights of Vegas that
lured me in, but the thrill of playing and testing my skills in the basement of a
Billings bar named the Crystal Lounge. I had a lot to learn, but I was excited to
learn it. My plan was to earn some money during this break from school, stay on
the academic path, and continue playing poker as a hobby.
My temporary break turned into a twenty-year career as a professional poker
player. When I retired from playing in 2012, I had won a World Series of Poker
gold bracelet, the WSOP Tournament of Champions, and the NBC National
Heads-Up Championship, and earned more than $4 million in poker
tournaments. Howard, meanwhile, went on to win two World Series bracelets, a

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tournaments. Howard, meanwhile, went on to win two World Series bracelets, a
pair of titles at the Hall of Fame Poker Classic, two World Poker Tour
championships, and over $6.4 million in tournament prize money.
To say that I had strayed from the academic path might seem like an
understatement. But I realized pretty quickly that I hadn’t really left academics
so much as moved to a new kind of lab for studying how people learn and make
decisions. A hand of poker takes about two minutes. Over the course of that
hand, I could be involved in up to twenty decisions. And each hand ends with a
concrete result: I win money or I lose money. The result of each hand provides
immediate feedback on how your decisions are faring. But it’s a tricky kind of
feedback because winning and losing are only loose signals of decision quality.
You can win lucky hands and lose unlucky ones. Consequently, it’s hard to
leverage all that feedback for learning.
The prospect of some grizzled ranchers in Montana systematically taking
my money at a poker table forced me to find practical
ways to either solve this
learning puzzle or go broke. I was lucky, early in my career, to meet some
exceptional poker players and learn from them how they handled not only luck
and uncertainty but also the relationship between learning and decision-making.
Over time, those world-class poker players taught me to understand what a
bet really is: a decision about an uncertain future. The implications of treating
decisions as bets made it possible for me to find learning opportunities in
uncertain environments. Treating decisions as bets, I discovered, helped me
avoid common decision traps, learn from results in a more rational way, and
keep emotions out of the process as much as possible.
In 2002, thanks to my friend and super-successful poker player Erik Seidel
turning down a speaking engagement, a hedge-fund manager asked me to speak
to a group of traders and share some poker tips that might apply to securities
trading. Since then, I have spoken to professional groups across many industries,
looking inward at the approach I learned in poker, continually refining it, and
helping others apply it to decisions in financial markets, strategic planning,
human resources, law, and entrepreneurship.
The good news is that we can find practical work-arounds and strategies to
keep us out of the traps that lie between the decisions we’d like to be making and
the execution of those decisions. The promise of this book is that thinking in bets
will improve decision-making throughout our lives. We can get better at
separating outcome quality from decision quality, discover the power of saying,
“I’m not sure,” learn strategies to map out the future, become less reactive
decision-makers, build and sustain pods of fellow truthseekers to improve our

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decision-makers, build and sustain pods of fellow truthseekers to improve our
decision process, and recruit our past and future selves to make fewer emotional
decisions.
I didn’t become an always-rational, emotion-free decision-maker from
thinking in bets. I still made (and make) plenty of
mistakes. Mistakes, emotions,
losing—those things are all inevitable because we are human. The approach of
thinking in bets moved me
toward
objectivity, accuracy, and open-mindedness.
That movement compounds over time to create significant changes in our lives.
So this is not a book about poker strategy or gambling. It is, however, about
things poker taught me about learning and decision-making. The practical
solutions I learned in those smoky poker rooms turned out to be pretty good
strategies for anyone trying to be a better decision-maker.
• • •
Thinking in bets starts with recognizing that there are exactly two things that
determine how our lives turn out: the quality of our decisions and luck. Learning
to recognize the difference between the two is what thinking in bets is all about.

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CHAPTER 1
Life Is Poker, Not Chess
Pete Carroll and the Monday Morning Quarterbacks
One of the most controversial decisions in Super Bowl history took place in the
closing seconds of Super Bowl XLIX in 2015. The Seattle Seahawks, with
twenty-six seconds remaining and trailing by four points, had the ball on second
down at the New England Patriots’ one-yard line. Everybody expected
Seahawks coach Pete Carroll to call for a handoff to running back Marshawn
Lynch. Why wouldn’t you expect that call? It was a short-yardage situation and
Lynch was one of the best running backs in the NFL.
Instead, Carroll called for quarterback Russell Wilson to pass. New England
intercepted the ball, winning the Super Bowl moments later. The headlines the
next day were brutal:
USA Today
: “What on Earth Was Seattle Thinking with Worst Play Call in
NFL History?”
Washington Post
: “‘Worst Play-Call in Super Bowl History’ Will Forever
Alter Perception of Seahawks, Patriots”
FoxSports.com: “Dumbest Call in Super Bowl History Could Be
Beginning of the End for Seattle Seahawks”
Seattle Times
: “Seahawks Lost Because of the Worst Call in Super Bowl
History”
The
New Yorker
: “A Coach’s Terrible Super Bowl Mistake”
Although the matter was considered by nearly every pundit as beyond
debate, a few outlying voices argued that the play choice was sound, if not
brilliant. Benjamin Morris’s analysis on FiveThirtyEight.com and Brian Burke’s

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brilliant. Benjamin Morris’s analysis on FiveThirtyEight.com and Brian Burke’s
on Slate.com convincingly argued that the decision to throw the ball was totally
defensible, invoking clock-management and end-of-game considerations. They
also pointed out that an interception was an extremely unlikely outcome. (Out of
sixty-six passes attempted from an opponent’s one-yard line during the season,
zero had been intercepted. In the previous fifteen seasons, the interception rate in
that situation was about 2%.)
Those dissenting voices didn’t make a dent in the avalanche of criticism
directed at Pete Carroll. Whether or not you buy into the contrarian analysis,
most people didn’t want to give Carroll the credit for having thought it through,
or having
any
reason at all for his call. That raises the question: Why did
so
many people
so
strongly believe that Pete Carroll got it
so
wrong?
We can sum it up in four words: the play didn’t work.
Take a moment to imagine that Wilson completed the pass for a game-
winning touchdown. Wouldn’t the headlines change to “Brilliant Call” or
“Seahawks Win Super Bowl on Surprise Play” or “Carroll Outsmarts
Belichick”? Or imagine the pass had been incomplete and the Seahawks scored
(or didn’t) on a
third-or fourth-down running play. The headlines would be about
those other plays. What Pete Carroll called on second down would have been
ignored.
Carroll got unlucky. He had control over the quality of the play-call
decision, but not over how it turned out. It was exactly because he didn’t get a
favorable result that he took the heat. He called a play that had a high percentage
of ending in a game-winning touchdown or an incomplete pass (which would
have allowed two more plays for the Seahawks to hand off the ball to Marshawn
Lynch). He made a good-quality decision that got a bad result.
Pete Carroll was a victim of our tendency to equate the quality of a decision
with the quality of its outcome. Poker players have a word for this: “resulting.”
When I started playing poker, more experienced players warned me about the
dangers of resulting, cautioning me to resist the temptation to change my
strategy just because a few hands didn’t turn out well in the short run.
Pete Carroll understood that his universe of critics was guilty of resulting.
Four days after the Super Bowl, he appeared on the
Today
show and
acknowledged, “It was the worst
result
of a call ever,” adding, “The call would
have been a great one if we catch it. It would have been just fine, and nobody
would have thought twice about it.”
Why are we so bad at separating luck and skill? Why are we so
uncomfortable knowing that results can be beyond our control? Why do we

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uncomfortable knowing that results can be beyond our control? Why do we
create such a strong connection between results and the quality of the decisions
preceding them? How can we avoid falling into the trap of the Monday Morning
Quarterback, whether it is in analyzing someone else’s decision or in making
and reviewing the decisions in our own lives?
The hazards of resulting
Take a moment to imagine your best decision in the last year. Now take a
moment to imagine your worst decision.
I’m willing to bet that your best decision preceded a good result and the
worst decision preceded a bad result.
That is a safe bet for me because resulting isn’t just something we do from
afar. Monday Morning Quarterbacks are an easy target, as are writers and
bloggers providing instant analysis to a mass audience. But, as I found out from
my own experiences in poker, resulting is a routine thinking pattern that bedevils
all of us. Drawing an overly tight relationship between results and decision
quality affects our decisions every day, potentially with far-reaching,
catastrophic consequences.
When I consult with executives, I sometimes start with this exercise. I ask
group members to come to our first meeting with a brief description of their best
and worst decisions of the previous year. I have yet to come across someone
who doesn’t identify their best and worst
results
rather than their best and worst
decisions.
In a consulting meeting with a group of CEOs and business owners, one
member of the group identified firing the president of his company as his worst
decision. He explained, “Since we fired him, the search for a replacement has
been awful. We’ve had two different people on the job. Sales are falling. The
company’s not doing well. We haven’t had anybody come in who actually turns
out to be as good as he was.”
That sounds like a disastrous result, but I was curious to probe into why the
CEO thought the decision to fire his president was so bad (other than that it
didn’t work out).
He explained the decision process and the basis of the conclusion to fire the
president. “We looked at our direct competitors and comparable companies, and

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concluded we weren’t performing up to their level. We thought we could
perform and grow at that level and that it was probably a leadership issue.”
I asked whether the process included working with the president to
understand his skill gaps and what he could be doing better. The company had,
indeed, worked with him to identify his skill gaps. The CEO hired an executive
coach to work with him on improving his leadership skills, the chief weakness
identified.
In addition, after executive coaching failed to produce improved
performance, the company considered splitting the president’s responsibilities,
having him focus on his strengths and moving other responsibilities to another
executive. They rejected that idea, concluding that the president’s morale would
suffer, employees would likely perceive it as a vote of no confidence, and it
would put extra financial pressure on the company to split a position they
believed one person could fill.
Finally, the CEO provided some background about the company’s
experience making high-level outside hires and its understanding of the available
talent. It sounded like the CEO had a reasonable basis for believing they would
find someone better.
I asked the assembled group, “Who thinks this was a bad decision?” Not
surprisingly, everybody agreed the company had gone through a thoughtful
process and made a decision that was reasonable given what they knew at the
time.
It sounded like a bad result, not a bad decision. The imperfect relationship
between results and decision quality devastated the CEO and adversely affected
subsequent decisions regarding the company. The CEO had identified the
decision as a mistake solely because it didn’t work out. He obviously felt a lot of
anguish
and regret because of the decision. He stated very clearly that he thought
he should have known that the decision to fire the president would turn out
badly. His decision-making behavior going forward reflected the belief that he
made a mistake. He was not only resulting but also succumbing to its
companion, hindsight bias. Hindsight bias is the tendency, after an outcome is
known, to see the outcome as having been inevitable. When we say, “I should
have known that would happen,” or, “I should have seen it coming,” we are
succumbing to hindsight bias.
Those beliefs develop from an overly tight connection between outcomes
and decisions. That is typical of how we evaluate our past decisions. Like the
army of critics of Pete Carroll’s decision to pass on the last play of the Super
Bowl, the CEO had been guilty of resulting, ignoring his (and his company’s)

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Bowl, the CEO had been guilty of resulting, ignoring his (and his company’s)
careful analysis and focusing only on the poor outcome. The decision didn’t
work out, and he treated that result as if it were an inevitable consequence rather
than a probabilistic one.
In the exercise I do of identifying your best and worst decisions, I never
seem to come across anyone who identifies a bad decision where they got lucky
with the result, or a well-reasoned decision that didn’t pan out. We link results
with decisions even though it is easy to point out indisputable examples where
the relationship between decisions and results isn’t so perfectly correlated. No
sober person thinks getting home safely after driving drunk reflects a good
decision or good driving ability. Changing future decisions based on that lucky
result is dangerous and unheard of (unless you are reasoning this out while drunk
and obviously deluding yourself).
Yet this is exactly what happened to that CEO. He changed his behavior
based on the quality of the result rather than the
quality of the decision-making
process. He decided he drove better when he was drunk.
Quick or dead: our brains weren’t built for rationality
The irrationality displayed by Pete Carroll’s critics and the CEO should come as
no surprise to anyone familiar with behavioral economics. Thanks to the work of
many brilliant psychologists, economists, cognitive researchers, and
neuroscientists, there are a number of excellent books that explain why humans
are plagued by certain kinds of irrationality in decision-making. (If you are
unaware of these books, see the Selected Bibliography and Recommendations
for Further Reading.) But here’s a summary.
To start, our brains evolved to create certainty and order. We are
uncomfortable with the idea that luck plays a significant role in our lives. We
recognize the existence of luck, but we resist the idea that, despite our best
efforts, things might not work out the way we want. It feels better for us to
imagine the world as an orderly place, where randomness does not wreak havoc
and things are perfectly predictable. We evolved to see the world that way.
Creating order out of chaos has been necessary for our survival.
When our ancestors heard rustling on the savanna and a lion jumped out,
making a connection between “rustling” and “lions” could save their lives on
later occasions. Finding predictable connections is, literally, how our species

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survived. Science writer, historian, and skeptic Michael Shermer, in
The
Believing Brain
, explains why we have historically (and prehistorically) looked
for connections even if they were doubtful or false. Incorrectly interpreting
rustling from the wind as an oncoming lion is called a type I error, a false
positive. The consequences of such an error were much less grave than those of
a type II error, a false negative. A false negative could have been fatal: hearing
rustling and always assuming it’s the wind would have gotten our ancestors
eaten, and we wouldn’t be here.
Seeking certainty helped keep us alive all this time, but it can wreak havoc
on our decisions in an uncertain world. When we work backward from results to
figure out why those things happened, we are susceptible to a variety of
cognitive traps, like assuming causation when there is only a correlation, or
cherry-picking data to confirm the narrative we prefer. We will pound a lot of
square pegs into round holes to maintain the illusion of a tight relationship
between our outcomes and our decisions.
Different brain functions compete to control our decisions. Nobel laureate
and psychology professor Daniel Kahneman, in his 2011 best-selling
Thinking,
Fast and Slow
, popularized the labels of “System 1” and “System 2.” He
characterized System 1 as “fast thinking.” System 1 is what causes you to hit the
brakes the instant someone jumps into the street in front of your car. It
encompasses reflex, instinct, intuition, impulse, and automatic processing.
System 2, “slow thinking,” is how we choose, concentrate, and expend mental
energy. Kahneman explains how System 1 and System 2 are capable of dividing
and conquering our decision-making but work mischief when they conflict.
I particularly like the descriptive labels “reflexive mind” and “deliberative
mind” favored by psychologist Gary Marcus. In his 2008 book,
Kluge: The
Haphazard Evolution of the Human Mind
, he wrote, “Our thinking can be
divided into two streams, one that is fast, automatic, and largely unconscious,
and another that is
slow, deliberate, and judicious.” The first system, “the
reflexive system, seems to do its thing rapidly and automatically, with or without
our conscious awareness.” The second system, “the deliberative system . . .
deliberates, it considers, it chews over the facts.”
The differences between the systems are more than just labels. Automatic
processing originates in the evolutionarily older parts of the brain, including the
cerebellum, basal ganglia, and amygdala. Our deliberative mind operates out of
the prefrontal cortex.
Colin Camerer, a professor of behavioral economics at Caltech and leading
speaker and researcher on the intersection of game theory and neuroscience,

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speaker and researcher on the intersection of game theory and neuroscience,
explained to me the practical folly of imagining that we could just get our
deliberative minds to do more of the decision-making work. “We have this thin
layer of prefrontal cortex made just for us, sitting on top of this big animal brain.
Getting this thin little layer to handle more is unrealistic.” The prefrontal cortex
doesn’t control most of the decisions we make every day. We can’t
fundamentally get more out of that unique, thin layer of prefrontal cortex. “It’s
already overtaxed,” he told me.
These are the brains we have and they aren’t changing anytime soon.
*
Making more rational decisions isn’t just a matter of willpower or consciously
handling more decisions in deliberative mind. Our deliberative capacity is
already maxed out. We don’t have the option, once we recognize the problem, of
merely shifting the work to a different part of the brain, as if you hurt your back
lifting boxes and shifted to relying on your leg muscles.
Both deliberative and reflexive mind are necessary for our survival and
advancement. The big decisions about what we want
to accomplish recruit the
deliberative system. Most of the decisions we execute on the way to achieving
those goals, however, occur in reflexive mind. The shortcuts built into the
automatic processing system kept us from standing around on the savanna,
debating the origin of a potentially threatening sound while its source devoured
us. Those shortcuts keep us alive, routinely executing the thousands of decisions
that make it possible for us to live our daily lives.
We need shortcuts, but they come at a cost. Many decision-making missteps
originate from the pressure on the reflexive system to do its job fast and
automatically. No one wakes up in the morning and says, “I want to be closed-
minded and dismissive of others.” But what happens when we’re focused on
work and a fluff-headed coworker approaches? Our brain is already using body
language and curt responses to get rid of them without flouting conventions of
politeness. We don’t deliberate over this; we just do it. What if they had a useful
piece of information to share? We’ve tuned them out, cut them short, and are
predisposed to dismiss anything we do pick up that varies from what we already
know.
Most of what we do daily exists in automatic processing. We have habits
and defaults that we rarely examine, from gripping a pencil to swerving to avoid
an auto accident. The challenge is not to change the way our brains operate but
to figure out how to work within the limitations of the brains we already have.
Being aware of our irrational behavior and wanting to change is not enough, in
the same way that knowing that you are looking at a visual illusion is not enough
to make the illusion go away. Daniel Kahneman used the famous Müller-Lyer

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to make the illusion go away. Daniel Kahneman used the famous Müller-Lyer
illusion to illustrate this.
MÜLLER-LYER ILLUSION
Which of these three lines is longest? Our brain sends us the signal that the
second line is the longest, but you can see from adding the measurement lines
that they are the same length.
We can measure the lines to confirm they are the same length, but we can’t
make ourselves unsee the illusion.
What we can do is look for practical work-arounds, like carrying around a
ruler and knowing when to use it to check against how your brain processes what
you see. It turns out that poker is a great place to find practical strategies to get
the execution of our decisions to align better with our goals. Understanding how

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poker players think can help us deal with the decision challenges that
bedevil us
in our workplaces, financial lives, relationships—even in deciding whether or
not passing the ball was a brilliant play.
Two-minute warning
Our goal is to get our reflexive minds to execute on our deliberative minds’ best
intentions. Poker players don’t need to know the underlying science to
understand the difficulty of reconciling the two systems. Poker players have to
make multiple decisions with significant financial consequences in a compressed
time frame, and do it in a way that lassoes their reflexive minds to align with
their long-term goals. This makes the poker table a unique laboratory for
studying decision-making.
Every poker hand requires making at least one decision (to fold your starting
cards or play them), and some hands can require up to twenty decisions. During
a poker game in a casino card room, players get in about thirty hands per hour.
An average hand of poker takes about two minutes to complete, including the
time it takes for the dealer to gather, shuffle, and deal the cards between hands.
Poker sessions typically last for several hours, with many decisions in every
hand. This means a poker player makes hundreds of decisions per session, all of
which take place at breakneck speed.
The etiquette and rules of the game discourage players from slowing down
the game to deliberate, even when huge financial consequences ride on the
decision. If a player takes extra time, another player can “call the clock” on
them. This gives the deliberating player all of
seventy seconds
to now make up
their mind. That is an eternity in poker time.
Every hand (and therefore every decision) has immediate financial
consequences. In a tournament or a high-stakes game, each decision can be
worth more than the cost of an average three-bedroom house, and players have
to make those decisions more quickly than we decide what to order in a
restaurant. Even at lower stakes, most or all of the money a player has on the
table is potentially at stake in every decision. Poker players, as a result, must
become adept at in-the-moment decision-making or they won’t survive in the
profession. That means finding ways to execute their best intentions (deliberated
in advance) within the constraints of the speed expected at the table. Making a

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living at poker requires interpolating between the deliberative and reflexive
systems. The best players must find ways to harmonize otherwise irresolvable
conflicts.
In addition, once the game is over, poker players must learn from that
jumbled mass of decisions and outcomes, separating the luck from the skill, the
signal from the noise, and guarding against resulting. That’s the only way to
improve, especially when those same under-pressure situations will recur in a
variety of forms.
Solving the problem of how to execute is even more important than innate
talent to succeed in poker. All the talent in the world won’t matter if a player
can’t execute; avoiding common decision traps, learning from results in a
rational way, and keeping emotions out of the process as much as possible.
Players with awe-inspiring talent clean up on their best nights but go broke
plenty of other nights if they haven’t confronted this challenge. The poker
players who stand the test of time have a variety of talents, but what they share is
the ability to execute in the face of these limitations.
We all struggle to execute our best intentions. Poker players have the same
struggle, with the added challenges of time pressure,
in-your-face uncertainty,
and immediate financial consequences. That makes poker a great place to find
innovative approaches to overcoming this struggle. And the value of poker in
understanding decision-making has been recognized in academics for a long
time.
Dr. Strangelove
It’s hard for a scientist to become a household name. So it shouldn’t be
surprising that for most people the name John von Neumann doesn’t ring a bell.
That’s a shame because von Neumann is a hero of mine, and should be to
anyone committed to making better decisions. His contributions to the science of
decision-making were immense, and yet they were just a footnote in the short
life of one of the greatest minds in the history of scientific thought. (And, not
coincidentally, he was a poker player.)
After a twenty-year period in which he contributed to practically every
branch of mathematics, this is what he did in the last ten years of his life: played
a key role on the Manhattan Project, pioneered the physics behind the hydrogen
bomb, developed the first computers, figured out the optimal way to route

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bomb, developed the first computers, figured out the optimal way to route
bombers and choose targets at the end of World War II, and created the concept
of mutually assured destruction (MAD), the governing geopolitical principle of
survival throughout the Cold War. Even after being diagnosed with cancer in
1955 at the age of fifty-two, he served in the first civilian agency overseeing
atomic research and development, attending meetings, though in great pain, in a
wheelchair for as long as he was physically able.
Despite all he accomplished in science, somehow von
Neumann’s legacy in
popular culture is as one of the models for the title character in Stanley
Kubrick’s apocalyptic comedy,
Dr. Strangelove
: a heavily accented, crumpled,
wheelchair-bound genius whose strategy of relying on mutually assured
destruction goes awry when an insane general sends a single bomber on an
unauthorized mission that could trigger the automated firing of all American and
Soviet nuclear weapons.
In addition to everything else he accomplished, John von Neumann is also
the father of game theory. After finishing his day job on the Manhattan Project,
he collaborated with Oskar Morgenstern to publish
Theory of Games and
Economic Behavior
in 1944. The Boston Public Library’s list of the “100 Most
Influential Books of the Century” includes
Theory of Games.
William
Poundstone, author of a widely read book on game theory,
Prisoner’s Dilemma
,
called it “one of the most influential and least-read books of the twentieth
century.” The introduction to the sixtieth-anniversary edition pointed out how
the book was instantly recognized as a classic. Initial reviews in the most
prestigious academic journals heaped it with praise, like “one of the major
scientific achievements of the first half of the twentieth century” and “ten more
such books and the progress of economics is assured.”
Game theory revolutionized economics, evidenced by at least eleven
economics Nobel laureates connected with game theory and its decision-making
implications, including John Nash (a student of von Neumann’s), whose life
story was chronicled in the Oscar-winning film
A Beautiful Mind.
Game theory
has broad applications outside economics, informing the behavioral sciences
(including psychology and sociology) as well as political science, biomedical
research, business, and numerous other fields.
Game theory was succinctly defined by economist Roger
Myerson (one of
the game-theory Nobel laureates) as “the study of mathematical models of
conflict and cooperation between intelligent rational decision-makers.” Game
theory is the modern basis for the study of the bulk of our decision-making,

## Page 22

addressing the challenges of changing conditions, hidden information, chance,
and multiple people involved in the decisions. Sound familiar?
Fortunately, you don’t need to know any more than this about game theory
to understand its relevance. And the important thing for this book is that John
von Neumann modeled game theory on a stripped-down version of poker.
Poker vs. chess
In
The Ascent of Man
, scientist Jacob Bronowski recounted how von Neumann
described game theory during a London taxi ride. Bronowski was a chess
enthusiast and asked him to clarify. “You mean, the theory of games like chess?”
Bronowski quoted von Neumann’s response: “‘No, no,’ he said. ‘Chess is
not a game. Chess is a well-defined form of computation. You may not be able
to work out the answers, but in theory there must be a solution, a right procedure
in any position. Now, real games,’ he said, ‘are not like that at all. Real life is not
like that. Real life consists of bluffing, of little tactics of deception, of asking
yourself what is the other man going to think I mean to do. And that is what
games are about in my theory.’”
The decisions we make in our lives—in business, saving and spending,
health and lifestyle choices, raising our children, and relationships—easily fit
von Neumann’s definition of “real games.” They involve uncertainty, risk, and
occasional deception,
prominent elements in poker. Trouble follows when we
treat life decisions as if they were chess decisions.
Chess contains no hidden information and very little luck. The pieces are all
there for both players to see. Pieces can’t randomly appear or disappear from the
board or get moved from one position to another by chance. No one rolls dice
after which, if the roll goes against you, your bishop is taken off the board. If
you lose at a game of chess, it must be because there were better moves that you
didn’t make or didn’t see. You can theoretically go back and figure out exactly
where you made mistakes. If one chess player is more than just a bit better than
another, it is nearly inevitable the better player will win (if they are white) or, at
least, draw (if they are black). On the rare occasions when a lower-ranked grand
master beats a Garry Kasparov, Bobby Fischer, or Magnus Carlsen, it is because
the higher-ranked player made identifiable, objective mistakes, allowing the
other player to capitalize.
Chess, for all its strategic complexity, isn’t a great model for decision-

## Page 23

Chess, for all its strategic complexity, isn’t a great model for decision-
making in life, where most of our decisions involve hidden information and a
much greater influence of luck. This creates a challenge that doesn’t exist in
chess: identifying the relative contributions of the decisions we make versus luck
in how things turn out.
Poker, in contrast, is a game of
incomplete information
. It is a game of
decision-making under conditions of uncertainty over time. (Not coincidentally,
that is close to the definition of game theory.) Valuable information remains
hidden. There is also an element of luck in any outcome. You could make the
best possible decision at every point and still lose the hand, because you don’t
know what new cards will be dealt and revealed. Once the game is finished and
you try to learn from the results, separating the quality of your decisions from
the influence of luck is difficult.
In chess, outcomes correlate more tightly with decision quality. In poker, it
is much easier to get lucky and win, or get unlucky and lose. If life were like
chess, nearly every time you ran a red light you would get in an accident (or at
least receive a ticket). If life were like chess, the Seahawks would win the Super
Bowl every time Pete Carroll called that pass play.
But life is more like poker. You could make the smartest, most careful
decision in firing a company president and still have it blow up in your face. You
could run a red light and get through the intersection safely—or follow all the
traffic rules and signals and end up in an accident. You could teach someone the
rules of poker in five minutes, put them at a table with a world champion player,
deal a hand (or several), and the novice could beat the champion. That could
never happen in chess.
Incomplete information poses a challenge not just for split-second decision-
making, but also for learning from past decisions. Imagine my difficulty as a
poker player in trying to figure out if I played a hand correctly when my
opponents’ cards were never revealed. If the hand concluded after I made a bet
and my opponents folded, all I know is that I won the chips. Did I play poorly
and get lucky? Or did I play well?
If we want to improve in any game—as well as in any aspect of our lives—
we have to learn from the results of our decisions. The quality of our lives is the
sum of decision quality plus luck. In chess, luck is limited in its influence, so it’s
easier to read the results as a signal of decision quality. That more tightly tethers
chess players to rationality. Make a mistake and your opponent’s play points it
out, or it is capable of analysis afterward. There is always a theoretically right
answer out there. If you lose, there is little room to off-load losing to any other

## Page 24

explanation than your inferior decision-making. You’ll almost never hear a chess
player say, “I was robbed in that game!” or, “I played perfectly and caught some
terrible breaks.” (Walk the hallways during a break in a poker tournament and
you’ll hear a lot of that.)
That’s chess, but life doesn’t look like that. It looks more like poker, where
all that uncertainty gives us the room to deceive ourselves and misinterpret the
data. Poker gives us the leeway to make mistakes that we never spot because we
win the hand anyway and so don’t go looking for them, or the leeway to do
everything right, still lose, and treat the losing result as proof that we made a
mistake. Resulting, assuming that our decision-making is good or bad based on a
small set of outcomes, is a pretty reasonable strategy for learning in chess. But
not in poker—or life.
Von Neumann and Morgenstern understood that the world doesn’t easily
reveal the objective truth. That’s why they based game theory on poker. Making
better decisions starts with understanding this: uncertainty can work a lot of
mischief.
A lethal battle of wits
In one of the more famous scenes in
The

Princess Bride,
the Dread Pirate
Roberts (the love-besotted Westley) catches up to Vizzini, the mastermind who
kidnapped Princess Buttercup. Having vanquished Fezzik the Giant in a battle of
strength and having outdueled swordsman Inigo Montoya, the Dread Pirate
Roberts proposes he and Vizzini compete in a lethal battle of wits, which
provides a great demonstration of the peril of making decisions with incomplete
information. The pirate produces a packet of deadly iocane powder and, hiding
two goblets of wine from view, he empties the packet, and puts one goblet in
front of himself and
the other in front of Vizzini. Once Vizzini chooses a goblet,
they will both drink “and find out who is right and who is dead.”
“It’s all so simple,” Vizzini scoffs. “All I have to do is deduce, from what I
know of you, the way your mind works. Are you the kind of man who would put
the poison into his own glass, or into the glass of his enemy?” He provides a
dizzying series of reasons why the poison can’t (or must) be in one cup, and then
in the other. His rant accounts for cleverness, anticipating cleverness, iocane’s
origin (the criminal land of Australia), untrustworthiness, anticipating
untrustworthiness, and dueling presumptions related to Westley defeating the

## Page 25

untrustworthiness, and dueling presumptions related to Westley defeating the
giant and the swordsman.
While explaining all this, Vizzini diverts Westley’s attention, switches the
goblets, and declares that they should drink from the goblets in front of them.
Vizzini pauses for a moment and, when he sees Westley drink from his own
goblet, confidently drinks from the other.
Vizzini roars with laughter. “You fell victim to one of the classic blunders.
The most famous is ‘Never get involved in a land war in Asia,’ but only slightly
less well known is this: ‘Never go in against a Sicilian when death is on the
line.’”
In the midst of laughing, Vizzini falls over, dead. Buttercup says, “To think,
all that time it was your cup that was poisoned.”
Westley tells her, “They were both poisoned. I’ve spent the last two years
building up immunity to iocane powder.”
Like all of us, Vizzini didn’t have all the facts. He considered himself a
genius without equal: “Let me put it this way. Have you ever heard of Plato,
Aristotle, Socrates?
Morons
.” But, also like all of us, he underestimated the
amount and effect of what he didn’t know.
Suppose someone says, “I flipped a coin and it landed heads four times in a
row. How likely is that to occur?”
It feels like that should be a pretty easy question to answer. Once we do the
math on the probability of heads on four consecutive 50-50 flips, we can
determine that would happen 6.25% of the time (.50 × .50 × .50 × .50).
That’s making the same mistake as Vizzini. The problem is that we came to
this answer without knowing anything about the coin or the person flipping it. Is
it a two-sided coin or three-sided or four? If it is two-sided, is it a two-headed
coin? Even if the coin is two-sided (heads and tails), is the coin weighted to land
on heads more often than tails (but maybe not always)? Is the flipper a magician
who is capable of influencing how the coin lands? This information is all
incomplete, yet we answered the question as if we had examined the coin and
knew everything about it. We never considered that both goblets might be
poisoned. (“Inconceivable” would have been Vizzini’s term, had he been able to
comment on his own death.)
Now if that person flipped the coin 10,000 times, giving us a sufficiently
large sample size, we could figure out, with some certainty, whether the coin is
fair. Four flips simply isn’t enough to determine much about the coin.
We make this same mistake when we look for lessons in life’s results. Our
lives are too short to collect enough data from our own experience to make it

## Page 26

easy to dig down into decision quality from the small set of results we
experience. If we buy a house, fix it up a little, and sell it three years later for
50% more than we paid, does that mean we are smart at buying and selling
property, or at fixing up houses? It could, but it could also mean there was a big
upward trend in the market and buying almost any piece of property would have
made just as much money. Or maybe buying that same house and not fixing it up
at all might have resulted in the same (or even better) profit. A lot of previously
successful
house flippers had to face that real possibility between 2007 and
2009.
When someone asks you about a coin they flipped four times, there is a
correct answer: “I’m not sure.”
“I’m not sure”: using uncertainty to our advantage
Just as we have problems with resulting and hindsight bias, when we evaluate
decisions solely on how they turn out, we have a mirror-image problem in
making prospective decisions. We get only one try at any given decision—one
flip of the coin—and that puts great pressure on us to feel we have to be certain
before acting, a certainty that necessarily will overlook the influences of hidden
information and luck.
Famed novelist and screenwriter William Goldman (who wrote
The
Princess Bride
, as well as
Misery
and
Butch Cassidy and the Sundance Kid
)
reflected on his experiences working with actors like Robert Redford, Steve
McQueen, Dustin Hoffman, and Paul Newman at the height of their successful
careers. What did it mean to be a “movie star”? He quoted an actor who
explained the type of characters he wanted to play: “I don’t want to be the man
who learns. I want to be the man who
knows
.”
We are discouraged from saying “I don’t know” or “I’m not sure.” We
regard those expressions as vague, unhelpful, and even evasive. But getting
comfortable with “I’m not sure” is a vital step to being a better decision-maker.
We have to make peace with
not knowing.
Embracing “I’m not sure” is difficult. We are trained in school that saying “I
don’t know” is a bad thing. Not knowing in
school is considered a failure of
learning. Write “I don’t know” as an answer on a test and your answer will be
marked wrong.

## Page 27

Admitting that we don’t know has an undeservedly bad reputation. Of
course, we want to encourage acquiring knowledge, but the first step is
understanding what we don’t know. Neuroscientist Stuart Firestein’s book
Ignorance: How It Drives Science
champions the virtue of recognizing the limits
of our knowledge. (You can get a taste of the book by watching his TED Talk,
“The Pursuit of Ignorance.”) In the book and the talk, Firestein points out that in
science, “I don’t know” is not a failure but a necessary step toward
enlightenment. He backs this up with a great quote from physicist James Clerk
Maxwell: “Thoroughly conscious ignorance is the prelude to every real advance
in science.” I would add that this is a prelude to every great decision that has
ever been made.
What makes a decision great is not that it has a great outcome. A great
decision is the result of a good process, and that process must include an attempt
to accurately represent our own state of knowledge. That state of knowledge, in
turn, is some variation of “I’m not sure.”
“I’m not sure” does not mean that there is no objective truth. Firestein’s
point is, in fact, that acknowledging uncertainty is the first step in executing on
our goal to get closer to what is objectively true. To do this, we need to stop
treating “I don’t know” and “I’m not sure” like strings of dirty words
.
What if we shifted our definition of “I don’t know” from the negative frame
(“I have no idea” or “I know nothing about that,” which feels like we lack
competence or confidence) to a more neutral frame? What if we thought about it
as recognizing that, although we might know something about the chances of
some event occurring, we are still not sure how things will turn out in
any given
instance? That is just the truth. If we accept that, “I’m not sure” might not feel so
bad.
What good poker players and good decision-makers have in common is their
comfort with the world being an uncertain and unpredictable place. They
understand that they can almost never know exactly how something will turn
out. They embrace that uncertainty and, instead of focusing on being sure, they
try to figure out how
unsure
they are, making their best guess at the chances that
different outcomes will occur. The accuracy of those guesses will depend on
how much information they have and how experienced they are at making such
guesses. This is part of the basis of all bets.
To be sure, an experienced poker player is more likely to make a better
guess than a novice player at determining the chances they will win or lose a
hand. The experienced player knows the math better and is better able to narrow
down what their opponents’ cards might be based on how players behave with

## Page 28

down what their opponents’ cards might be based on how players behave with
certain types of hands. They will also be better at figuring out the choices their
opponents are likely to make with those cards. So, yes, more experience will
allow the player to narrow down the possibilities. None of that experience,
however, makes it possible for a poker player to know how any given hand will
turn out.
This is true in any field. An expert trial lawyer will be better than a new
lawyer at guessing the likelihood of success of different strategies and picking a
strategy on this basis. Negotiating against an adversary whom we have
previously encountered gives us a better guess at what our strategy should be.
An expert in any field will have an advantage over a rookie. But neither the
veteran nor the rookie can be sure what the next flip will look like. The veteran
will just have a better guess.
It is often the case that our best choice doesn’t even have a
particularly high
likelihood of succeeding. A trial lawyer with a tough case could be choosing
among strategies that are all more likely to fail than to succeed. The goal of a
lawyer in that situation is to identify the different possible strategies, figure out
their best guess of the chance of success for each unpromising alternative, and
pick the least awful one to maximize the quality of the outcome for their client.
That’s true in any business. Start-ups have very low chances of succeeding but
they try nonetheless, attempting to find the best strategy to achieve the big win,
even though none of the strategies is highly likely to create success for the
company. This is still worthwhile because the payoff can be so large.
There are many reasons why wrapping our arms around uncertainty and
giving it a big hug will help us become better decision-makers. Here are two of
them. First, “I’m not sure” is simply a more accurate representation of the world.
Second, and related, when we accept that we can’t be sure, we are less likely to
fall into the trap of black-and-white thinking.
Imagine you’re stepping on a traditional medical scale. It has two weight
bars, one with notches at fifty-pound intervals and the other with notches at one-
pound intervals. This allows the user to measure their weight down to the pound.
What would happen if your doctor used a scale with only one bar with just two
notches, one at fifty pounds and one at five hundred pounds, with no way to
measure anything in between? Good luck getting medical advice after the person
weighing you writes one or the other on your chart. You could only be morbidly
obese or severely underweight. It would be impossible to make good decisions
about your weight with such a poor model.
The same holds true for just about all of our decisions. If we misrepresent

## Page 29

the world at the extremes of right and wrong, with
no shades of grey in between,
our ability to make good choices—choices about how we are supposed to be
allocating our resources, what kind of decisions we are supposed to be making,
and what kind of actions we are supposed to be taking—will suffer.
The secret is to make peace with walking around in a world where we
recognize that we are not sure and that’s okay. As we learn more about how our
brains operate, we recognize that we don’t perceive the world objectively. But
our goal should be to
try
.
Redefining wrong
When I attend charity poker tournaments, I will often sit in as the dealer and
provide a running commentary at the final table. The atmosphere at these final
tables is fun and raucous. Everyone running the event has had a long night and is
breathing a sigh of relief. There is typically a big crowd around the table
including friends and families of the players, rooting them on (or vocally rooting
against them). If people have been drinking, then . . . people have been drinking.
Everyone is having a good time.
When players have put all their chips in the pot, there is no more betting on
the hand. After an all-in situation, the players in the hand turn their cards faceup
on the table so that everyone can see them before I deal the remaining cards.
This makes it fun for the audience, because they get to see each player’s position
in the hand and the drama mounts. With the cards faceup, I can determine the
likelihood each player will win the hand, and announce the percentage of the
time each hand will win in the long run.
At one such tournament, I told the audience that one player
would win 76%
of the time and the other would win 24% of the time. I dealt the remaining cards,
the last of which turned the 24% hand into the winner. Amid the cheers and
groans, someone in the audience called out, “Annie, you were wrong!”
In the same spirit that he said it, I explained that I wasn’t. “I said that would
happen 24% of the time. That’s not zero. You got to see part of the 24%!”
A few hands later, almost the same thing happened. Two players put all of
their chips in the pot and they turned their cards faceup. One player was 18% to
win and the other 82% to win the hand. Again, the player with the worse hand
when they put in their chips hit a subsequent lucky card to win the pot.
This time that same guy in the crowd called out, “Look, it was the 18%!” In

## Page 30

This time that same guy in the crowd called out, “Look, it was the 18%!” In
that aha moment, he changed his definition of what it meant to be wrong. When
we think in advance about the chances of alternative outcomes and make a
decision based on those chances, it doesn’t automatically make us wrong when
things don’t work out. It just means that one event in a set of possible futures
occurred.
Look how quickly you can begin to redefine what it means to be wrong.
Once we start thinking like this, it becomes easier to resist the temptation to
make snap judgments after results or say things like “I knew it” or “I should
have known.” Better decision-making and more self-compassion follow.
The public-at-large is often guilty of making black-and-white judgments
about the “success” or “failure” of probabilistic thinking. When the UK voted to
leave the European Union (“Brexit”) in July 2016, it was an unlikely result.
Betting shops had set odds heavily favoring a vote for Remain. That does not
mean the betting shops had an opinion that Remain would win the day. The goal
of the bookmaker is to make sure the amount of money bet on
either side is
equal, so that the losers essentially pay the winners while the bookmaker just
takes their fee. They aim to have no stake in the outcome and adjust the odds
accordingly. The bookmaker’s odds reflect the market’s view, essentially our
collective best guess of what is fair.
That didn’t stop even sophisticated people from resulting, declaring after the
vote came in Leave that the bookmakers made a mistake. The chief strategist at
one Swiss bank told the
Wall Street Journal
, “I can’t remember any time when
the bookies were so wrong.” One of America’s most famous lawyers and
professors, Alan Dershowitz, made this same error. Asserting in September 2016
that the Clinton-Trump election was too difficult to make any predictions about,
he said, “Think about the vote on Brexit. Virtually all the polls—including exit
polls that asked voters how they voted—got it wrong. The financial markets got
it wrong. The bookies got it wrong.”
Just like my spectator, Dershowitz missed the point. Any prediction that is
not 0% or 100% can’t be wrong solely because the most likely future doesn’t
unfold. When the 24% result happened at the final table of the charity
tournament, that didn’t reflect inaccuracy about the probabilities as determined
before that single outcome. Long shots hit some of the time. Blaming the
oddsmakers or the odds themselves assumes that once something happens, it was
bound to have happened and anyone who didn’t see it coming was wrong.
The same thing happened after Donald Trump won the presidency. There
was a huge outcry about the polls being wrong. Nate Silver, the founder of

## Page 31

FiveThirtyEight.com, drew a lot of that criticism. But he never said Clinton was
a sure thing. Based on his aggregation and weighting of polling data, he had
Trump between 30% and 40% to win (approximately between two-to-one and
three-to-two against) in the week before the election. An event predicted to
happen 30% to 40% of the time will happen a lot.
Being a poker player, I’ve played out more two-to-one shots in my
tournament career than I could possibly count. A lot of those have been
situations where the tournament was on the line for me. If I lose the hand, I’m
out of the tournament. If I win, I earn a huge pot, maybe even winning the entire
tournament. I know viscerally how likely 60–40 and 70–30 favorites are to lose
(and, of course, the opposite). When people complained that Nate Silver did his
job poorly because he had Clinton favored, I thought, “Those people haven’t
gotten all their chips in a pot with a pair against a straight draw and lost.” Or,
more likely, they’ve had those things happen throughout their lives and didn’t
realize that’s what 30% or 40% feels like.
Decisions are bets on the future, and they aren’t “right” or “wrong” based on
whether they turn out well on any particular iteration. An unwanted result
doesn’t make our decision wrong if we thought about the alternatives and
probabilities in advance and allocated our resources accordingly, as my client the
CEO and Pete Carroll both did. It would be absurd for me, after making a big bet
on the best possible starting hand (a pair of aces) and losing, to spend a lot of
time thinking that I was wrong to make the decision to play the hand in the first
place. That would be resulting.
When we think probabilistically, we are less likely to use adverse results
alone as proof that we made a decision error, because we recognize the
possibility that the decision might have been good but luck and/or incomplete
information (and a sample size of one) intervened.
Maybe we made the best decision from a set of unappealing choices, none
of which were likely to turn out well.
Maybe we committed our resources on a long shot because
the payout more
than compensated for the risk, but the long shot didn’t come in this time.
Maybe we made the best choice based on the available information, but
decisive information was hidden and we could not have known about it.
Maybe we chose a path with a very high likelihood of success and got
unlucky.
Maybe there were other choices that might have been better
and
the one we
made wasn’t wrong or right but somewhere in between. The second-best choice

## Page 32

isn’t wrong. By definition, it is more right (or less wrong) than the third-best or
fourth-best choice. It is like the scale at the doctor’s office: there are a lot more
choices other than the extremes of obesity or anorexia. For most of our
decisions, there will be a lot of space between unequivocal “right” and “wrong.”
When we move away from a world where there are only two opposing and
discrete boxes that decisions can be put in—right or wrong—we start living in
the continuum between the extremes. Making better decisions stops being about
wrong or right but about calibrating among all the shades of grey.
Redefining wrong is easiest in situations where we know the mathematical
facts in advance. In the charity-tournament final-table example with the players’
cards faceup, or when I get all my chips in with the best possible starting hand,
the hidden information is removed. We can make a clear calculation. If we have
that unquestionably right and make an allocation of resources (a bet) on the
calculation, we can more naturally get to “I wasn’t wrong just because it didn’t
turn out well and I shouldn’t change my behavior.” When the chances are
known, we are tethered more tightly to a rational interpretation of the influence
of luck. It feels a little more like chess that way.
There is no doubt it is harder to get there when we add in hidden
information on top of the influence of luck. Untethered from seeing what the
coin actually looks like, we are more likely to anchor ourselves to the way things
turned out as the sole signal for whether we were right or wrong. We are more
likely to declare, “I told you so!” or “I should have known!” When we start
doing that, compassion goes out the window. Just ask Pete Carroll.
Redefining wrong allows us to let go of all the anguish that comes from
getting a bad result. But it also means we must redefine “right.” If we aren’t
wrong just because things didn’t work out, then we aren’t right just because
things turned out well. Do we win emotionally to making that mindset trade-off?
Being right feels really good. “I was right,” “I knew it,” “I told you so”—
those are all things that we say, and they all feel very good to us. Should we be
willing to give up the good feeling of “right” to get rid of the anguish of
“wrong”? Yes.
First, the world is a pretty random place. The influence of luck makes it
impossible to predict exactly how things will turn out, and all the hidden
information makes it even worse. If we don’t change our mindset, we’re going to
have to deal with being wrong a lot. It’s built into the equation.
Poker teaches that lesson. A great poker player who has a good-size
advantage over the other players at the table, making significantly better
strategic decisions, will still be losing over 40% of the time at the end of eight

## Page 33

strategic decisions, will still be losing over 40% of the time at the end of eight
hours of play. That’s a whole lot of wrong. And it’s not just confined to poker.
The most successful investors in start-up companies have a majority of bad
results. If you applied to NASA’s astronaut program or the NBC page program,
both of which have drawn thousands of applicants for a handful of positions,
things will go your way a minority of the time, but you didn’t necessarily do
anything wrong. Don’t fall in love or even date anybody if you want only
positive results. The world is structured to give us lots of opportunities to feel
bad about being wrong if we want to measure ourselves by outcomes. Don’t fall
for it!
Second, being wrong hurts us more than being right feels good. We know
from Daniel Kahneman and Amos Tversky’s work on loss aversion, part of
prospect theory (which won Kahneman the Nobel Prize in Economics in 2002),
that losses in general feel about two times as bad as wins feel good. So winning
$100 at blackjack feels as good to us as losing $50 feels bad to us. Because
being right feels like winning and being wrong feels like losing, that means we
need two favorable results for every one unfavorable result just to break even
emotionally. Why not live a smoother existence, without the swings, especially
when the losses affect us more intensely than the wins?
• • •
Are you ready to really wrap your arms around uncertainty, like great decision-
makers do? Are you ready to embrace this redefinition of wrong, and to
recognize you are always guessing and that those guesses drive how you place
your resources? Getting comfortable with this realignment, and all the good
things that follow, starts with recognizing that you’ve been betting all along.

## Page 34

CHAPTER 2
Wanna Bet?
Thirty days in Des Moines
During the 1990s, John Hennigan, an eccentric gambler who had been making a
living by his wits and skills in poker and pool for several years, moved from
Philadelphia to Las Vegas. His reputation and nickname, “Johnny World,”
preceded him, due to his already exceptional skills and willingness to bet on
anything. His talent has stood the test of time: he is a legendarily successful
player in high-stakes games, and in major poker tournaments has earned four
World Series of Poker bracelets, a World Poker Tour championship, and more
than $6.5 million in prize money.
John was a perfect match for Las Vegas. He arrived already in rhythm with
the town: sleeping all day and spending all night in poker games, pool halls,
bars, and restaurants with adventurous, like-minded peers. He quickly found a
group of professional gamblers with similar interests, many from the East Coast.
Although John and Vegas seemed made for each other, he had a love-hate
relationship with the lifestyle. Playing poker for
a living has the allure of giving
you the freedom to make your own schedule but, once it boils down to your per-
hour net advantage, you are tethered to putting in the hours. You’re “free” to
play or not play whenever you want, but you can feel compelled to punch a
clock. Worse, the best games are at night, so you’re working the graveyard shift.
You get out of rhythm with the rest of the world, never see the sun, and your
workplace is a smoke-filled room where you can’t even see outside. John felt
this keenly.
One night, John was in a high-stakes poker game and the talk between hands
somehow included the state capitol of Iowa, Des Moines. John had never been
there or seen much of the Midwest, so he was curious about what life in Des

## Page 35

there or seen much of the Midwest, so he was curious about what life in Des
Moines might be like—a “normal” life that increasingly seemed foreign to him,
waking up in the morning and living in the daylight hours. This led to some
good-natured ribbing as the other players in the game imagined the prospect of a
nocturnal action junkie like John in a place that seemed, to them at least, like the
opposite of Las Vegas: “There’s no gambling action.” “The bars close early.”
“You’d hate it there.” Over the course of the evening, the discussion shifted to
whether Hennigan could even live in such an unfamiliar place.
As is often the case with poker players, a conversation about a hypothetical
turned into an opportunity to propose a wager. What would the stakes have to be
for Hennigan to get up from the table, catch a flight, and relocate to Des
Moines? If he took such a bet, how long would he have to live there?
John and the others landed on a month in Des Moines—a real commitment
but not a permanent exile. When he seemed willing to, literally, walk out of a
poker game and move 1,500 miles to a place he had never been, the other
players added a diabolical
condition to the negotiation: he would have to confine
himself to one street in Des Moines, a street with a single hotel, restaurant, and
bar, where everything closed at 10 p.m. That enforced idleness would be a
challenge for anyone, regardless of the location. But for someone like John, a
young, single, high-stakes gambler, this might actually count as torture. John
said he would take such a challenge if they made one concession: he could
practice and play at a nearby golf course.
After agreeing on the conditions, they still had to negotiate the size of the
bet. The other players needed a number that was large enough to entice John to
accept the wager, but not so large that it would entice John to stay even if he
really hated it in Iowa. As one of the most successful cash-game players in Las
Vegas, a month in Des Moines could cost John, potentially, six figures. On the
other hand, if they offered him too large of an upside to stay in Des Moines, he
would certainly endure the discomfort and boredom.
They settled on $30,000.
John considered two distinct and mutually exclusive alternatives: taking the
bet or not taking the bet. Each came with new risks and new reward potentials.
He could win or lose $30,000 if he took the bet (or win or lose greater dollar
amounts at the poker table if he turned it down). He could also win to the
decision to move to Des Moines long after the bet was over, if he used the golf-
practice time to improve his chances gambling at high-stakes golf. He could
further his reputation of being willing to bet on anything and being capable of
anything, a profitable asset for professional gamblers. He also had to think about

## Page 36

the other, less quantifiable things he might value. How much might he like the
pace of life? How would he value taking a break from the
action? Would he
become more relaxed experiencing the more traditional schedule? Was the break
worth it to take the big pay cut from not being able to play poker for a month?
And then there were the real unknowns. He might just meet the love of his life
on that one street in Iowa. He had to weigh all of this against the opportunity
costs of leaving Vegas—money from lost earning opportunities, nights missing
doing the things he enjoyed, and even perhaps
missing
meeting the love of his
life at the Mirage during that month.
Johnny World moved to Des Moines.
Was a month of detox away from the nightly life of a high-stakes Vegas pro
going to be a blessing or a curse?
It took just two days for him to realize that it was a curse. From his hotel
room in Des Moines, John called one of his friends on the other side of the bet
and tried to negotiate a settlement. Just as parties in commercial lawsuits often
settle before trial, in the gambling world negotiated settlements are common.
What was particularly funny about John’s call was that his opening offer was
that the others
pay him
$15,000 to spare them the cost and indignity of losing the
whole amount. He argued that since he was already in Des Moines, he was
clearly capable of waiting out the month to get the full amount.
The other bettors, literally, were not buying it. After all, John made this
offer
after only two days.
That was a pretty strong signal that not only would
they likely win the bet, but they might earn a return (in fun) by needling John
while he served out his sentence.
Within a few days, John agreed to
pay
$15,000 to get out of the bet and
return to Vegas. John proved, in spectacular fashion, that the grass is always
greener.
We’ve all been to Des Moines
The punch line of the John Hennigan–Des Moines story—“after two days, he
begged
to get out of it”—made it part of gambling folklore. That punch line,
however, obscures how
usual
the underlying analysis about whether to move
was. The only real difference between Johnny World’s decision to move to Des
Moines and anyone else’s decision to relocate or take a job was that he and the

## Page 37

poker players made explicit that the decision was a bet on what would most
improve their quality of life (financial, emotional, and otherwise).
John considered two distinct and mutually exclusive alternative futures:
taking the bet and living for a month in Des Moines, or not taking the bet and
staying in Las Vegas. Any of us thinking about relocating for a new job has this
same choice between moving, with the potential to earn the money being
offered, or staying where we are and maintaining the status quo. How does the
new job pay compared to what we have now? There are plenty of things we
value in addition to money; we might be willing to make less money to move to
a place we imagine we would like a lot better. Will the new job have better
opportunities for advancement and future gains, independent of short-term gains
in compensation? What are the differences in pay, benefits, security, work
environment, and the kind of work we’d be doing? What are we giving up by
leaving our city, colleagues, and friends for a new place?
We have to inventory the potential upside and downside of taking the bet
just like Hennigan did. That his $30,000 wasn’t a sure thing doesn’t make his
decision distinct from other job or
relocation decisions. People take jobs all the
time where a large portion of the compensation is contingent. In many
businesses, compensation includes bonuses, stock options, or performance-based
pay. Even though most people don’t have to consider losing $30,000 when they
take a job, every decision has risks, regardless of whether we acknowledge them.
Even a set salary is still not “guaranteed.” We could get laid off or hate the job
and quit (as John Hennigan did), or the company could go out of business. When
we take a job, especially one promising big financial rewards, the commitment
to work can cost us time with our family and affect those relationships, a costly
if not losing compromise.
In addition, whenever we choose an alternative (whether it is taking a new
job or moving to Des Moines for a month), we are automatically rejecting every
other possible choice. All those rejected alternatives are paths to possible futures
where things could be better or worse than the path we chose. There is potential
opportunity cost in any choice we forgo.
Likewise, the players on the other side of that bet, risking $30,000 to see if
John would live a month in Des Moines, thought about similar factors that
employers consider in making job offers or spending money to create enticing
workplace environments. The poker players had to strike a fine balance in
offering that bet to Hennigan: the proposition had to be good enough to entice
him to take the bet but not so good that it would be guaranteed to cost them the
$30,000.

## Page 38

$30,000.
Although employers aren’t trying to entice employees to quit, their goal is
similar in arriving at a compensation package to get the prospect to accept the
offer and stay in the job. They must balance offering attractive pay and benefits
with going too far and impairing their ability to make a profit. Employers also
want employees to be loyal, and work long, productive hours, and
maintain
morale. An employer might or might not offer on-premises child care. That
could encourage someone to work more hours . . . or scare off a prospective
employee because it implies they may be expected to sacrifice aspects of their
non-work lives. Offering paid vacation leave makes a job more attractive but,
unlike offering free dining and exercise facilities, encourages them to spend time
away from work.
Hiring an employee, like offering a bet, is not a riskless choice. Betting on
hiring the wrong person can have a huge cost (as the CEO who fired his
president can attest). Recruitment costs can be substantial, and every job offer
has an associated opportunity cost. This is the only person you can offer
this
opportunity. You might have dodged the cost of hiring Bernie Madoff, but you
might have lost the benefit of hiring Bill Gates.
The John Hennigan story seems so unusual because it started with a
discussion about what Des Moines was like and ended with one of the people in
the discussion moving there the next day. That happened, though, because when
you are betting, you have to back up your belief by putting a price on it. You
have to put your money where your mouth is. To me, the ironic thing about a
story that seems so crazy is how the underlying analysis was actually very
logical: a difference of opinion about alternatives, consequences, and
probabilities.
By treating decisions as bets, poker players explicitly recognize that they are
deciding on alternative futures, each with benefits and risks. They also recognize
there are no simple answers. Some things are unknown or unknowable. The
promise of this book is that if we follow the example of poker players by making
explicit that our decisions are bets, we can make better decisions and anticipate
(and take protective measures) when irrationality is likely to keep us from acting
in our best interest.
All decisions are bets

## Page 39

Our traditional view of betting is very narrow: casinos, sporting events, lottery
tickets, wagering against someone else on the chance of a favorable outcome of
some event. The definition of “bet” is much broader.
Merriam-Webster’s Online
Dictionary
defines “bet” as “a
choice
made by thinking about
what will probably
happen
,” “to
risk
losing (something) when you try to do or achieve something”
and “to make
decisions
that are based on the
belief
that something will happen or
is true.” I have emphasized the broader, often overlooked, aspects of betting:
choice, probability, risk, decision, belief. We can also see from this definition
that betting doesn’t have to take place in a casino or against somebody else.
No matter how far we get from the familiarity of betting at a poker table or
in a casino, our decisions are always bets. We routinely decide among
alternatives, put resources at risk, assess the likelihood of different outcomes,
and consider what it is that we value. Every decision commits us to some course
of action that, by definition, eliminates acting on other alternatives. Not placing
a bet on something is, itself, a bet. Choosing to go to the movies means that we
are choosing to not do all the other things with our time that we might do during
that two hours. If we accept a job offer, we are also choosing to foreclose all
other alternatives: we aren’t sticking with our current job, or negotiating to get a
better deal in our current job, or getting or taking other offers, or changing
careers, or taking some time away from work. There is always opportunity cost
in choosing one path over others.
The betting elements of decisions—choice, probability, risk, etc.—are more
obvious in some situations than others. Investments are clearly bets. A decision
about a stock (buy, don’t buy, sell, hold,
not to mention esoteric investment
options) involves a choice about the best use of financial resources. Incomplete
information and factors outside of our control make all our investment choices
uncertain. We evaluate what we can, figure out what we think will maximize our
investment money, and execute. Deciding not to invest or not to sell a stock,
likewise, is a bet. These are the same decisions I make during a hand of poker:
fold, check, call, bet, or raise.
We don’t think of our parenting choices as bets but they are. We want our
children to be happy, productive adults when we send them out into the world.
Whenever we make a parenting choice (about discipline, nutrition, school,
parenting philosophy, where to live, etc.), we are betting that our choice will
achieve the future we want for our children more than any other choice we might
make given the constraints of the limited resources we have to allocate—our
time, our money, our attention.
Job and relocation decisions are bets. Sales negotiations and contracts are

## Page 40

Job and relocation decisions are bets. Sales negotiations and contracts are
bets. Buying a house is a bet. Ordering the chicken instead of the steak is a bet.
Everything is a bet.
Most bets are bets against ourselves
One of the reasons we don’t naturally think of decisions as bets is because we
get hung up on the zero-sum nature of the betting that occurs in the gambling
world; betting
against somebody

else
(or the casino), where the gains and losses
are symmetrical. One person wins, the other loses, and the net between the two
adds to zero. Betting includes, but is not limited to, those situations.
In most of our decisions, we are not betting against another
person. Rather,
we are betting against
all the future versions of ourselves that we are not
choosing
. We are constantly deciding among alternative futures: one where we
go to the movies, one where we go bowling, one where we stay home. Or futures
where we take a job in Des Moines, stay at our current job, or take some time
away from work. Whenever we make a choice, we are betting on a potential
future. We are betting that the future version of us that results from the decisions
we make will be better off. At stake in a decision is that the return to us
(measured in money, time, happiness, health, or whatever we value in that
circumstance) will be greater than what we are giving up by betting against the
other alternative future versions of us.
Have you ever had a moment of regret after a decision where you felt, “I
knew I should have made the other choice!”? That’s an alternative version of
you saying, “See, I told you so!”
When Pete Carroll called for a pass on second down, he didn’t need an inner
voice second-guessing him. He had the collective cry of the Seahawks fans
yelling, “When you called for Wilson to pass, you bet on the wrong future!”
How can we be sure that we are choosing the alternative that is best for us?
What if another alternative would bring us more happiness, satisfaction, or
money? The answer, of course, is we can’t be sure. Things outside our control
(luck) can influence the result. The futures we imagine are merely
possible.
They
haven’t happened yet. We can only make our best guess, given what we know
and don’t know, at what the future will look like. If we’ve never lived in Des
Moines, how can we possibly be sure how we will like it? When we decide, we
are betting whatever we value (happiness, success, satisfaction, money, time,

## Page 41

reputation, etc.) on one of a set of possible and uncertain futures. That is where
the risk is.
Poker players live in a world where that risk is made explicit. They can get
comfortable with uncertainty because they put it up front in their decisions.
Ignoring the risk and uncertainty in every decision might make us feel better in
the short run, but the cost to the quality of our decision-making can be immense.
If we can find ways to become more comfortable with uncertainty, we can see
the world more accurately and be better for it.
Our bets are only as good as our beliefs
In an episode of the classic sitcom
WKRP in Cincinnati
, called “Turkeys Away,”
the radio station’s middle-aged manager, Mr. Carlson, tries to prove he can stage
a successful promotion for the rock-and-roll station. He sends his veteran news
reporter, Les Nessman, to a local shopping center and tells him to report, live, on
a turkey giveaway he is about to unleash.
The station’s DJ, Johnny Fever, cuts from his show to a live “man on the
scene” report from Nessman. Nessman fills time, describing a helicopter
overhead. Then something comes out of the helicopter. “No parachutes yet . . .
Those can’t be skydivers. I can’t tell what they are but—oh, my God! They’re
turkeys! . . . One just went through the windshield of a parked car! This is
terrible! . . . Oh, the humanity! . . . The turkeys are hitting the ground like sacks
of wet cement!” Nessman has to flee amid an ensuing riot. He returns to the
studio and describes how Mr. Carlson tried to land the helicopter and free the
remaining turkeys, but they waged a counterattack.
Carlson enters, ragged and covered with feathers. “As God is my witness, I
thought turkeys could fly.”
We bet based on what we believe about the world. Pete Carroll’s Super
Bowl decision to pass on the Patriots’ one-yard line was driven by his beliefs—
his beliefs about quarterback Russell Wilson’s likelihood of completing the pass,
of having the pass intercepted, of getting sacked (or scrambling for a
touchdown). He had data on and experience about all these things, and then had
to apply that to this unique situation, considering his beliefs about the Patriots’
defense and how their coach, Bill Belichick, would set up the defense for a likely

## Page 42

running play on the goal line. He then made a choice about the best play to call
based on these beliefs. He bet on a pass play.
The CEO who suffered all that anguish over firing the president did what he
did based on his beliefs. He made his decision based on his beliefs about how the
company was doing compared with competitors, what he thought the president
did that contributed to or detracted from that, the likelihood he could get the
president’s performance to improve, the costs and benefits to splitting the job
between two people, and the likelihood he could find a replacement. He bet on
letting the president go.
John Hennigan had beliefs about how he would adapt to Des Moines. Our
beliefs drive the bets we make: which brands of cars better retain their value,
whether critics knew what they were talking about when they panned a movie
we are thinking about seeing, how our employees will behave if we let them
work from home.
This is ultimately very good news: part of the
skill
in life comes from
learning to be a better belief calibrator, using experience and information to
more objectively update our beliefs to more accurately represent the world. The
more accurate our beliefs, the better the foundation of the bets we make. There is
also
skill in identifying when our thinking patterns might lead us astray, no
matter what our beliefs are, and in developing strategies to work with (and
sometimes around) those thinking patterns. There are effective strategies to be
more open-minded, more objective, more accurate in our beliefs, more rational
in our decisions and actions, and more compassionate toward ourselves in the
process.
We have to start, however, with some bad news. As Mr. Carlson learned in
WKRP in Cincinnati
, our beliefs can be way, way off.
Hearing is believing
When I speak at professional conferences, I will occasionally bring up the
subject of belief formation by asking the audience a question: “Who here knows
how you can predict if a man will go bald?” People will raise their hands, I’ll
call on someone, and they’ll say, “You look at the maternal grandfather.”
Everyone nods in agreement. I’ll follow up by asking, “Does anyone know how
you calculate a dog’s age in human years?” I can practically see audience
members mouthing, “Multiply by seven.”

## Page 43

members mouthing, “Multiply by seven.”
Both of these widely held beliefs aren’t actually accurate. If you search
online for “common misconceptions,” the baldness myth is at the top of most
lists. As
Medical Daily
explained in 2015, “a key gene for baldness is on the X
chromosome, which you get from your mother” but “it is not the only genetic
factor in play since men with bald fathers have an increased chance of going
bald when compared to men whose fathers have a full set of hair. . . . [S]cientists
say baldness anywhere in your family may be a sign of your own impending
fate.”
As for the dog-to-human age ratio, it’s just a made-up number that’s been
circulating with no basis, yet with increasing weight through repetition, since the
thirteenth century. Where did we get these beliefs? And why do they persist,
despite contrary science and logic?
We form beliefs in a haphazard way, believing all sorts of things based just
on what we hear out in the world but haven’t researched for ourselves.
This is how
we think
we form abstract beliefs:
1.
We hear something;
2.
We think about it and vet it, determining whether it is true or false; only
after that
3.
We form our belief.
It turns out, though, that we
actually
form abstract beliefs this way:
1.
We hear something;
2.
We believe it to be true;
3.
Only sometimes, later, if we have the time or the inclination, we think
about it and vet it, determining whether it is, in fact, true or false.
Harvard psychology professor Daniel Gilbert, best known for his book
Stumbling on Happiness
and his starring role in Prudential Financial
commercials, is also responsible for some pioneering work on belief formation.
In a 1991 paper in which he summarized centuries of philosophical and scientific
study on the subject, he concluded, “Findings from a multitude of research
literatures converge on a single point: People are credulous creatures who find it
very easy to believe and very difficult to doubt. In fact, believing is so easy, and

## Page 44

perhaps so inevitable, that it may be more like involuntary comprehension than it
is like rational assessment.”
Two years later, Gilbert and colleagues demonstrated through a series of
experiments that our default is to believe that what we hear and read is true.
Even when that information is clearly presented as being false, we are still likely
to process it as true. In these experiments, subjects read a series of statements
about a criminal defendant or a college student. These statements were color
coded to make it clear whether they were true or false. Subjects under time
pressure or who had their cognitive load increased by a minor distraction made
more errors in recalling whether the statements were true or false. But the errors
weren’t random. The subjects were not equally likely to ignore some statements
labeled “true” as they were to rely on some statements labeled “false.” Rather,
their errors went in one direction: under any sort of pressure, they presumed all
the statements were true, regardless of their labeling. This suggests our default
setting is to believe what we hear is true.
This is why we believe that baldness is passed down from the maternal
grandfather. If you, like me until I looked it up for this book, held that belief, had
you ever researched it for yourself? When I ask my audiences this question, they
generally say it is just something they heard but they have no idea where or from
whom. Yet they are very confident that this is true. That should be proof enough
that the way we form beliefs is pretty goofy.
As with many of our irrationalities, how we form beliefs was shaped by the
evolutionary push toward efficiency rather than accuracy. Abstract belief
formation (that is, beliefs outside our direct experience, conveyed through
language) is likely among the few things that are uniquely human, making it
relatively new
in the scope of evolutionary time. Before language, our ancestors
could form new beliefs only through what they directly experienced of the
physical world around them. For perceptual beliefs from direct sensory
experience, it’s reasonable to presume our senses aren’t lying. Seeing is, after
all, believing. If you see a tree right in front of you, it would generally be a waste
of cognitive energy to question whether the tree exists. In fact, questioning what
you see or hear can get you eaten. For survival-essential skills, type I errors
(false positives) were less costly than type II errors (false negatives). In other
words, better to be safe than sorry, especially when considering whether to
believe that the rustling in the grass is a lion. We didn’t develop a high degree of
skepticism when our beliefs were about things we directly experienced,
especially when our lives were at stake.

## Page 45

As complex language evolved, we gained the ability to form beliefs about
things we had not actually experienced for ourselves. And, as Gilbert pointed
out, “nature does not start from scratch; rather, she is an inveterate jury rigger
who rarely invents a new mechanism to do splendidly what an old mechanism
can be modified to do tolerably well.” In this case, the system we already had
was (1) experience it, (2) believe it to be true, and (3) maybe, and rarely,
question it later. We may have more reasons to question this flood of
secondhand information, but our older system is still in charge. (This is a very
simple summary of a great deal of research and documentation. For some good
overviews, I highly recommend Dan Gilbert’s
Stumbling on Happiness
, Gary
Marcus’s
Kluge,
and Dan Kahneman’s
Thinking, Fast and Slow,
listed in the
Selected Bibliography and Recommendations for Further Reading.)
A quick Google search will show that many of our commonly held beliefs
are untrue. We just don’t get around to doing Google
searches on these things.
(Spoiler alerts: (1) Abner Doubleday had nothing to do with inventing the game
of baseball. (2) We use all parts of our brain. The 10% figure was made up to
sell self-improvement books; neural imaging and brain-injury studies disprove
the fabrication. (3) Immigrants didn’t have their names Americanized,
involuntarily or otherwise, at Ellis Island.)
Maybe it’s no big deal that some of these inconsequential common beliefs
are clearly false. Presumably, people aren’t using a bogus dog-age calculator to
make medical decisions for their pets, and veterinarians know better. But this is
our general belief-formation process, and it applies in areas that can have
significant consequences.
In poker, this belief-formation process can cost players a lot of money. One
of the first things players learn in Texas Hold’em is a list of two-card starting
hands to play or fold, based on your table position and actions from players
before you.
*
When Texas Hold’em first developed in the sixties, some expert
players innovated deceptive plays with middle cards consecutive in rank and of
the same suit (like the six and seven of diamonds). In poker shorthand, such
cards are called “suited connectors.”
Suited connectors have the attraction of making a powerful,
camouflaged
straight or a flush. Expert players might choose to play these types of hands in a
very limited set of circumstances, namely where they feel they could fold the
hand at a small loss; successfully bluff if it doesn’t develop; or extract maximum
value in later betting rounds by trapping a player with conventionally stronger
starting cards when the hand does develop favorably.
Unfortunately, the mantra of “win big or lose small with suited connectors”

## Page 46

Unfortunately, the mantra of “win big or lose small with suited connectors”
filtered down over the years without the subtlety of the expertise needed to play
them well or the narrow circumstances needed to make those hands profitable.
When I taught poker seminars, most of my students strongly believed suited
connectors were profitable starting cards under pretty much any circumstances.
When I asked why, I would hear “everyone knows that” or “I see players
cleaning up with suited connectors all the time on TV.” But no one I asked had
kept a P&L on their experience with suited connectors. “Do that,” I’d say, “and
report back what you find.” Lo and behold, players who came back to me
discovered they were net losers with suited connectors.
The same belief-formation process led hundreds of millions of people to bet
the quality and length of their lives on their belief about the merits of a low-fat
diet. Led by advice drawn, in part, from research secretly funded by the sugar
industry, Americans in one generation cut a quarter of caloric intake from fat,
replacing it with carbohydrates. The U.S. government revised the food pyramid
to include six to eleven servings of carbohydrates and advised that the public
consume fats sparingly. It encouraged the food industry (which enthusiastically
followed) to substitute starch and sugar to produce “reduced-fat” foods. David
Ludwig, a Harvard Medical School professor and doctor at Boston Children’s
Hospital, summarized the cost of substituting
carbs for fats in the
Journal of the
American Medical Association
: “Contrary to prediction, total calorie intake
increased substantially, the prevalence of obesity tripled, the incidence of type 2
diabetes increased many-fold, and the decades-long decrease in cardiovascular
disease plateaued and may reverse, despite greater use of preventive drugs and
surgical procedures.”
Low-fat diets became the suited connectors of our eating habits.
Even though our default is “true,” if we were good at updating our beliefs
based on new information, our haphazard belief-formation process might cause
relatively few problems. Sadly, this is not the way it works. We form beliefs
without vetting most of them, and maintain them even after receiving clear,
corrective information. In 1994, Hollyn Johnson and Colleen Seifert reported in
the
Journal of Experimental Psychology
the results of a series of experiments in
which subjects read messages about a warehouse fire. For subjects reading
messages mentioning that the fire started near a closet containing paint cans and
pressurized gas cylinders, that information (predictably) encouraged them to
infer a connection. When, five messages later, subjects received a correction that
the closet was empty, they still answered questions about the fire by blaming
burning paint for toxic fumes and citing negligence for keeping flammable

## Page 47

objects nearby. (This shouldn’t be a surprise to anyone recognizing the futility of
issuing a retraction after reporting a news story with a factual error.)
Truthseeking, the desire to know the truth regardless of whether the truth
aligns with the beliefs we currently hold, is not naturally supported by the way
we process information. We might think of ourselves as open-minded and
capable of updating our beliefs based on new information, but the research
conclusively
shows otherwise. Instead of altering our beliefs to fit new
information, we do the opposite, altering our interpretation of that information to
fit our beliefs.
“
They saw a game”
As a college football season is about to close, all eyes are fixed on a fierce
rivalry. The favorite, playing at home, has a twenty-two-game winning streak
and is on the verge of completing a second consecutive undefeated season. The
most emotional reception will be for Dick Kazmaier, the offensive star. One of
the school’s all-time athletic heroes, he made the cover of
Time
and is in
contention for All-American and other postseason honors. The visitors, however,
have no intention of going down to defeat quietly. Although their record this
season has been only average, they have a reputation for playing hard. Pulling
off a stunning upset would be an unexpected treat.
Welcome to Princeton’s Palmer Stadium, November 23, 1951. The
Dartmouth-Princeton football game became famous: part of a historic rivalry,
the end of an epoch in Ivy League sports, and the subject of a groundbreaking
scientific experiment.
First, the game. Princeton won, 13–0. The outcome was not in much doubt,
but it was nevertheless a dirty, violent, penalty-laden game. Dartmouth received
seventy yards in penalties, Princeton twenty-five. A fallen Princeton player got
kicked in the ribs. One Dartmouth player broke a leg, and a second also suffered
a leg injury. Kazmaier exited the game in the second quarter with a concussion
and a broken nose. (He returned for the final play, earning a victory lap on his
teammates’ shoulders. A few months
later he became the last player from the Ivy
League to win the Heisman Trophy.)
Surprised by the ferocity of the editorials in both schools’ newspapers after
the game, a pair of psychology professors saw the occasion as an opportunity to
study how beliefs can radically alter the way we process a common experience.

## Page 48

study how beliefs can radically alter the way we process a common experience.
Albert Hastorf of Dartmouth and Hadley Cantril of Princeton collected the
newspaper stories, obtained a copy of the game film, showed it to groups of
students from their schools, and had them complete questionnaires counting and
characterizing the infractions on both sides. Their 1954 paper, “They Saw a
Game,” could have been called “They Saw Two Games” because students from
each school, based on their questionnaires and accounts, seemed to be watching
different games.
Hastorf and Cantril collected anecdotal evidence of this in the lively
accounts and editorials of the Dartmouth-Princeton game in local newspapers.
The
Daily Princetonian
said, “Both teams were guilty but the blame must be laid
primarily on Dartmouth’s doorstep.” The
Princeton Alumni Weekly
called out
Dartmouth for a late hit on the play that ended Kazmaier’s college career and for
kicking a prone Princeton player in the ribs. Meanwhile, an editorial in the
Dartmouth
placed heavy blame on Princeton coach Charley Caldwell. After the
injury to the “Princeton idol,” “Caldwell instilled the old see-what-they-did-go-
get-them attitude into his players. His talk got results,” the editorial asserted,
referring to the pair of Dartmouth players suffering leg injuries in the third
quarter. In the next issue of the
Dartmouth
, the paper listed star players from the
opposing team that Princeton had stopped by a similar “concentrated effort.”
When the researchers showed groups of students the film of the game and
asked them to fill out the questionnaires, the same
difference of opinion about
what they had seen appeared. Princeton students saw Dartmouth commit twice
as many flagrant penalties and three times the mild penalties as Princeton.
Dartmouth students saw each team commit an equal number of infractions.
Hastorf and Cantril concluded, “We do not simply ‘react to’ a
happening. . . . We behave according to what we bring to the occasion.” Our
beliefs affect how we process all new things, “whether the ‘thing’ is a football
game, a presidential candidate, Communism, or spinach.”
A study in the 2012
Stanford Law Review
called “They Saw a Protest” (the
title is a homage to the original Hastorf and Cantril experiment) by Yale
professor of law and psychology Dan Kahan, a leading researcher and analyst of
biased reasoning, and four colleagues reinforces this notion that our beliefs drive
the way we process information.
In the study, two groups of subjects watched a video of police action halting
a political demonstration. One group was told the protest occurred outside an
abortion clinic, aimed at protesting legalized abortion. Another group was told it
occurred at a college career-placement facility, where the military was
conducting interviews and protestors were demonstrating against the then-

## Page 49

conducting interviews and protestors were demonstrating against the then-
existing ban on openly gay and lesbian soldiers. It was the same video, carefully
edited to blur or avoid giving away the subject of the actual protest. Researchers,
after gathering information about the worldviews of the subjects, asked them
about facts and conclusions from what they saw.
The results mirrored those found by Hastorf and Cantril nearly sixty years
before: “Our subjects all viewed the same video. But what they
saw
—earnest
voicing of dissent intended only to persuade, or physical intimidation calculated
to interfere with the
freedom of others—depended on the congruence of the
protestors’ positions with the subjects’ own cultural values.” Whether it is a
football game, a protest, or just about anything else, our pre-existing beliefs
influence the way we experience the world. That those beliefs aren’t formed in a
particularly orderly way leads to all sorts of mischief in our decision-making.
The stubbornness of beliefs
Flaws in forming and updating beliefs have the potential to snowball. Once a
belief is lodged, it becomes difficult to dislodge. It takes on a life of its own,
leading us to notice and seek out evidence confirming our belief, rarely
challenge the validity of confirming evidence, and ignore or work hard to
actively discredit information contradicting the belief. This irrational, circular
information-processing pattern is called
motivated reasoning
. The way we
process new information is driven by the beliefs we hold, strengthening them.
Those strengthened beliefs then drive how we process further information, and
so on.
During a break in a poker tournament, a player approached me for my
opinion about how he played one of those suited-connector hands. I didn’t
witness the hand, and he gave me a very abbreviated description of how he
stealthily played the six and seven of diamonds to make a flush on the second-to-
last card but “had the worst luck” when the other player made a full house on the
very last card.
We had only a minute or two left in the break, so I asked what I thought to
be the most relevant question: “Why were you playing six-seven of diamonds in
the first place?” (Even a brief
explanation, I expected, would fill in details on
many of the areas that determine how to play a hand like that and whether it was

## Page 50

a profitable choice, such as table position, pot size, chip stack sizes, his
opponent’s style of play, how the table perceived his style, etc.)
His exasperated response was, “That’s not the point of the story!” Motivated
reasoning tells us it’s not really the point of anyone’s story.
It doesn’t take much for any of us to believe something. And once we
believe it, protecting that belief guides how we treat further information relevant
to the belief. This is perhaps no more evident than in the rise in prominence of
“fake news” and disinformation. The concept of “fake news,” an intentionally
false story planted for financial or political gain, is hundreds of years old. It has
included such legendary practitioners as Orson Welles, Joseph Pulitzer, and
William Randolph Hearst. Disinformation is different than fake news in that the
story has some true elements, embellished to spin a particular narrative. Fake
news works because people who already hold beliefs consistent with the story
generally won’t question the evidence. Disinformation is even more powerful
because the confirmable facts in the story make it feel like the information has
been vetted, adding to the power of the narrative being pushed.
Fake news isn’t meant to change minds. As we know, beliefs are hard to
change. The potency of fake news is that it entrenches beliefs its intended
audience already has, and then amplifies them. The Internet is a playground for
motivated reasoning. It provides the promise of access to a greater diversity of
information sources and opinions than we’ve ever had available, yet we gravitate
toward sources that confirm our beliefs, that agree with us. Every flavor is out
there, but we tend to stick with our favorite.
Making matters worse, many social media sites tailor our
Internet
experience to show us more of what we already like. Author Eli Pariser
developed the term “filter bubble” in his 2011 book of the same name to
describe the process of how companies like Google and Facebook use
algorithms to keep pushing us in the directions we’re already headed. By
collecting our search, browsing, and similar data from our friends and
correspondents, they give users headlines and links that cater to what they’ve
divined as our preferences. The Internet, which gives us access to a diversity of
viewpoints with unimaginable ease, in fact speeds our retreat into a confirmatory
bubble. No matter our political orientation, none of us is immune.
The most popular websites have been doing our motivated reasoning for
us.
*
Even when directly confronted with facts that disconfirm our beliefs, we
don’t let facts get in the way. As Daniel Kahneman pointed out, we just want to

## Page 51

think well of ourselves and feel that the narrative of our life story is a positive
one. Being
wrong
doesn’t fit into that narrative. If we think of beliefs as only
100% right
or
100% wrong
, when confronting new information that might
contradict our belief, we have only two options: (a) make the massive shift in
our opinion of ourselves from 100% right to 100% wrong, or (b) ignore or
discredit the new information. It feels bad to be wrong, so we choose (b).
Information that disagrees with us is an assault on our self-narrative. We’ll work
hard to swat that threat away. On the flip side, when additional information
agrees with us, we effortlessly embrace it.
How we form beliefs, and our inflexibility about changing our beliefs, has
serious consequences because we bet on those
beliefs. Every bet we make in our
lives depends on our beliefs: who we believe will make the best president, if we
think we will like Des Moines, if we believe a low-fat diet will make us
healthier, or even if we believe turkeys can fly.
Being smart makes it worse
The popular wisdom is that the smarter you are, the less susceptible you are to
fake news or disinformation. After all, smart people are more likely to analyze
and effectively evaluate where information is coming from, right? Part of being
“smart” is being good at processing information, parsing the quality of an
argument and the credibility of the source. So, intuitively, it feels like smart
people should have the ability to spot motivated reasoning coming and should
have more intellectual resources to fight it.
Surprisingly, being smart can actually make bias worse. Let me give you a
different intuitive frame: the smarter you are, the better you are at constructing a
narrative that supports your beliefs, rationalizing and framing the data to fit your
argument or point of view. After all, people in the “spin room” in a political
setting are generally pretty smart for a reason.
In 2012, psychologists Richard West, Russell Meserve, and Keith Stanovich
tested the blind-spot bias—an irrationality where people are better at recognizing
biased reasoning in others but are blind to bias in themselves. Overall, their work
supported, across a variety of cognitive biases, that, yes, we all have a blind spot
about recognizing our biases. The surprise is that blind-spot bias is greater the
smarter you are. The researchers tested subjects for
seven cognitive biases and

## Page 52

found that cognitive ability did not attenuate the blind spot. “Furthermore,
people who were aware of their own biases were not better able to overcome
them.” In fact, in six of the seven biases tested, “more cognitively sophisticated
participants showed
larger
bias blind spots.” (Emphasis added.) They have since
replicated this result.
Dan Kahan’s work on motivated reasoning also indicates that smart people
are not better equipped to combat bias—and may even be more susceptible. He
and several colleagues looked at whether conclusions from objective data were
driven by subjective pre-existing beliefs on a topic. When subjects were asked to
analyze complex data on an experimental skin treatment (a “neutral” topic), their
ability to interpret the data and reach a conclusion depended, as expected, on
their numeracy (mathematical aptitude) rather than their opinions on skin cream
(since they really had no opinions on the topic). More numerate subjects did a
better job at figuring out whether the data showed that the skin treatment
increased or decreased the incidence of rashes. (The data were made up, and for
half the subjects, the results were reversed, so the correct or incorrect answer
depended on using the data, not the actual effectiveness of a particular skin
treatment.)
When the researchers kept the data the same but substituted “concealed-
weapons bans” for “skin treatment” and “crime” for “rashes,” now the subjects’
opinions on those topics drove how subjects analyzed the exact same data.
Subjects who identified as “Democrat” or “liberal” interpreted the data in a way
supporting their political belief (gun control reduces crime). The “Republican”
or “conservative” subjects interpreted the same data to support their opposing
belief (gun control increases crime).
That generally fits what we understand about motivated reasoning. The
surprise, though, was Kahan’s finding about subjects
with differing math skills
and the same political beliefs. He discovered that the
more

numerate
people
(whether pro-or anti-gun) made
more mistakes
interpreting the data on the
emotionally charged topic than the less numerate subjects sharing those same
beliefs. “This pattern of polarization . . . does not abate among high-Numeracy
subjects. Indeed, it
increases
.” (Emphasis in original.)
It turns out the better you are with numbers, the better you are at spinning
those numbers to conform to and support your beliefs.
Unfortunately, this is just the way evolution built us. We are wired to protect
our beliefs even when our goal is to truthseek. This is one of those instances
where being smart and aware of our capacity for irrationality alone doesn’t help
us refrain from biased reasoning. As with visual illusions, we can’t make our

## Page 53

us refrain from biased reasoning. As with visual illusions, we can’t make our
minds work differently than they do no matter how smart we are. Just as we
can’t unsee an illusion, intellect or willpower alone can’t make us resist
motivated reasoning.
So far, this chapter has mainly been bad news. We bet on our beliefs. We
don’t vet those beliefs well before we form them. We stubbornly refuse to
update our beliefs. Now I’ve piled on by telling you that being smart doesn’t
help—and can make it worse.
The good news starts here.
Wanna bet?
Imagine taking part in a conversation with a friend about the movie
Citizen
Kane
. Best film of all time, introduced a bunch of new techniques by which
directors could contribute to
storytelling. “Obviously, it won the best-picture
Oscar,” you gush, as part of a list of superlatives the film unquestionably
deserves.
Then your friend says, “Wanna bet?”
Suddenly, you’re not so sure. That challenge puts you on your heels, causing
you to back off your declaration and question the belief that you just declared
with such assurance. When someone challenges us to bet on a belief, signaling
their confidence that our belief is inaccurate in some way, ideally it triggers us to
vet the belief, taking an inventory of the evidence that informed us.
How do I know this?
Where did I get this information?
Who did I get it from?
What is the quality of my sources?
How much do I trust them?
How up to date is my information?
How much information do I have that is relevant to the belief?
What other things like this have I been confident about that turned out not
to be true?
What are the other plausible alternatives?
What do I know about the person challenging my belief?
What is their view of how credible my opinion is?

## Page 54

What do they know that I don’t know?
What is their level of expertise?
What am I missing?
Remember the order in which we form abstract beliefs:
1.
We hear something;
2.
We believe it;
3.
Only sometimes, later, if we have the time or the inclination, we think
about it and vet it, determining whether or not it is true.
“Wanna bet?” triggers us to engage in that third step that we only sometimes
get to. Being asked if we are willing to bet money on it makes it much more
likely that we will examine our information in a less biased way, be more honest
with ourselves about how sure we are of our beliefs, and be more open to
updating and calibrating our beliefs. The more objective we are, the more
accurate our beliefs become. And the person who wins bets over the long run is
the one with the more accurate beliefs.
Of course, in most instances, the person offering to bet isn’t actually looking
to put any money on it. They are just making a point—a valid point that perhaps
we overstated our conclusion or made our statement without including relevant
caveats. Most people aren’t like poker players, around whom there is always the
potential that someone might propose a bet and they will mean it.
Next thing you know, someone moves to Des Moines and there’s $30,000 at
stake.
It’s a shame the social contract for poker players is so different than for the
rest of us in this regard because a lot of good can result from someone saying,
“Wanna bet?” Offering a wager brings the risk out in the open, making explicit
what is already implicit (and frequently overlooked). The more we recognize
that we are betting on our beliefs (with our happiness, attention, health, money,
time, or some other limited resource), the more we are likely to temper our
statements, getting closer to the truth as we acknowledge the risk inherent in
what we believe.
Expecting everyone starting to throw the gauntlet down,
challenging each
other to bet on any opinion, is impractical if you aren’t hanging out in a poker
room. (Even in poker rooms, this generally happens only among players who

## Page 55

know each other well.) I imagine that if you went around challenging everyone
with “Wanna bet?” it would be difficult to make friends and you’d lose the ones
you have. But that doesn’t mean we can’t change the framework for ourselves in
the way we think about our decisions. We can train ourselves to view the world
through the lens of “Wanna bet?”
Once we start doing that, we are more likely to recognize that there is
always a degree of uncertainty, that we are generally less sure than we thought
we were, that practically nothing is black and white, 0% or 100%. And that’s a
pretty good philosophy for living.
Redefining confidence
Not much is ever certain. Samuel Arbesman’s
The Half-Life of Facts
is a great
read about how practically every fact we’ve ever known has been subject to
revision or reversal. We are in a perpetual state of learning, and that can make
any prior fact obsolete. One of many examples he provides is about the
extinction of the coelacanth, a fish from the Late Cretaceous period. A mass-
extinction event (such as a large meteor striking the Earth, a series of volcanic
eruptions, or a permanent climate shift) ended the Cretaceous period. That was
the end of dinosaurs, coelacanths, and a lot of other species. In the late 1930s
and independently in the mid-1950s, however, coelacanths were found
alive and
well. A species becoming “unextinct” is pretty common. Arbesman cites the
work of a pair of biologists at the University of Queensland who made a list of
all 187 species of mammals declared extinct in the last five hundred years. More
than a third of those species have subsequently been rediscovered.
Given that even scientific facts can have an expiration date, we would all be
well-advised to take a good hard look at our beliefs, which are formed and
updated in a much more haphazard way than those in science. We don’t need
someone challenging us to an actual bet to do this. We can think like a bettor,
purposefully and on our own, like it’s a game even if we’re just doing it
ourselves.
We would be better served as communicators and decision-makers if we
thought less about whether we are confident in our beliefs and more about
how
confident we are. Instead of thinking of confidence as all-or-nothing (“I’m

## Page 56

confident” or “I’m not confident”), our expression of our confidence would then
capture all the shades of grey in between.
When we express our beliefs (to others or just to ourselves as part of our
internal decision-making dialogue), they don’t generally come with
qualifications. What if, in addition to expressing what we believe, we also rated
our level of confidence about the accuracy of our belief on a scale of zero to ten?
Zero would mean we are certain a belief is not true. Ten would mean we are
certain that our belief is true. A zero-to-ten scale translates directly to
percentages. If you think the belief rates a three, that means you are 30% sure
the belief is accurate. A nine means you are 90% sure. So instead of saying to
ourselves, “
Citizen Kane
won the Oscar for best picture,” we would say, “I think
Citizen Kane
won the Oscar for best picture but I’m only a six on that.” Or “I’m
60% that
Citizen Kane
won the Oscar for best picture.” That means
your level of
certainty is such that 40% of the time it will turn out that
Citizen Kane
did not
win the best-picture Oscar. Forcing ourselves to express how sure we are of our
beliefs brings to plain sight the probabilistic nature of those beliefs, that what we
believe is almost never 100% or 0% accurate but, rather, somewhere in between.
In a similar vein, the number can reflect several different kinds of
uncertainty. “I’m 60% confident that
Citizen Kane
won best picture” reflects that
our knowledge of this past event is incomplete. “I’m 60% confident the flight
from Chicago will be late” incorporates a mix of our incomplete knowledge and
the inherent uncertainty in predicting the future (e.g., the weather might
intervene or there might be an unforeseen mechanical issue).
We can also express how confident we are by thinking about the number of
plausible alternatives and declaring that range. For example, if I am stating my
belief about what age Elvis died, I might say, “Somewhere between age forty
and forty-seven.” I know he died in his forties and I remember that it was his
earlier forties, so for me this is the range of plausible alternatives. The more we
know about a topic, the better the quality of information we have, the tighter the
range of plausible alternatives. (When it comes to predictions, the plausible
range of outcomes would also be tighter when there is less luck involved.) The
less we know about a topic or the more luck involved, the wider our range.
We can declare how sure we are whether we are thinking about a particular
fact or set of facts (“dinosaurs were herd animals”), a prediction (“I think there is
life on other planets”), or how the future will turn out given some decision we
might make (“I think I will be happier if I move to Des Moines than I am where
I live now” or “I think the company will be better off if we fire the president”).
These are all beliefs of differing sorts.

## Page 57

These are all beliefs of differing sorts.
Incorporating uncertainty into the way we think about our beliefs comes
with many benefits. By expressing our level of confidence in what we believe,
we are shifting our approach to how we view the world. Acknowledging
uncertainty is the first step in measuring and narrowing it. Incorporating
uncertainty in the way we think about what we believe creates open-mindedness,
moving us closer to a more objective stance toward information that disagrees
with us. We are less likely to succumb to motivated reasoning since it feels
better to make small adjustments in degrees of certainty instead of having to
grossly downgrade from “right” to “wrong.” When confronted with new
evidence, it is a very different narrative to say, “I was 58% but now I’m 46%.”
That doesn’t feel nearly as bad as “I thought I was right but now I’m wrong.”
Our narrative of being a knowledgeable, educated, intelligent person who holds
quality opinions isn’t compromised when we use new information to calibrate
our beliefs, compared with having to make a full-on reversal. This shifts us away
from treating information that disagrees with us as a threat, as something we
have to defend against, making us better able to truthseek.
When we work toward belief calibration, we become less judgmental of
ourselves. Incorporating percentages or ranges of alternatives into the expression
of our beliefs means that our personal narrative no longer hinges on whether we
were wrong or right but on how well we incorporate new information to adjust
the estimate of how accurate our beliefs are. There is no sin in finding out there
is evidence that contradicts what we believe. The only sin is in not using that
evidence as objectively as possible to refine that belief going forward.
Declaring our uncertainty in our beliefs to others makes us more credible
communicators. We assume that if we don’t come off as 100% confident, others
will value our opinions less. The
opposite is usually true. If one person expresses
a belief as absolutely true, and someone else expresses a belief by saying, “I
believe this to be true, and I’m 80% on it,” who are you more likely to believe?
The fact that the person is expressing their confidence as less than 100% signals
that they are trying to get at the truth, that they have considered the quantity and
quality of their information with thoughtfulness and self-awareness. And
thoughtful and self-aware people are more believable.
Expressing our level of confidence also invites people to be our
collaborators. As I said, most of us don’t live our lives in poker rooms, where it
is more socially acceptable to challenge a peer who expresses an opinion we
believe to be inaccurate to a wager. Outside of the poker room, when we declare
something as 100% fact, others might be reluctant to offer up new and relevant
information that would inform our beliefs for two reasons. First, they might be

## Page 58

information that would inform our beliefs for two reasons. First, they might be
afraid they are wrong and so won’t speak up, worried they will be judged for
that, by us or themselves. Second, even if they are very confident their
information is high quality, they might be afraid of making us feel bad or judged.
By saying, “I’m 80%” and thereby communicating we aren’t sure, we open the
door for others to tell us what they know. They realize they can contribute
without having to confront us by saying or implying, “You’re wrong.”
Admitting we are not sure is an invitation for help in refining our beliefs, and
that will make our beliefs much more accurate over time as we are more likely to
gather relevant information.
Expressing our beliefs this way also serves our listeners. We know that our
default is to believe what we hear, without vetting the information too carefully.
If we communicate to our listeners that we are not 100% on what we are saying,
they are less likely to walk away having been infected by our beliefs. Expressing
the
belief as uncertain signals to our listeners that the belief needs further
vetting, that step three is still in progress.
When scientists publish results of experiments, they share with the rest of
their community their methods of gathering and analyzing the data, the data
itself, and their confidence in that data. That makes it possible for others to
assess the quality of the information being presented, systematized through peer
review before publication. Confidence in the results is expressed through both p-
values, the probability one would expect to get the result that was actually
observed (akin to declaring your confidence on a scale of zero to ten), and
confidence intervals (akin to declaring ranges of plausible alternatives).
Scientists, by institutionalizing the expression of uncertainty, invite their
community to share relevant information and to test and challenge the results
and explanations. The information that gets shared back might confirm,
disconfirm, or refine published hypotheses. The goal is to advance knowledge
rather than affirm what we already believe. This is why science advances at a
fast clip.
*
By communicating our own uncertainty when sharing beliefs with others,
we are inviting the people in our lives to act like scientists with us. This
advances our beliefs at a faster clip because we miss out on fewer opportunities
to get new information, information that would help us to calibrate the beliefs we
have.
Acknowledging that decisions are bets based on our beliefs,
getting
comfortable with uncertainty, and redefining right and wrong are integral to a

## Page 59

good overall approach to decision-making. But I don’t expect that, having
dumped all these concepts in your lap, you should somehow know the best way
to use them. These patterns are so engrained in our thinking that it takes more
than knowing the problem or even having the right outlook to overcome the
irrationalities that hold us back. What I’ve done so far, really, is identify the
target; now that we are facing the right direction, thinking in bets is a tool to be
somewhat
better at hitting
it.

## Page 60

CHAPTER 3
Bet to Learn: Fielding the Unfolding Future
Nick the Greek, and other lessons from the Crystal
Lounge
When I first started playing poker, I lived in Columbus, Montana, population
1,200. The nearest poker game was forty miles away in downtown Billings, in
the basement of a bar called the Crystal Lounge. Every day, I drove those forty
miles, arriving by early afternoon. I would play until the evening, and drive
home.
The game was filled with characters out of a clichéd vision of Montana:
ranchers and farmers killing time in the off-season, filling the basement room
with smoke wafting over the brims of their cowboy hats. It was 1992 but it could
have just as easily been 1952 from the décor and the grizzled countenances of
the locals. The only thing suggesting that John Wayne wasn’t going to mosey on
in was a handful of misfits, including me (a woman and the youngest player by a
few decades, on the lam from defending my dissertation at the University of
Pennsylvania) and a player named “Nick the Greek.”
If your name is Nick, you come from Greece, and you gamble,
they’re going
to call you Nick the Greek, as sure as they’ll call you Tiny if you weigh more
than 350 pounds. (And, yes, there was a guy named Tiny, real name Elwood,
who regularly played in the game.) This Nick the Greek was of the small-time
Billings variety. He was the general manager of the hotel across the street,
having gotten transferred from Greece by the hotel chain. He left work for a
couple of hours every afternoon like clockwork to play in the game.
Nick the Greek had formed an unusual set of beliefs that guided his poker
decisions. I knew this because he described them to me and the other players, at
length, using the results of particular hands to punctuate his points. He fixated on

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length, using the results of particular hands to punctuate his points. He fixated on
the relatively common belief that the element of surprise was important in poker.
(Don’t be predictable, mix up your play—that sort of stuff.) Then he jacked it up
on steroids. To him, a starting pair of aces, the mathematically best two cards
you can get dealt, was the worst hand because everyone predictably played it.
“They always expect you to have aces. You get killed with that hand.”
By that logic, he explained, the very best two starting cards to play were the
mathematically weakest
two cards you could receive, a seven and a deuce of
different suits—a hand almost any player avoids.
“I bet you never saw it coming,” he would say when he turned over that
hand and won a pot. And because he played seven-deuce all the time,
occasionally the stars would line up and he’d win. I also remember times when
he threw away a pair of aces, faceup, at the first sign of a bet. (Never mind that
he was compromising a vital element of his strategy of subterfuge by constantly
showing and telling
us he was doing this. Given that he had
such an entrenched
set of beliefs, it’s not surprising that he didn’t see the incongruity.)
Nick the Greek, needless to say, rarely came out ahead. Yet he never
changed his strategy, often complaining about his bad luck when he lost, though
never in a bitter way. He was a friendly guy, pleasant to play with—the perfect
poker opponent. I tried to time my daily arrivals so I’d be in the game when he
made his afternoon appearance.
One day, Nick the Greek didn’t show up to the game. When I asked where
he was, another player muttered, confidentially (though it seemed everyone in
the game already knew about this), “Oh, he got sent back.”
“Sent back?”
“Yeah, to Greece. They deported him.”
I can’t say that Nick the Greek’s deportation was the result of his wacky
poker beliefs, but I have my suspicions. Other players speculated that he went
broke, or dipped into the till at the hotel, or lost his work visa because he was
playing poker every day on company time.
I can say that Nick the Greek lost a lot of money based on his beliefs—or,
more accurately, because he ignored lots of feedback that his strategy was a
losing one. He eventually went broke because he didn’t recognize learning
opportunities as they arose.
If Nick the Greek were unique in his resistance to learning from the
outcomes he was having at the poker table, I suppose he would just be a footnote
for me, a funny story of a guy unique in his ability to hold tight to his strategy

## Page 62

despite that strategy resulting in a lot of losing. But, while an extreme case to be
sure, Nick the Greek wasn’t all that unique. And that was a puzzle for me. I was
taught, as all psychology students are, that learning
occurs when you get lots of
feedback tied closely in time to decisions and actions. If we took that at face
value, poker would be an ideal learning environment. You make a bet, get an
immediate response from opponents, and win or lose the hand (with real-money
consequences), all within minutes.
So why was Nick the Greek, who had been playing for years, unable to learn
from his mistakes? Why was a novice like me cleaning up in the game? The
answer is that while experience is necessary to becoming an expert, it’s not
sufficient.
Experience can be an effective teacher. But, clearly, only some students
listen to their teachers. The people who learn from experience improve, advance,
and (with a little bit of luck) become experts and leaders in their fields. I
benefited from adopting the learning habits of some of the phenomenal poker
players I was exposed to along the way. We can all benefit from those practical
strategies to become better decision-makers. Thinking in bets can help us get
there.
But before getting to the solutions, we must first understand the problem.
What are the obstacles in our way that make learning from experience so
difficult? We all clearly have a desire to reach our long-term goals. Listening to
what our outcomes have to teach us is necessary to do that. So what is
systematically getting in the way?
Outcomes are feedback
We can’t just “absorb” experiences and expect to learn. As novelist and
philosopher Aldous Huxley recognized, “Experience is not what happens to a
man; it is what a man does with what happens
to him.” There is a big difference
between getting experience and becoming an expert. That difference lies in the
ability to identify when the outcomes of our decisions have something to teach
us and what that lesson might be.
Any decision, whether it’s putting $2 on Count de Change at the racetrack
or telling your kids they can eat whatever they want, is a bet on what will likely
create the most favorable future for us. The future we have bet on unfolds as a
series of outcomes. We bet on staying up late to watch the end of a football

## Page 63

series of outcomes. We bet on staying up late to watch the end of a football
game and we sleep through our alarm, wake up tired, get to work late, and get
reprimanded by the boss. Or we stay up late and any of the myriad other
outcomes follows, including waking up perfectly on time and making it to work
early. Whichever future actually unfolds, when we decide to stay up late to see
the end of the game, we are making a bet that we will be happier in the future for
having seen the final play. We bet on moving to Des Moines and we find our
dream job, meet the love of our life, and take up yoga. Or, like John Hennigan,
we move there, hate it within two days, and have to buy our way home for
$15,000. We bet on firing a division president or calling a pass play, and the
future unfolds as it does. We can represent this like so:
As the future unfolds into a set of outcomes, we are faced with another
decision: Why did something happen the way it did?
How we figure out what—if anything—we should learn from an outcome
becomes another bet. As outcomes come our way, figuring out whether those
outcomes were caused mainly by luck
or whether they were the predictable
result of particular decisions we made is a bet of great consequence. If we
determine our decisions drove the outcome, we can feed the data we get
following those decisions back into belief formation and updating, creating a
learning loop:
We have the opportunity to learn from the way the future unfolds to improve

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We have the opportunity to learn from the way the future unfolds to improve
our beliefs and decisions going forward. The more evidence we get from
experience, the less uncertainty we have about our beliefs and choices. Actively
using outcomes to examine our beliefs and bets closes the feedback loop,
reducing uncertainty. This is the heavy lifting of how we learn.
Ideally, our beliefs and our bets improve with time as we learn from
experience. Ideally, the more information we have, the better we get at making
decisions about which possible future to bet on. Ideally, as we learn from
experience we get better at assessing the likelihood of a particular outcome given
any decision, making our predictions about the future more accurate. As you
may have guessed, when it comes to how we process experience, “ideally”
doesn’t always apply.
Learning might proceed in a more ideal way if life were more like chess
than poker. The connection between outcome quality and decision quality would
be clearer because there would be less uncertainty. The challenge is that any
single outcome can happen
for multiple reasons. The unfolding future is a big
data dump that we have to sort and interpret. And the world doesn’t connect the
dots for us between outcomes and causes.
If a patient comes into a doctor’s office with a cough, the doctor must work
backward from that one symptom, that one outcome of a possible disease
process, to decide among the multiple reasons the patient might have that cough.
Is it because of a virus? Bacteria? Cancer? A neurological disorder? Because a
cough looks roughly the same whether it is from cancer or a virus, working
backward from the symptom to the cause is difficult. The stakes are high.
Misdiagnose the cause, and the patient might die. That is why doctors require
years of training to properly diagnose patients.
When the future coughs on us, it is hard to tell why.
Imagine calls to a customer by two salespeople from the same company. In
January, Joe pitches the company’s products and gets $1,000 in orders. In
August, Jane calls on the same customer and gets $10,000 in orders. What gives?
Was it because Jane is a better salesperson than Joe? Or was it because the
company updated its product line in February? Did a low-cost competitor go out
of business in April? Or is the difference in their success due to any of a variety
of other unconsidered reasons? It’s hard to know why because we can’t go back
in time and run the controlled experiment where Joe and Jane switch places. And
the way the company sorts this outcome can affect decisions on training, pricing,
and product development.

## Page 65

This problem is top of mind for poker players. Most poker hands end in a
cloud of incomplete information: one player bets, no one calls the bet, the bettor
is awarded the pot, and no one is required to reveal their hidden cards. After
those hands, the players are left guessing why they won or lost the hand. Did the
winner have a superior hand? Did the loser fold the best hand? Could the player
who won the hand have made more money if they chose a different line of play?
Could the player who lost have made the winner forfeit if they chose to play the
hand differently? In answering these questions, none of the players knows what
cards their opponents actually held, or how the players would have reacted to a
different sequence of betting decisions. How poker players adjust their play from
experience determines their future results. How they fill in all those blanks is a
vitally important bet on whether they get better at the game.
We are good at identifying the “-ER” goals we want to pursue (better,
smarter, richer, healthier, whatever). But we fall short in achieving our “-ER”
because of the difficulty in executing all the little decisions along the way to our
goals. The bets we make on when and how to close the feedback loop are part of
the execution, all those in-the-moment decisions about whether something is a
learning opportunity. To reach our long-term goals, we have to improve at
sorting out when the unfolding future has something to teach us, when to close
the feedback loop.
And the first step to doing this well is in recognizing that things sometimes
happen because of the other form of uncertainty: luck.
Luck vs. skill: fielding outcomes
The way our lives turn out is the result of two things: the influence of skill and
the influence of luck. For the purposes of this discussion, any outcome that is the
result of our decision-making is in the skill category. If making the same
decision again would
predictably result in the same outcome, or if changing the
decision would predictably result in a different outcome, then the outcome
following that decision was due to skill. The quality of our decision-making was
the main influence over how things turned out. If, however, an outcome occurs
because of things that we can’t control (like the actions of others, the weather, or
our genes), the result would be due to luck. If our decisions didn’t have much
impact on the way things turned out, then luck would be the main influence.
*
When a golfer hits a tee shot, where the ball lands is the result of the

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When a golfer hits a tee shot, where the ball lands is the result of the
influence of skill and luck, whether it is a first-time golfer or Rory McIlroy. The
elements of skill, those things directly in the golfer’s control that influence the
outcome, include club choice, setup, and all the detailed mechanics of the golf
swing. Elements of luck include a sudden gust of wind, somebody yelling their
name as they swing, the ball landing in a divot or hitting a sprinkler head, the
age of the golfer, the golfer’s genes, and the opportunities they received (or
didn’t receive) up to the moment of the shot.
An outcome like losing weight could be the direct result of a change in diet
or increased exercise (skill), or a sudden change in our metabolism or a famine
(luck). We could get in a car crash because we didn’t stop at a red light (skill) or
because another driver ran a red light (luck). A student could do poorly on a test
because they didn’t study (skill) or because the teacher is mean
(luck). I can lose
a hand of poker because I made poor decisions, applying the skill elements of
the game poorly, or because the other player got lucky.
Chalk up an outcome to skill, and we take credit for the result. Chalk up an
outcome to luck, and it wasn’t in our control. For any outcome, we are faced
with this initial sorting decision. That decision is a bet on whether the outcome
belongs in the “luck” bucket or the “skill” bucket. This is where Nick the Greek
went wrong.
We can update the learning loop to represent this like so:
Think about this like we are an outfielder catching a fly ball with runners on
base. Fielders have to make in-the-moment game decisions about where to throw
the ball: hit the cutoff man, throw behind a base runner, throw out an advancing
base runner. Where the outfielder throws after fielding the ball is a bet.

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We make similar bets about where to “throw” an outcome: into the “skill
bucket” (in our control) or the “luck bucket” (outside of our control). This initial
fielding of outcomes, if done well, allows us to focus on experiences that have
something to teach us (skill) and ignore those that don’t (luck). Get this right
and, with experience, we get closer to whatever “-ER” we are striving for: better,
smarter, healthier, happier, wealthier,
etc.
It is hard to get this right. Absent omniscience, it is difficult
to tell why
anything happened the way it did. The bet on whether to field outcomes into the
luck or skill bucket is difficult to execute because of ambiguity.
Working backward is hard: the SnackWell’s
Phenomenon
In the nineties, millions of people jumped on the SnackWell’s bandwagon.
Nabisco developed these devil’s food cookies as a leading product to take
advantage of the now-discredited belief that fat, not sugar, makes you fat. Foods
made with less fat were, at the time, considered healthier. With the blessing of
the U.S. government, companies swapped in sugar for fat as a flavoring
ingredient. SnackWell’s came in a green package, the color associated with “low
fat” and, therefore, “healthy”—like spinach!
For all those people trying to lose weight or make healthier snacking
choices, SnackWell’s were a delicious godsend. SnackWell’s eaters bet their
health on substituting these cookies for other types of snacks like, say, cashews,
which are high in fat. You could ingest sugar-laden SnackWell’s by the box,
because sugar wasn’t the enemy. Fat was the enemy, and the packaging
screamed “LOW FAT!”
Of course, we know now that obesity rose significantly during the low-fat
craze. (Michael Pollan used the phrase “SnackWell’s Phenomenon” in
describing people increasing their consumption of something that has less of a
bad ingredient.) As those SnackWell’s eaters gained weight, it wasn’t easy for
them to figure out why. Should the weight gain be fielded into the skill bucket,
used as feedback that their belief about the health value of SnackWell’s was
inaccurate? Or was the weight gain due to bad
luck, like a slow metabolism or
something else that wasn’t their fault or at least didn’t have to do with their

## Page 68

choice to eat SnackWell’s? If the weight gain got fielded into the luck bucket, it
wouldn’t be a signal to alter the choice to eat SnackWell’s.
Looking back
now
, it seems obvious how the weight gain should have been
fielded. But it is only obvious once you
know
that SnackWell’s are an unhealthy
choice. We have the benefit of twenty years of new research, more and better-
quality information about what causes weight gain. The folks on the low-fat
bandwagon had only their weight gain to learn from. The cards remained
concealed.
Working backward from the way things turn out isn’t easy. We can get to
the same health outcome (weight gain) by different routes. One person might
choose SnackWell’s; another might choose Oreos (also a Nabisco product,
developed by the same person who invented SnackWell’s); a third might choose
lentils and kale. If all three people gain weight, how can any of them figure it out
for sure?
Outcomes don’t tell us what’s our fault and what isn’t, what we should take
credit for and what we shouldn’t. Unlike in chess, we can’t simply work
backward from the quality of the outcome to determine the quality of our beliefs
or decisions. This makes learning from outcomes a pretty haphazard process. A
negative outcome could be a signal to go in and examine our decision-making.
That outcome could also be due to bad luck, unrelated to our decision, in which
case treating that outcome as a signal to change future decisions would be a
mistake. A good outcome could signal that we made a good decision. It could
also mean that we got lucky, in which case we would be making a mistake to use
that outcome as a signal to repeat that decision in the future.
When Nick the Greek won with a seven and a deuce, he fielded that
outcome into the skill bucket, taking credit for his brilliant strategy. When he
lost with that hand—a much more common occurrence—he wrote it off as bad
luck. His fielding error meant he never questioned his beliefs, no matter how
much he lost. We’re all like Nick the Greek sometimes. Uncertainty—luck and
hidden information—gave him the leeway to make fielding errors about why he
was losing. We all face uncertainty. And we all make fielding errors.
Rats get tripped up by uncertainty in a way that should appear very familiar
to us. Classical stimulus-response experiments have shown that the introduction
of uncertainty drastically slows learning. When rats are trained on a
fixed
reward
schedule (for example, a pellet for every tenth press of a lever), they learn pretty
fast to press that lever for food. If you withdraw the reward, the lever-pressing

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behavior is quickly extinguished. The rats figure out that no more food is on its
way.
But when you reward the rats on a variable or intermittent reinforcement
schedule (a pellet that comes
on average
every tenth lever press), that introduces
uncertainty. The average number of lever presses for the reward is the same, but
the rat could get a reward on the next press or not for thirty presses. In other
words, the rats are rewarded the way humans usually are: having no way to
know with certainty what will happen on the next try. When you withdraw the
reward from those rats, the lever-pressing behavior extinguishes only after a very
long time of fruitless lever pushing, sometimes thousands of tries.
We might imagine the rats thinking, “I bet the next lever press will get me a
pellet. . . . I’ve just been getting unlucky . . . I’m due.” Actually, we don’t even
have to imagine this. We can hear it if we listen to what people say while they
play slot
machines. Slot machines operate on a variable-payoff system. It’s no
wonder that, despite those machines being among the worst bets in the casino,
the banks of slots in a casino are packed. In the end, our rat brains dominate.
If this all doesn’t seem difficult enough, outcomes are rarely all skill or all
luck. Even when we make the most egregious mistakes and get appropriately
negative outcomes, luck plays a role. For every drunk driver who swerves into a
ditch and flips his car, there are several who swerve harmlessly across multilane
highways. It might feel like the drunk driver in the ditch deserved that outcome,
but the luck of the road conditions and presence or absence of other drivers also
played a role. When we do everything right, like drive through a green light
perfectly sober and live to tell the tale, there is also an element of luck. No one
else simultaneously ran a red light and hit us. There
wasn’t
a patch of ice on the
road to make us lose control of our vehicle. We
didn’t
run over a piece of debris
and blow a tire.
When we field our outcomes as the future unfolds, we always run into this
problem: the way things turn out could be the result of our decisions, luck, or
some combination of the two. Just as we are almost never 100% wrong or right,
outcomes are almost never 100% due to luck or skill. Learning from experience
doesn’t offer us the orderliness of chess or, for that matter, folding and sorting
laundry. Getting insight into the way uncertainty trips us up, whether the errors
we make are patterned (hint: they are) and what motivates those errors, should
give us clues for figuring out achievable strategies to calibrate the bets we make
on our outcomes.

## Page 70

“
If it weren’t for luck, I’d win every one”
Just as with motivated reasoning, our fielding errors aren’t random. They are,
borrowing from psychologist and behavioral economist Dan Ariely,
*
“predictably irrational.” The way we field outcomes is predictably patterned: we
take credit for the good stuff and blame the bad stuff on luck so it won’t be our
fault. The result is that we don’t learn from experience well.
“Self-serving bias” is the term for this pattern of fielding outcomes.
Psychologist Fritz Heider was a pioneer in studying how people make luck and
skill attributions about the results of their behavior. He said we study our
outcomes like scientists, but like “naïve scientists.” When we figure out why
something happened, we look for a plausible reason, but one that also fits our
wishes. Heider said, “It is usually a reason that flatters us, puts us in a good light,
and it is imbued with an added potency by the attribution.”
Our capacity for self-deception has few boundaries. Look at the reasons
people give for their accidents on actual auto insurance forms: “I collided with a
stationary truck coming the other way.” “A pedestrian hit me and went under my
car.” “The guy was all over the road. I had to swerve a number of times before I
hit him.” “An invisible car came out of nowhere, struck my car, and vanished.”
“The pedestrian had no idea which direction to run,
so I ran over him.” “The
telephone pole was approaching. I was attempting to swerve out of its way when
it struck my car.”
*
Stanford law professor and social psychologist Robert MacCoun studied
accounts of auto accidents and found that in 75% of accounts, the victims
blamed someone else for their injuries. In multiple-vehicle accidents, 91% of
drivers blamed someone else. Most remarkably, MacCoun found that in
single
-
vehicle accidents, 37% of drivers still found a way to pin the blame on someone
else.
We can’t write this off to lack of self-knowledge by a few bad drivers. John
von Neumann, considered a terror on the roads of Princeton, New Jersey, once
offered this explanation after smashing his car: “I was proceeding down the
road. The trees on the right were passing me in orderly fashion at 60 MPH.
Suddenly, one of them stepped out in my path. Boom!” Et tu, JvN? Et tu?
This predictable fielding error is probably the single most significant
problem for poker players. I watched it firsthand with Nick the Greek at the
Crystal Lounge. When he lost with seven-deuce, it was because he got unlucky
that time. When he won playing those cards, it was because his plan of “surprise
attack” was so brilliant. Off-loading the losses to luck and onboarding the wins

## Page 71

attack” was so brilliant. Off-loading the losses to luck and onboarding the wins
to skill meant he persisted in overestimating the likelihood of winning with
seven-deuce. He kept betting on a losing future.
And this is not confined to Nick the Greeks of the small-time Billings
variety. Phil Hellmuth, the biggest winner in
World Series of Poker history
(fourteen championship bracelets and counting), famously fell prey to this
fielding error. After getting eliminated from a televised poker tournament,
Hellmuth said on camera to ESPN, “If it weren’t for luck, I’d win every one.”
This line has become legend in the poker world. (It was even the basis for a
song, “I’d Win Everytime [If It Wasn’t for Luck]” in
All In: The Poker Musical
,
a show based on Phil’s life.) When the ESPN episode aired, there was a
collective gasp in the poker community. After all, Phil is saying that if the luck
element were eliminated from poker—if he were playing chess—his poker skills
are so superior that he would win every tournament he entered. So, clearly, any
negative outcomes are due to luck, and any positive outcomes are the result of
his superior skill.
Poker players may have gasped, but the only difference between Phil and
everyone else is that he said this
out loud and on television
. Most of us just have
the sense to keep the sentiment to ourselves, especially when the cameras and
mics are on. But, trust me, we are all vulnerable to the exact same thinking.
I, certainly, am not exempt. When I was playing poker, I did my share of
taking credit for winning and complaining about bad luck when I lost. It’s a
fundamental urge. I’ve been conscious of that tendency in all areas of my life.
Remember, we can know it’s a visual illusion, but that doesn’t keep us from still
seeing it.
Self-serving bias has immediate and obvious consequences for our ability to
learn from experience.
*
Blaming the bulk of our
bad outcomes on luck means
we miss opportunities to examine our decisions to see where we can do better.
Taking credit for the good stuff means we will often reinforce decisions that
shouldn’t be reinforced and miss opportunities to see where we could have done
better. To be sure, some of the bad stuff that happens is mainly due to luck. And
some of the good stuff that happens is mainly due to skill. I just know that’s not
true all the time. 100% of our bad outcomes aren’t because we got unlucky and
100% of our good outcomes aren’t because we are so awesome. Yet that is how
we process the future as it unfolds.
The predictable pattern of blaming the bad stuff on the world and taking
credit for the good stuff is by no means limited to poker or car accidents. It’s
everywhere.

## Page 72

everywhere.
When Chris Christie participated in a debate before the Republican
presidential primary in Iowa in early 2016, he played the part of behavioral
psychologist in his attack on Hillary Clinton’s response to the tragic outcome in
Benghazi: “She refuses to be held accountable for anything that goes wrong. If it
had gone right, believe me, she would have been running around to be able to
take credit for it.” Whether or not the accusation is correct, Christie certainly got
the human tendency right: we take credit for good things and deflect blame for
bad things. Ironically, only minutes earlier, he offered a pretty competitive
example of the bias in himself. When asked by the moderator if the GOP should
take a chance on nominating him in light of Bridgegate, he answered, “Sure,
because there’s been three different investigations that have proven that I knew
nothing.” Then he added, “And let me tell you something else. I inherited a state
in New Jersey that was downtrodden and beaten by liberal Democratic policies,
high taxes, high regulation. And this year, in 2015, New Jersey had the best year
of job growth that our state has ever had in the last fifteen years. That’s because
we’ve put conservative policies in place.”
That’s a pretty fast pivot from “that bad outcome isn’t my fault” to “and let
me tell you the good outcome I can take credit for
.
”
I described this pattern during an address at a meeting of the International
Academy of Trial Lawyers (IATL). One lawyer in the audience rushed to tell me
after the speech about a senior partner he trained under when he was first out of
law school. “You won’t believe how on-point this is, Annie. I assisted this
partner at several trials and at the end of each day, he would analyze the witness
testimony the same way. If the witness helped our case, he would say, ‘You see
how well I prepared that witness? When you know how to prepare a witness,
you get the results you want.’ If the witness hurt our case, he would tell me,
‘That guy refused to listen to me.’ It never varied.”
I’m betting any parent of a school-aged child knows this. On occasions
when my kids have done poorly on a test, it seems to have never been because
they didn’t study. “The teacher doesn’t like me. Everybody did poorly. The
teacher put material on the test we didn’t cover in class. You can ask anyone!”
Self-serving bias is a deeply embedded and robust thinking pattern.
Understanding why this pattern emerges is the first step to developing practical
strategies to improve our ability to learn
from experience. These strategies
encourage us to be more rational in the way we field outcomes, fostering open-
mindedness in considering all the possible causes of an outcome, not just the
ones that flatter us.

## Page 73

All-or-nothing thinking rears its head again
Black-and-white thinking, uncolored by the reality of uncertainty, is a driver of
both motivated reasoning and self-serving bias. If our only options are being
100% right or 100% wrong, with nothing in between, then information that
potentially contradicts a belief requires a total downgrade, from right all the way
to wrong. There is no “somewhat less sure” option in an all-or-nothing world, so
we ignore or discredit the information to hold steadfast in our belief.
Both of these biases cause us to see our outcomes through the equivalent of
a funhouse mirror. The reflection distorts reality, maximizing the appearance of
our skill in good outcomes. For bad outcomes, it makes skill all but disappear,
while luck looms giant.
Just as with motivated reasoning, self-serving bias arises from our drive to
create a positive self-narrative. In that narrative, taking credit for something
good is the same as saying we made the right decision. And being right feels
good. Likewise, thinking that something bad was our fault means we made a
wrong decision, and being wrong feels bad. When our self-image is at stake, we
treat our fielding decisions as 100% or 0%: right versus wrong, skill versus luck,
our responsibility versus outside our control. There are no shades of grey.
Fielding outcomes with the goal of promoting our self-narrative and doing it
in an all-or-nothing fashion alters our ability to make smart bets about why the
future unfolded in a particular way. Learning from experience is difficult—and
sometimes impossible—with this kind of biased, broad-brush thinking.
Outcomes are rarely the result of our decision quality alone or chance alone, and
outcome quality is not a perfect indicator of the influence of luck or skill. When
it comes to self-serving bias, we act as if our good outcomes are perfectly
correlated to good skill and our bad outcomes are perfectly correlated to bad
luck.
*
Whether it is a poker hand, an auto accident, a football call, a trial
outcome, or a business success, there are elements of luck and skill in virtually
any outcome.
That the motivation to update our self-image in a positive way underlies
self-serving bias gives us an idea of where we might look for solutions to
overcome the bias. Maybe we could stop clinging to ego, giving up on that need
to have a positive narrative of our lives. Maybe we could still drive a positive
narrative
but, instead of updating through credit and blame, we could get off on
striving to be more objective and open-minded in assessing the influence of luck
and skill on our outcomes. Maybe we could put in the time and hard work to

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retrain the way we process results, moving toward getting our positive self-
image updates from accurate fielding, from truthseeking.
Or maybe we could detour around the obstacles altogether by finding a
work-around that doesn’t require us to address self-serving bias at all.
People watching
One could conceivably argue that maybe self-serving bias isn’t such a big deal
because we can learn from other people’s experience. Maybe the solution that
has evolved is to compensate for the obstacles in learning from our own
experience by watching other people do stuff. There are more than seven billion
other people on the planet who do stuff all the time. As Yogi Berra said, “You
can observe a lot by watching.”
Watching is an established learning method. There is an entire industry
devoted to collecting other people’s outcomes. When you read the
Harvard
Business Review
or any kind of business or management case study, you’re
trying to learn from others. An important element of medical education is
watching doctors perform medical procedures or other caregivers do their jobs
up close. They watch, then they assist . . . and then, hopefully, they’ve learned.
Who would want a surgeon who says, “This will be the first time I’ve seen
inside a live human body”?
It’s possible we could follow that example, going to
school on the experiences of the people around us.
In poker, the bulk of what goes on is watching. An experienced player will
choose to play only about 20% of the hands they are dealt, forfeiting the other
80% of the hands before even getting past the first round of betting. That means
about 80% of the time is spent just watching other people play. Even if a poker
player doesn’t learn all that efficiently from the outcomes of the hands they play
themselves, there is still a whole lot to be learned from watching what happens
to the other players in the game. After all, there is four times as much watching
everyone else as there is playing a hand yourself.
Not only are all those other people’s outcomes plentiful, they are also
free
(aside from any ante). When a poker player chooses to play a hand, they are
putting their own money at risk. When a poker player is just watching the game,
they get to sit back while other people put money at risk. That’s an opportunity
to learn at no extra cost.

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When any of us makes decisions in life away from the poker table, we
always have something at risk: money, time, health, happiness,
etc.
When it’s
someone else’s decision, we don’t have to pay to learn. They do. There’s a lot of
free information out there.
Unfortunately, learning from watching others is just as fraught with bias.
Just as there is a pattern in the way we field our own outcomes, we field the
outcomes of our peers predictably. We use the same black-and-white thinking as
with our own outcomes, but now we flip the script. Where we blame our own
bad outcomes on bad luck, when it comes to our peers, bad outcomes are clearly
their fault. While our own good outcomes are due to
our awesome decision-
making, when it comes to other people, good outcomes are because they got
lucky. As artist and writer Jean Cocteau said, “We must believe in luck. For how
else can we explain the success of those we don’t like?”
When it comes to watching the bad outcomes of other people, we load the
blame on them, quickly and heavily. One of the most famous moments in
baseball history, in which 40,000 people at a baseball stadium and tens of
millions around the world instantly blamed an otherwise typical fan for keeping
the Chicago Cubs out of the World Series, demonstrates this. The incident is
known as the Bartman play.
In 2003, the Chicago Cubs were one game from reaching their first World
Series since 1945. They led three games to two in the series against the Florida
Marlins, and led Game Six with one out in the top of the eighth inning. A
Marlins hitter lifted a foul fly toward the left-field stands. At the base of the
sloping wall separating spectators from the field, Cubs left fielder Moises Alou
reached his glove over his head for the ball as several spectators on the other
side of the wall also reached for it. One of the 40,000 spectators at Wrigley
Field, Steve Bartman, deflected the ball, which then bounced off the railing,
landing at the feet of another spectator. Alou stormed away, expressing anger at
the fan who kept him from attempting to catch the ball.
The Cubs had a 3–0 lead at the time Bartman, those folks near him, and
Moises Alou reached for the foul ball. Had Alou caught it, the Cubs would have
been four outs from reaching the World Series. Here’s how the future unfolded
after Bartman touched the ball: The Cubs lost the game, and Game Seven too,
failing again to reach the World Series. Bartman got the blame, first from the
40,000 people present (who pointed and chanted “A**hole!,” threw beer and
garbage at him, and yelled death threats), then from the
millions of Cubs fans,
not just on sports and news shows replaying the incident at the time but for more

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than a decade. One person who actually assaulted Bartman while he was
surrounded by stadium security said, “I wanted to expose him for ruining what
could have been a once-in-a-lifetime experience.”
Steve Bartman had a bad outcome. He reached for the ball, and the Cubs
eventually lost. Was that due to his bad decision-making or bad luck? To be
sure, he made the decision to reach for the ball, so there was some skill in that.
He also, however, had an overwhelming amount of bad luck. Almost uniformly,
people discounted that bad luck, blaming Bartman for the loss of that game and
the series itself.
Alex Gibney’s ESPN documentary on the Bartman play,
Catching Hell,
displays the double standard, showing the replay from numerous angles and
interviewing spectators and members of the media from the scene. Gibney noted
(and the footage clearly showed), “There are a lot of people who went for that
ball.” One fan, who lunged right next to Bartman for the ball, couldn’t deny that
he had reached for the ball, the same as Bartman. “I went for the ball. There’s no
way . . . I obviously went for the ball.” Yet, he tried to claim that, unlike
Bartman, he never would have interfered with the play: “Once I saw [Moises
Alou’s] glove, I had no interest in the ball.” That fan, at once, took credit for his
good outcome in not having touched the ball and placed blame squarely on
Bartman’s decision-making rather than bad luck.
While everybody in the vicinity behaved the same way, Bartman was the
unlucky one who touched the baseball. But the fans didn’t see it as bad luck.
They saw it as his fault. Worse yet, none of the subsequent things that happened
in the game—all clearly outside Bartman’s control—mitigated his responsibility.
Remember that after that ball landed in the stands, the Cubs
were in the same
position they were in before the Bartman play. They still had the Marlins down
to their last five outs, with a 3–0 lead in the game, their ace pitcher hurling a
shutout, and a 3–2 lead in a best-of-seven series. That batter who hit the foul ball
was still at the plate, with a 3–2 count. The Marlins went on to score
eight runs
in the inning, seven of them after (two batters later) the Cubs shortstop, Alex
Gonzalez, bobbled an inning-ending double-play ball for an error.
Bartman had a lot of bad luck to end up with that result, much of which had
to do with the play of the team, over which Bartman clearly had no control. The
fans, nevertheless, laid all the blame on Bartman rather than on, say, Gonzalez.
Nearby fans yelled, “Rot in hell! Everyone in Chicago hates you! You suck!” As
he walked through the concourse, people yelled, “We’re gonna kill you! Go to
prison!” “Put a twelve-gauge in his mouth and pull the trigger!”

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It would be nice if a story line developed that gave Steve Bartman credit for
the Cubs winning the World Series in 2016. After all, Steve Bartman was a key
player in the chain of events responsible for a moribund franchise hiring
baseball-turnaround specialist Theo Epstein as president of baseball operations
and Joe Maddon as manager. Not surprisingly, that narrative hasn’t gotten any
traction.
*
We see this pattern of blaming others for bad outcomes and failing to give
them credit for good ones all over the place. When someone else at work gets a
promotion instead of us, do we admit they worked harder than and deserved it
more than we did? No, it was because they schmoozed the boss. If someone does
better on a test at school, it was because the teacher likes them more. If someone
explains the circumstances of a car accident and how it wasn’t their fault, we roll
our eyes. We assume the cause was their bad driving.
When I started in poker, I followed the same pattern (and still fight the urge
in every area of my life to this day). Even though I was quick to take credit for
my own successes and blame bad luck for my losses, I flipped this when
evaluating other players. I didn’t give other players enough credit for winning
(or, viewed another way, give other players credit for my losing) and I was quick
to blame their losses on their poor play.
When I first started, my brother gave me a list of cards that were good to
play, written on a napkin while we were eating in a coffee shop at Binion’s
Horseshoe Casino. I clutched this napkin like I was Moses clutching the Ten
Commandments. When I saw people win with hands that were not on my
brother’s list, I dismissed it as good luck since they clearly did not know how to
play. I was so closed-minded to the thought that they might deserve credit for
winning that I didn’t even bother to describe these hands to my brother to ask
him if there might be a good reason they would play a hand off the list.
As my understanding of the game grew over time, I realized the hands on
that list weren’t the only hands you could ever play.
For one thing, only playing
hands on the list meant you would never bluff. My brother gave me that list to
keep me, a total novice, out of trouble. He gave me that list because the hands he
was telling me to play would limit the mistakes a total beginner might make. I
didn’t know that, depending on all sorts of factors, sometimes there are hands
that wouldn’t be on the list that would be perfectly okay to play.
Even though I had access to the list writer, I never asked my brother why
these guys were playing these off-list cards. My biased assessment of why they
were winning slowed my learning down considerably. I missed out on a lot of
opportunities to make money because I dismissed other players as lucky when I

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opportunities to make money because I dismissed other players as lucky when I
might have been learning from watching them. To be sure, some of those people
shouldn’t have been playing those hands and were actually playing poorly. But,
as I figured out almost a year into playing, not all of them.
The systematic errors in the way we field the outcomes of our peers comes
at a real cost. It doesn’t just come at the cost of reaching our goals but also at the
cost of compassion for others.
Other people’s outcomes reflect on us
We all want to feel good about ourselves in the moment, even if it’s at the
expense of our long-term goals. Just as with motivated reasoning and self-
serving bias, blaming others for their bad results and failing to give them credit
for their good ones is under the influence of ego. Taking credit for a win lifts our
personal narrative. So too does knocking down a peer by finding them at fault
for a loss. That’s schadenfreude: deriving pleasure from
someone else’s
misfortune. Schadenfreude is basically the opposite of compassion.
Ideally, our happiness would depend on how things turn out for us
regardless of how things turn out for anyone else. Yet, on a fundamental level,
fielding someone’s bad outcome as their fault feels good to us. On a fundamental
level, fielding someone’s good outcome as luck helps our narrative along.
This outcome fielding follows a logical pattern in zero-sum games like
poker. When I am competing head-to-head in a poker hand, I
must
follow this
fielding pattern to square my self-serving interpretation of my own outcomes
with the outcomes of my opponent. If I win a hand in poker, my opponent loses.
If I lose a hand in poker, my opponent wins. Wins and losses are symmetrical. If
I field my win as having to do with my skillful play, then my opponent in the
hand must have lost because of their less skillful play. Likewise, if I field my
loss as having to do with luck, then my opponent must have won due to luck as
well. Any other interpretation would create cognitive dissonance.
Thinking about it this way, we see that the way we field other people’s
outcomes is just part of self-serving bias. Viewed through this lens, the pattern
begins to make sense.
But this comparison of our results to others isn’t confined to zero-sum
games where one player directly loses to the other (or where one lawyer loses to
opposing counsel, or where one salesperson loses a sale to a competitor, etc.).

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We are really in competition for resources with everyone. Our genes are
competitive. As Richard Dawkins points out, natural selection proceeds by
competition among the phenotypes of genes so we literally evolved to compete,
a drive that allowed our species to survive. Engaging the world through the lens
of competition is deeply embedded in our animal brains. It’s not enough to boost
our self-image solely
by our own successes. If someone we view as a peer is
winning, we feel like we’re losing by comparison. We benchmark ourselves to
them. If their kids are doing better in school than ours, what are we doing wrong
with our kids? If their company is in the news because it is about to go public,
what’s wrong with us that we’re just inching forward in our work?
We think we know the ingredients for happiness. Sonja Lyubomirsky, a
psychology professor at the University of California, Riverside, and popular
author on the subject of happiness, summarized several reviews of the literature
on the elements we commonly consider: “a comfortable income, robust health, a
supportive marriage, and lack of tragedy or trauma.” Lyubomirsky noted,
however, that “the general conclusion from almost a century of research on the
determinants of well-being is that objective circumstances, demographic
variables, and life events are correlated with happiness less strongly than
intuition and everyday experience tell us they ought to be. By several estimates,
all of these variables put together account for no more than 8% to 15% of the
variance in happiness.” What accounts for most of the variance in happiness is
how we’re doing
comparatively.
(The breadth and depth of all that research on
happiness and its implications is important, but it’s beyond what we need to
understand our issue with sorting others’ outcomes. I encourage you to read
Lyubomirsky’s work on the subject, Daniel Gilbert’s
Stumbling on Happiness
,
and Jonathan Haidt’s
The Happiness Hypothesis
, cited in the Selected
Bibliography and Recommendations for Further Reading.)
A consistent example of how we price our own happiness relative to others
comes from a version of the party game “Would You Rather . . . ?” When you
ask people if they would rather earn $70,000 in 1900 or $70,000 now, a
significant number choose
1900. True, the average yearly income in 1900 was
about $450. So we’d be doing phenomenally well compared to our peers from
1900. But no amount of money in 1900 could buy Novocain or antibiotics or a
refrigerator or air-conditioning or a powerful computer we could hold in one
hand. About the only thing $70,000 bought in 1900 that it couldn’t buy today
was the opportunity to soar above most everyone else. We’d rather lap the field
in 1900 with an average life expectancy of only forty-seven years than sit in the

## Page 80

middle of the pack now with an average life expectancy of over seventy-six
years (and a computer in our palm).
A lot of the way we feel about ourselves comes from how we think we
compare with others. This robust and pervasive habit of mind impedes learning.
Luckily, habits can be changed, whether the habit is biting your nails or decrying
your terrible luck when you lose. By shifting what it is that makes us feel good
about ourselves, we can move toward a more rational fielding of outcomes and a
more compassionate view of others. We can learn better and be more open-
minded if we work toward a positive narrative driven by engagement in
truthseeking and striving toward accuracy and objectivity: giving others credit
when it’s due, admitting when our decisions could have been better, and
acknowledging that almost nothing is black and white.
Reshaping habit
Phil Ivey is one of those guys who can easily admit when he could have done
better. Ivey is one of the world’s best poker players, a player almost universally
admired by other professional poker players for his exceptional skill and
confidence in his game. Starting
in his early twenties, he built a reputation as a
top cash-game player, a top tournament player, a top heads-up player, a top
mixed-game player—a top player in every form and format of poker. In a
profession where, as I’ve explained, most people are awash in self-serving bias,
Phil Ivey is an exception.
In 2004, my brother provided televised final-table commentary for a
tournament in which Phil Ivey smoked a star-studded final table. After his win,
the two of them went to a restaurant for dinner, during which Ivey deconstructed
every potential playing error he thought he might have made on the way to
victory, asking my brother’s opinion about each strategic decision. A more run-
of-the-mill player might have spent the time talking about how great they
played, relishing the victory. Not Ivey. For him, the opportunity to learn from his
mistakes was much more important than treating that dinner as a self-satisfying
celebration. He earned a half-million dollars and won a lengthy poker
tournament over world-class competition, but all he wanted to do was discuss
with a fellow pro where he might have made better decisions.
I heard an identical story secondhand about Ivey at another otherwise
celebratory dinner following one of his now ten World Series of Poker victories.

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celebratory dinner following one of his now ten World Series of Poker victories.
Again, from what I understand, he spent the evening discussing in intricate detail
with some other pros the points in hands where he could have made better
decisions. Phil Ivey, clearly, has different habits than most poker players—and
most people in any endeavor—in how he fields his outcomes.
Habits operate in a neurological loop consisting of three parts: the cue, the
routine, and the reward. A habit could involve eating cookies: the cue might be
hunger, the routine going to the pantry and grabbing a cookie, and the reward a
sugar high. Or, in poker, the cue might be winning a hand, the routine taking
credit for it, the reward a boost to our ego. Charles Duhigg, in
The Power of
Habit
, offers the golden rule of habit change—that the best way to deal with a
habit is to respect the habit loop: “To change a habit, you must keep the old cue,
and deliver the old reward, but insert a new routine.”
When we have a good outcome, it cues the routine of crediting the result to
our awesome decision-making, delivering the reward of a positive update to our
self-narrative. A bad outcome cues the routine of off-loading responsibility for
the result, delivering the reward of avoiding a negative self-narrative update.
With the same cues, we flip the routine for the outcomes of peers, but the reward
is the same—feeling good about ourselves.
The good news is that we can work to change this habit of mind by
substituting what makes us feel good. The golden rule of habit change says we
don’t have to give up the reward of a positive update to our narrative, nor should
we. Duhigg recognizes that respecting the habit loop means respecting the way
our brain is built.
Our brain is built to seek positive self-image updates. It is also built to view
ourselves in competition with our peers. We can’t install new hardware.
Working with the way our brains are built in reshaping habit has a higher chance
of success than working against it. Better to change the part that is more plastic:
the routine of what gives us the good feeling in our narrative and the features by
which we compare ourselves to others.
At least as far back as Pavlov, behavioral researchers
*
have recognized the
power of substitution in physiological loops. In his famous experiments, his
colleague noticed that dogs salivated
when they were about to be fed. Because
they associated a particular technician with food, the presence of the technician
triggered the dogs’ salivation response. Pavlov discovered the dogs could learn
to associate just about any stimulus with food, including his famous bell,
triggering the salivary response.
We can work to change the bell we ring, substituting what makes us
salivate. We can work to get the reward of feeling good about ourselves from

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salivate. We can work to get the reward of feeling good about ourselves from
being a good credit-giver, a good mistake-admitter, a good finder-of-mistakes-
in-good-outcomes, a good learner, and (as a result) a good decision-maker.
Instead of feeling bad when we have to admit a mistake, what if the bad feeling
came from the thought that we might be missing a learning opportunity just to
avoid blame? Or that we might be basking in the credit of a good result instead
of, like Phil Ivey, recognizing where we could have done better? If we work
toward that, we can transform the unproductive habits of mind of self-serving
bias and motivated reasoning into productive ones. If we put in the work to
practice this routine, we can field more of our outcomes in an open-minded,
more objective way, motivated by accuracy and truthseeking to drive learning.
The habit of mind will change, and our decision-making will better align with
executing on our long-term goals.
There are people who, like Phil Ivey, have substituted the routine of
truthseeking for the outcome-oriented instinct to focus on seeking credit and
avoiding blame. When we look at the people performing at the highest level of
their chosen field, we find that the self-serving bias that interferes with learning
often recedes and even disappears. The people with the most legitimate claim to
a bulletproof self-narrative have developed habits around accurate self-critique.
In sports, the athletes at the top of the game look at outcomes
to spur further
improvement. American soccer great Mia Hamm said, “Many people say I’m the
best women’s soccer player in the world. I don’t think so. And because of that,
someday I just might be.” Such quotes can be discounted as a polite way of
dealing with the media. There are plenty of contrary examples burned in our
consciousness, like John McEnroe arguing line calls or golf pros who have
turned missed putts into a ritual of staring at the offending line of the putt and
tapping down phantom spike marks. Those aren’t anything more than
performance tics. It’s practically a ritual on the PGA Tour that if a player misses
a makeable putt, he has to stare at the green like it was somehow at fault. What
you don’t see are practice rituals like Phil Mickelson’s, in which he places ten
balls in a circle, three feet from the hole. He has to sink all ten, and then repeat
the process
nine more times
. Players of Phil Mickelson’s caliber couldn’t engage
in such a demanding regimen if they actually assigned much blame to spike
marks.
Changing the routine is hard and takes work. But we can leverage our
natural tendency to derive some of our self-esteem by how we compare to our
peers. Just as Duhigg recommends respecting the habit loop, we can also respect
that we are built for competition, and that our self-narrative doesn’t exist in a

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that we are built for competition, and that our self-narrative doesn’t exist in a
vacuum. Keep the reward of feeling like we are doing well compared to our
peers, but change the features by which we compare ourselves: be a better credit-
giver than your peers, more willing than others to admit mistakes, more willing
to explore possible reasons for an outcome with an open mind, even, and
especially, if that might cast you in a bad light or shine a good light on someone
else. In this way we can feel that we are doing well by comparison because we
are doing something unusual and hard that most people don’t do. That makes us
feel exceptional.
Once we start listening for it, we hear a chorus out in the
world like I heard
during breaks in poker tournaments: “things are going great because I’m making
such good decisions”; “things went poorly because I got so unlucky.” That’s
what the lawyer heard from his senior partner in each evening’s postmortem of
the trial. That’s what we heard from Chris Christie in the 2016 Republican
presidential debate. That’s what I heard in every poker room I was ever in. There
were times, and there still are, when I’m part of that chorus. Increasingly,
though, I’ve learned to use that chorus to avoid self-serving bias rather than
giving in to it. When I admitted mistakes, when I recognized the luck element in
my successes, when I gave other players credit for making some good decisions,
when I was eager to share a hand that I thought I played poorly because I might
learn something from it, that chorus reminded me that what I was doing was
hard, and that others weren’t often doing it. Identifying learning opportunities
that other players were missing made me feel good about myself, reinforcing my
routine change.
Ideally, we wouldn’t compare ourselves with others or get a good feeling
when the comparison favors us. We might adopt the mindful practices of
Buddhist monks, observing the flow of inner thoughts, emotions, and bodily
sensations without judging them as good or bad at all. That’s a great goal, and
I’m all for a regular mindful practice. It will, the research indicates, help
improve quality of life and is worth pursuing. But getting all the way there is a
tall order if we don’t want to quit our day jobs and move to Tibet. It works
against the way our brains evolved, against our competitive drive. As a parallel
practice, the more practical and immediate solution is to work with what we’ve
got, using that comparison to strengthen our focus on accuracy and truthseeking.
Plus, we won’t have to give up our lives and find a remote mountaintop to live
on.
We need a mindset shift. We need a plan to develop a more productive habit
of mind. That starts in deliberative mind and requires foresight and practice, but

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if it takes hold, it can become an established habit, running automatically and
changing the way we reflexively think.
We can get to this mindset shift by behaving as if we have something at risk
when we sort outcomes into the luck and skill buckets, because we do
have a lot
at risk
on that fielding decision. Thinking in bets is a smart way to start building
habits that achieve our long-term goals.
“Wanna bet?” redux
Treating outcome fielding as a bet can accomplish the mindset shift necessary to
reshape habit. If someone challenged us to a meaningful bet on how we fielded
an outcome, we would find ourselves quickly moving beyond self-serving bias.
If we wanted to win that bet, we wouldn’t reflexively field bad outcomes as all
luck or good ones as all skill. (If you walked into a poker room and threw around
words like “always” and “never,” you’d soon find yourself challenged to a
bunch of bets. It’s easy to win a bet against someone who takes extreme
positions.)
Imagine getting into an accident at an intersection after losing control of
your car on a patch of ice you couldn’t see. Your first thought would likely be
that you got unlucky. But what if you had to bet on that? Depending on the
details, there would be a lot of alternatives to just plain bad luck you would now
consider. Based on the weather, maybe you could have anticipated some ice on
the road. Maybe you were driving too fast for the weather
conditions. Once the
car started sliding, maybe you could have steered differently or maybe you
pumped the brakes when you shouldn’t have. Maybe you could have taken a
safer route, choosing a main road that would have been salted. Maybe you
should have kept the Mustang in the garage and taken the Suburban.
Some of the reasons we come up with may be easy to discount. And some
may not. The key is that in explicitly recognizing that the way we field an
outcome is a bet, we consider a greater number of alternative causes more
seriously than we otherwise would have. That is truthseeking. This is what Phil
Ivey does.
The prospect of a bet makes us examine and refine our beliefs, in this case
the belief about whether luck or skill was the main influence in the way things
turned out. Betting on what we believe makes us take a closer look by making
explicit what is already implicit: we have a great deal at risk in assessing why

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explicit what is already implicit: we have a great deal at risk in assessing why
anything turned out the way it did. That sure sounds like a bet worth taking
seriously.
When we treat outcome fielding as a bet, it pushes us to field outcomes
more objectively into the appropriate buckets because that is how bets are won.
Winning feels good. Winning is a positive update to our personal narrative.
Winning is a reward. With enough practice, reinforced by the reward of feeling
good about ourselves, thinking of fielding outcomes as bets will become a habit
of mind.
Thinking in bets triggers a more open-minded exploration of alternative
hypotheses, of reasons supporting conclusions
opposite
to the routine of self-
serving bias. We are more likely to explore the opposite side of an argument
more often and more seriously—and that will move us closer to the truth of the
matter.
Thinking in bets also triggers perspective taking, leveraging the difference
between how we field our own outcomes versus
others’ outcomes to get closer
to the objective truth. We know we tend to discount the success of our peers and
place responsibility firmly on their shoulders for their failures. A good strategy
for figuring out which way to bet would be to imagine if that outcome had
happened to us. If a competitor closes a big sale, we know about our tendency to
discount their skill. But if we imagine that we had been the one who closed the
sale, we are more likely to find the things to give them credit for, that they did
well and that we can learn from. Likewise, when we close the big sale, let’s
spare a little of the self-congratulations and, instead, examine that great result the
way we’d examine it if it happened to someone else. We’ll be more likely to find
the things we could have done even better and identify those factors that we had
no control over. Perspective taking gets us closer to the truth because that truth
generally lies in the middle of the way we field outcomes for ourselves and the
way we field them for others. By taking someone else’s perspective, we are
more likely to land in that middle ground.
Once we start actively training ourselves in testing alternative hypotheses
and perspective taking, it becomes clear that outcomes are rarely 100% luck or
100% skill. This means that when new information comes in, we have options
beyond unquestioned confirmation or reversal. We can modify our beliefs along
a spectrum because we know it
is
a spectrum, not a choice between opposites
without middle ground.
This makes us more compassionate, both toward ourselves and others.
Treating outcome fielding as bets constantly reminds us outcomes are rarely

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attributable to a single cause and there is almost always uncertainty in figuring
out the various causes. Identifying a negative outcome doesn’t have the same
personal sting if you turn it into a positive by finding things to learn from
it. You
don’t have to be on the defensive side of every negative outcome because you
can recognize, in addition to things you can improve, things you did well and
things outside your control. You realize that not knowing is okay.
Certainly, in exchange for losing the fear of taking blame for bad outcomes,
you also lose the unadulterated high of claiming good outcomes were 100%
skill. That’s a trade you should take. Remember, losing feels about twice as bad
as winning feels good; being wrong feels about twice as bad as being right feels
good. We are in a better place when we don’t have to live at the edges. Euphoria
or misery, with no choices in between, is not a very self-compassionate way to
live.
You also become more compassionate toward other people when you treat
fielding outcomes as bets. When you look at the outcomes of others from their
perspective, you have to ask yourself, “What if that had happened to me?” You
come up with more compassionate assessments of other people where bad things
aren’t always their fault and good things aren’t always luck. You are more likely
to walk in their shoes. Imagine how Bartman’s life would have changed if more
people worked to think this way.
The hard way
Thinking in bets is hard, especially initially. It has to start as a deliberative
process, and will feel clunky, weird, and slow. Certainly, there will be times it
doesn’t make sense. Like if you don’t get a promotion at work, you’re probably
going to wonder how you’re supposed to feel better acknowledging that so-and-
so was more deserving and that you could learn a lot from them. It takes
work to
avoid the temptation to blame it on the boss being a jerk who doesn’t know how
to evaluate talent.
That feeling is natural. I built my poker career out of these principles of
learning and truthseeking, yet I still catch myself falling into the self-serving
bias and motivated reasoning traps. Duhigg tells us that reshaping a habit
requires time, preparation, practice, and repetition.
Look at other kinds of habit changes. If I had a habit of getting out of bed at
midnight to eat a cookie, it takes work as well as will to change that habit. I have

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midnight to eat a cookie, it takes work as well as will to change that habit. I have
to identify the habit I want to change, figure out the routine to substitute, and
practice that routine in deliberative mind until the habit is reshaped. I would
need to stock apples in the house, and keep them more readily available than
cookies. Then I need to actually eat the apple at midnight instead of reaching for
the cookie, repeating this routine until it becomes a new habit. That takes work
and willpower and time.
Despite the difficulties, striving for accuracy through probabilistic thinking
is a worthwhile routine to pursue. For one thing, it won’t always be so difficult.
We have to start doing this with deliberation and effort, but it eventually
becomes a habit of mind. Just like declaring uncertainty in your beliefs, it
eventually goes from a somewhat goofy and awkward extra step to a habit
integral to how you view the world around you.
To be sure, thinking in bets is not a miracle cure. Thinking in bets won’t
make self-serving bias disappear or motivated reasoning vanish into thin air. But
it will make those things better. And a little bit better is all we need to transform
our lives. If we field just a few extra outcomes more accurately, if we catch just
a few extra learning opportunities, it will make a huge difference in what we
learn, when we learn, and how much we learn.
Poker’s compressed version of real-world decision-making showed me how
being a little better at decision-making could make a big difference. A poker
game can consist of a few hundred hands. Every hand can require up to twenty
decisions. If, over the course of a game, there were a hundred outcomes that
provided learning opportunities and we caught ten of them, we would still be
missing 90% of our chances to learn. We wouldn’t have transcended the way our
brains function and built ourselves a different brain, but we don’t need to. If our
opponents are people like Nick the Greek, they are missing almost every
learning opportunity. We’re obviously going to do better than they do just
catching 10%. If another of our opponents is someone just like us, but who isn’t
working to transform their outcome-processing routines, maybe they (this prior
version of ourselves) will pick up five opportunities. Again, we are missing
90%, and we are still going to clean up on an opponent who is trying to learn but
doesn’t know how.
*
The benefits of recognizing just a few extra learning opportunities
compound over time. The cumulative effect of being a little better at decision-
making, like compounding interest, can have huge effects in the long run on
everything that we do. When we catch that extra occasional learning
opportunity, it puts us in a better position for future opportunities of the same

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type. Any improvement in our decision quality puts us in a better position in the
future. Think of it like a ship sailing from New
York to London. If the ship’s
navigator introduces a one-degree navigation error, it would start off as barely
noticeable. Unchecked, however, the ship would veer farther and farther off
course and would miss London by miles, as that one-degree miscalculation
compounds mile over mile. Thinking in bets corrects your course. And even a
small correction will get you more safely to your destination.
The first step is identifying the habit of mind that we want to reshape and
how to reshape it. That first step is hard and takes time and effort and a lot of
missteps along the way. So the second step is recognizing that it is easier to
make these changes if we aren’t alone in the process. Recruiting help is key to
creating faster and more robust change, strengthening and training our new
truthseeking
routines.

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CHAPTER 4
The Buddy System
“
Maybe you’re the problem, do you think?”
When Lauren Conrad, star of MTV’s
The Hills,
appeared on the
Late Show with
David Letterman
in October 2008, her interview took an unexpected turn. The
first minute was standard talk-show banter for a twenty-two-year-old star of a
successful reality series: the amount of drama in her life. Less than a minute
later, Conrad asked Letterman if he was calling her an idiot.
She started off the interview by discussing her ongoing feud with her by-
then-former roommate Heidi Montag and Heidi’s boyfriend Spencer Pratt. In
case you’re not familiar, here is the backstory: Lauren and Heidi’s friendship
ended when they came to blows at a birthday party after Lauren accused Heidi
and Spencer of starting rumors that she had made a sex tape. In addition, Lauren
developed friendships with Stephanie (Spencer’s sister) and Holly (Heidi’s
sister), complicating the social and family encounters of everyone involved.
Lauren tried without success to strengthen the relationship between her
roommates, Audrina
and Lo. This strained Lauren’s friendship with Audrina,
who reestablished her friendship with Heidi. Brody Jenner was also in the mix,
dating Lauren, questioning her date with a
Teen Vogue
model, dating someone
else himself, arguing with Spencer about his friendship with Lauren, getting
accused of starting the rumors about Lauren’s sex tape,
etc.
All of that drama happening in Conrad’s life is what David Letterman was
referring to when he interjected, “That raises the question, maybe you’re the
problem, do you think?” That quip sent an interview that was supposed to be a
puffy, promotional chat into an uncomfortable tailspin.
Letterman immediately realized that he had taken the conversation into
much deeper, more serious territory than either of them could have anticipated.

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much deeper, more serious territory than either of them could have anticipated.
He tried to soften the blow in a self-deprecating way, adding that he had done
the same thing, for years refusing to close the learning loop by assuming
everybody around him was an idiot.
“Let me give you an example from my own life. . . . For a long time . . . I
thought, ‘Geez, people are idiots.’ Then it occurred to me, ‘Is it possible that
everybody’s an idiot? Maybe I’m the idiot,’ and it turns out I am.”
Conrad clearly didn’t want to hear it, replying, “Does that make me an idiot,
then?” Websites devoted to reality TV, gossip, media, and popular culture
immortalized the moment, and that’s how they saw it: Letterman “basically calls
Conrad an idiot” (Gawker.com), “ripped into” Conrad (Trendhunter.com),
“makes fun of Lauren Conrad” (Starpulse.com).
Letterman’s comment was actually quite perceptive. His mistake was
offering up the insight in an inappropriate forum to someone who hadn’t agreed
to that kind of truthseeking exchange.
Conrad certainly had a lot of drama in her life: enough that
MTV created
two
successive shows to document it. But, like most people do, she characterized
the drama as a series of things happening
to
her. In other words, the drama was
outside her control (luck). Letterman suggested some of it could be fielded into
the skill bucket, a suggestion that might have been helpful to Conrad in the
future if she had been receptive to it. Not surprisingly, she wasn’t.
Letterman had offered the helpful alternative hypothesis, unexpectedly, on a
late-night talk show where the norm is fluff and PR. Perhaps Letterman’s
approach would have been more appropriate in an Oprah Winfrey–style prime-
time interview. Or on one of those reality therapy shows where reality stars
agree to such an exchange. As it was, he violated the assumed social contract by
challenging Conrad to bet on her outcome fielding when she hadn’t agreed to
truthseek with him.
That exchange was similar to my interaction at the poker tournament with
the six-seven of diamonds guy. I thought he was asking for my advice, so I
responded by asking for more information to get an idea of whether he
accurately fielded his losing outcome as luck. He was expecting me to adhere to
the norm of being a sympathetic ear for a hard-luck story. When I attempted to
delve into the details, I violated this implied contract. I Lettermanned him.
Such interactions are reminders that not all situations are appropriate for
truthseeking, nor are all people interested in the pursuit. That being said, any of
us who wants to get better at thinking in bets would benefit from having more
David Lettermans in our lives. As the “original” Letterman learned from the
awkward exchange with Lauren Conrad, Lettermanning needs agreement by

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awkward exchange with Lauren Conrad, Lettermanning needs agreement by
both parties to be effective.
The red pill or the blue pill?
In the classic science-fiction film
The

Matrix
, when Neo (played by Keanu
Reeves) meets Morpheus (the hero-hacker played by Laurence Fishburne), Neo
asks Morpheus to tell him what “the matrix” is. Morpheus offers to show Neo,
giving him the choice between taking a blue pill and a red pill.
“You take the blue pill, the story ends. You wake up in your bed and believe
whatever you want to believe. You take the red pill, you stay in Wonderland and
I show you how deep the rabbit hole goes.”
As Neo reaches toward a pill, Morpheus reminds him, “Remember, all I am
offering is the truth. Nothing more.”
Neo chooses to see the world as it really is. He takes the red pill and is
pounded with a series of devastating truths. His comfortable world is a dream
created by machines to enslave him as an energy source. His job and lifestyle,
his clothes, his appearance, and the entire fabric of his life are an illusion
implanted in his brain. In the actual world, taking the red pill causes his body to
be unplugged from his feeding pod, flushed into a sewer, and picked up by
Morpheus’s pirate ship, the
Nebuchadnezzar
. As rebels against the machines,
Morpheus and his crew (and now Neo, due to his choice) live in cramped
quarters, sleep in uncomfortable cells, eat gruel, and wear rags. Machines are out
to destroy them.
The trade-off is that Neo sees the world as it actually is and, in the end, gets
to defeat the machines that have enslaved humanity.
In the movie, the matrix was built to be a more comfortable version of the
world. Our brains, likewise, have evolved to make our version of the world more
comfortable: our beliefs are nearly always correct; favorable outcomes are the
result of our skill;
there are plausible reasons why unfavorable outcomes are
beyond our control; and we compare favorably with our peers. We deny or at
least dilute the most painful parts of the message.
Giving that up is not the easiest choice. Living in the matrix is comfortable.
So is the natural way we process information to protect our self-image in the
moment. By choosing to exit the matrix, we are asserting that striving for a more
objective representation of the world, even if it is uncomfortable at times, will
make us happier and more successful in the long run.

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make us happier and more successful in the long run.
But it’s a trade-off that isn’t for everyone; it must be freely chosen to be
productive and sustainable. Morpheus (unlike Letterman) didn’t just go around
ripping people out of the matrix against their will. He asked Neo to make the
choice and exit the matrix with him.
If you have gotten this far in this book, I’m guessing that you are choosing
the red pill over the blue pill.
When I started playing poker, I chose truthseeking. Like Neo, I did it
reluctantly and wasn’t sure what I was getting into. My brother took a blunt
approach with me. My instinct was to complain about my bad luck and to marvel
at how poorly others played, decrying the injustice of any hand I might have lost.
He wanted to talk about where I had questions about my strategic decisions,
where I felt I might have made mistakes, and where I was confused on what to
do in a hand. I recognized he was passing along the approach he learned with his
friends, a group of smart, analytical East Coast players, many of whom, like Erik
Seidel,
*
were on their way to establishing themselves as legends
at the game. In
addition to introducing me to this approach, he also encouraged these
phenomenal professionals to treat me as a peer when discussing poker.
I was lucky to have access at such an early stage in my career to this group
of world-class players who became my learning pod in poker. And I was also
lucky that if I wanted to engage that group about poker, I had to ask about my
strategic decisions. I had to resist my urge to moan about my bad luck and focus
instead on where I felt I might have made mistakes and where I was confused on
what to do in a hand. Because I agreed to the group’s rules of engagement, I had
to learn to focus on the things I could control (my own decisions), let go of the
things I couldn’t (luck), and work to be able to accurately tell the difference
between the two.
I learned from this experience that thinking in bets was easier if I had other
people to help me. (Even Neo needed help to defeat the machines.) Remember
the buddy system from school field trips or camp? Teachers or counselors would
pair everybody up with a buddy. Our buddy was supposed to keep us from
wandering off or getting into water too deep, and we did the same for our buddy.
A good decision group is a grown-up version of the buddy system. To be sure,
even with help, none of us will ever be able to perfectly overcome our natural
biases in the way we process information; I certainly never have. But if we can
find a few people to choose to form a truthseeking pod with us and help us do
the hard work connected with it, it will move the needle—just a little bit, but

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with improvements that accumulate and compound over time. We will be more
successful in fighting bias, seeing
the world more objectively, and, as a result,
we will make better decisions. Doing it on our own is just harder.
Members of our decision pod could be our friends, or members of our
family, or an informal pod of coworkers, or an enterprise strategy group, or a
professional organization where members can talk about their decision-making.
Forming or joining a group where the focus is on thinking in bets means
modifying the usual social contract. It means agreeing to be open-minded to
those who disagree with us, giving credit where it’s due, and taking
responsibility where it’s appropriate, even (and especially) when it makes us
uncomfortable. That’s why, when we do it with others, we need to make it clear
the social contract is being modified, or feelings will get hurt, defensiveness will
rear its ugly head, and, just like Lauren Conrad, your audience won’t want to
hear what you have to say. So, while we find some people to think in bets with
us, with the rest of the world, it is generally better to observe the prevailing
social contract and not go around saying, “Wanna bet?” willy-nilly. (That
doesn’t mean we can’t ever engage in truthseeking outside of our group. Our
approach just needs to be less head-on, less Letterman-like. More on that later,
after we explore communications
within
the group.)
Out in the world, groups form all over the place because people recognize
how others can help us; the concept of working together on our individual
challenges is a familiar one. Having the help of others provides many decision-
making benefits, but one of the most obvious is that other people can spot our
errors better than we can. We can help others in our pod overcome their blind-
spot bias and they can help us overcome the same.
Whatever the obstacles to recruiting people into a decision group (and this
chapter points out several, along with strategies for overcoming them), it is
worth it to get a buddy to watch your
back—or your blind spot. The fortunate
thing is that we need to find only a handful of people willing to do the
exploratory thinking necessary for truthseeking. In fact, as long as there are three
people in the group (two to disagree and one to referee
*
), the truthseeking group
can be stable and productive.
It’s also helpful to recognize that people serve different purposes in our
lives. Even if we place a high value on truthseeking, that doesn’t mean everyone
in our lives has to adopt that or communicate with us in that way. Truthseeking
isn’t a cult; we don’t have to cut off people who don’t share that commitment.
Our Pilates friends or our football friends or any of our friends shouldn’t have to
take the red pill to remain our friends. Different friends fill different needs and

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take the red pill to remain our friends. Different friends fill different needs and
not all of them need to be cut from the same cloth. Those different groups can
also provide much-needed balance in our lives. After all, it takes effort to
acknowledge and explore our mistakes without feeling bad about ourselves, to
forgo credit for a great result, and to realize, with an open mind, that not all our
beliefs are true. Truthseeking flies in the face of a lot of comfortable behaviors;
it’s hard work and we need breaks to replenish our willpower.
In fact, in my poker strategy group, we understood the need to occasionally
opt out and off-load intense emotions before engaging in the work of accurately
fielding an outcome. If, for example, one of us just got eliminated from a
tournament, it was acceptable, every once in a while, to say, “For right now, I
just need to moan about my bad luck.” The key was that when we did that, we
recognized it was a temporary exception from the hard
work we were doing
together and to which we would return when the emotional rawness of the
moment passed.
We know our decision-making can improve if we find other people to join
us in truthseeking. And we know we need an agreement. What’s in that
agreement? What are the features of a productive decision-making pod? The
remainder of this chapter is devoted to offering answers to those questions.
Chapter 5 builds on that by providing a blueprint for rules of engagement within
truthseeking groups, how to keep the group from drifting off course, and the
productive habits of mind the group can reinforce in each of us.
Not all groups are created equal
A well-chartered group can be particularly useful for habits that are difficult to
break or change. This is not a crazy or even novel idea. We are all familiar with
how the group approach can help with reshaping habits involving eating,
consuming alcohol, and physical activity. The most well-known example of a
productive group approach is Alcoholics Anonymous (AA).
The first of AA’s founders, Bill W., initially refrained from drinking
through a difficult process that included years of failure, hopelessness,
hospitalization, drugs, and a transformative religious experience. To
maintain
sobriety, however, he realized that he needed to talk to another alcoholic. Bill W.
recruited Dr. Bob, the second founder of AA, on a trip to Akron, Ohio. Dr. Bob,
considered by family and doctors to be a hopeless and incurable alcoholic, kept

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Bill W. from drinking on the trip. In turn, Bill W.
eventually helped Dr. Bob
give up drinking. AA has subsequently helped millions of people get and stay
sober, and led to organizations trying the same approach with other difficult-to-
tackle habits like narcotics abuse, smoking, unhealthy eating, and abusive
relationships. That all sprang from the concept that we can do better with the
help of others.
But, while a group
can
function to be better than the sum of the individuals,
it doesn’t automatically turn out that way. Being in a group can improve our
decision quality by exploring alternatives and recognizing where our thinking
might be biased, but a group can also exacerbate our tendency to confirm what
we already believe. Philip Tetlock and Jennifer Lerner, leaders in the science of
group interaction, described the two kinds of group reasoning styles in an
influential 2002 paper: “Whereas confirmatory thought involves a one-sided
attempt to rationalize a particular point of view, exploratory thought involves
even-handed consideration of alternative points of view.” In other words,
confirmatory thought amplifies bias, promoting and encouraging motivated
reasoning because its main purpose is justification. Confirmatory thought
promotes a love and celebration of one’s own beliefs, distorting how the group
processes information and works through decisions, the result of which can be
groupthink. Exploratory thought, on the other hand, encourages an open-minded
and objective consideration of alternative hypotheses and a tolerance of dissent
to combat bias. Exploratory thought helps the members of a group reason toward
a more accurate representation of the world.
Without an explicit charter for exploratory thought and accountability to that
charter, our tendency when we interact with others follows our individual
tendency, which is toward confirmation. The expression “echo chamber”
instantly conjures up
the image of what results from our natural drift toward
confirmatory thought. That was the chorus I heard among some groups of
players during breaks of poker tournaments. When one player brought up how
unlucky they had gotten, another would nod in assent as a prelude to telling their
own hard-luck story, which, in turn, would be nodded at and assented to by the
group.
Lerner and Tetlock offer insight into what should be included in the group
agreement to avoid confirmatory thought and promote exploratory thought.
“Complex and open-minded thought is most likely to be activated when decision
makers learn prior to forming any opinions that they will be accountable to an
audience (a) whose views are unknown, (b) who is interested in accuracy, (c)

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who is reasonably well-informed, and (d) who has a legitimate reason for
inquiring into the reasons behind participants’ judgments/choices.” Their 2002
paper was one of several they coauthored supporting the conclusion that groups
can improve the thinking of individual decision-makers when the individuals are
accountable
to a group whose interest is in
accuracy
.
In addition to accountability and an interest in accuracy, the charter should
also encourage and celebrate a
diversity of perspectives
to challenge biased
thinking by individual members. Jonathan Haidt, a professor at New York
University’s Stern School of Business, is a leading expert in exploring group
thought in politics. Haidt, in his book
The Righteous Mind: Why Good People
Are Divided by Politics and Religion
, built on Tetlock’s work, connecting it with
the need for diversity. “If you put individuals together in the right way, such that
some individuals can use their reasoning powers to disconfirm the claims of
others, and all individuals feel some common bond or shared fate that allows
them to interact civilly, you can create a group that ends up producing good
reasoning as an emergent property of the social system. This is
why it’s so
important to have intellectual and ideological diversity within any group or
institution whose goal is to find truth.”
In combination, the advice of these experts in group interaction adds up to a
pretty good blueprint for a truthseeking charter:
1.
A focus on accuracy (over confirmation), which includes rewarding
truthseeking, objectivity, and open-mindedness within the group;
2.
Accountability, for which members have advance notice; and
3.
Openness to a diversity of ideas.
An agreement along these lines creates a common bond and shared fate
among members, allowing the group to produce sound reasoning.
None of this should be surprising to anyone who recognizes the benefits of
thinking in bets. We don’t win bets by being in love with our own ideas. We win
bets by relentlessly striving to calibrate our beliefs and predictions about the
future to more accurately represent the world. In the long run, the more objective
person will win against the more biased person. In that way, betting is a form of
accountability to accuracy. Calibration requires an open-minded consideration of
diverse points of view and alternative hypotheses. Wrapping all that into your
group’s charter makes a lot of sense.
The charter of the group must be communicated unambiguously, as Erik

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Seidel made clear to me. I had met Erik when I was a teenager, but when I
started running into him at poker tournaments, it was the first time we were
interacting in a business setting. Early on in my career, I saw Erik during a break
in a tournament, and started moaning to him about my bad luck in
losing a big
hand. In three sentences, he laid out all the elements of a productive group
charter. “I don’t want to hear it. I’m not trying to hurt your feelings, but if you
have a question about a hand, you can ask me about strategy all day long. I just
don’t think there’s much purpose in a poker story if the point is about something
you had no control over, like bad luck.”
When you think about a charter for truthseeking interactions, Erik Seidel
pretty much nailed it. He told me the rules of being in a pod with him. He
discouraged me from confirmatory or biased thought like “I got unlucky.” He
encouraged me to find things I might have control over and how to improve
decisions about those. I knew he would hold me accountable to these things in
future interactions. We would explore diverse ideas because he insisted that be
the focus of our interactions.
Because I was lucky enough to be part of a group with a truthseeking
charter, there was no question that my poker decision-making improved. When I
could consult them on in-progress decisions, like whether to move up in stakes
or bankroll management or game selection, their advice reduced the number of
errors I was making. Likewise, access to their range of strategies and
experiences improved the quality of my thinking and decisions on a continuing
basis. When I had questions or didn’t understand why something happened, they
would see things I didn’t. When they had questions or needed advice on a hand,
I wasn’t just helping them work through a decision they made but would often
get insights into my own game. Those interactions led to improvements in my
game I would have overlooked or, at best, figured out on my own only after
making a lot of costly errors.
Even better, interacting with similarly motivated people improves the ability
to combat bias not just during direct interactions
but when we are making and
analyzing decisions on our own. The group gets into our head—in a good way—
reshaping our decision habits.
The group rewards focus on accuracy
We all want to be thought well of, especially by people we respect. Lerner and

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We all want to be thought well of, especially by people we respect. Lerner and
Tetlock recognized that our craving for approval is incredibly strong and
incentivizing. In most laboratory situations, they noted, study participants
expected to explain their actions to someone they’d never met and never
expected to meet again. “What is remarkable about this literature is that—despite
the prevalence of these minimalist manipulations—participants still reliably
respond as if audience approval matters.” It’s great to get approval from people
we respect, but we crave approval so badly, we’ll still work to get it from a
stranger. A productive decision group can harness this desire by rewarding
accuracy and intellectual honesty with social approval.
Motivated reasoning and self-serving bias are two habits of mind that are
deeply rooted in how our brains work. We have a huge investment in
confirmatory thought, and we fall into these biases all the time without even
knowing it. Confirmatory thought is hard to spot, hard to change, and, if we do
try changing it, hard to self-reinforce. It is one thing to commit to rewarding
ourselves for thinking in bets, but it is a lot easier if we get others to do the work
of rewarding us.
Groups like AA demonstrate how a supportive group can provide the reward
for doing the hard work of changing a habit
routine, just by its approval. For
engaging in the difficult work involved in sobriety, local AA groups give tokens
or chips celebrating the length of individual members’ sobriety. The tokens
(which members often carry or customize as jewelry) are a tangible reminder
that others acknowledge you are accomplishing something difficult. There are
chips for marking one to sixty-five years of sobriety. There are also chips given
for every month of sobriety in the first year. There is even a chip given for being
sober for twenty-four hours.
I experienced firsthand the power of a group’s approval to reshape
individual thinking habits. I got my fix by
trying
to be the best credit-giver, the
best mistake-admitter, and the best finder-of-mistakes-in-good-outcomes. The
reward was their enthusiastic engagement and deep dives introducing me to the
nuances of poker strategy. It was also rewarding to have these intelligent,
successful players take my questions seriously and increasingly ask for
my
opinions. In contrast, I felt disapproval from them when I acted against the
charter and complained about my bad luck, or expected them to confirm how
great I played simply because I was winning.
While I never got close to attaining the goal of a pure focus on accuracy, my
group helped me to give a little more credit than I otherwise would have, to spot
a few more mistakes than I would have spotted on my own, to be more open-
minded to strategic choices that I disagreed with. That moved me, even if just a

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minded to strategic choices that I disagreed with. That moved me, even if just a
little bit at a time, toward my goal of getting closer to the objective truth. And
that little bit had a huge long-run impact on my success.
When I started playing poker, “discussing hands” consisted mostly of my
complaining about bad luck when I lost. My brother quickly got sick of my
moaning. He laid down the law and said I
was only allowed to ask him about
hands that I had won. If I wanted him to engage with me, I had to identify some
point in those hands where I might have made a mistake.
Talking about winning (even if we are identifying mistakes along the way to
a win) is less painful than talking about losing, allowing new habits to be more
easily trained. Identifying mistakes in hands I won reinforced the separation
between outcomes and decision quality. These discussions also made me feel
good about analyzing and questioning my decisions because of the approval I
got from Howard and the players I looked up to. I used that approval as evidence
that I understood the game and had promise as a player. When they
complimented me for finding alternative approaches in my winning hands or
understanding the contribution of luck, that felt terrific. In time, I could expand
this approach to identifying learning opportunities in any hand I played, not just
the winning ones.
Once we are in a group that regularly reinforces exploratory thought, the
routine becomes reflexive, running on its own. Exploratory thought becomes a
new habit of mind, the new routine, and one that is self-reinforced. In a
Pavlovian way, after enough approval from the group for doing the hard work of
thinking in bets, we get the same good feeling from focusing on accuracy on our
own. We internalize the group’s approval, and, as a matter of habit, we begin to
do the kind of things that would earn it when we are away from the group (which
is, after all, most of the time).
“One Hundred White Castles . . . and a large
chocolate shake”: how accountability improves
decision-making
David Grey is a high-stakes poker player and professional gambler, and a good
friend. After a night at a racetrack and a bowling alley in New Jersey, David and
a bunch of other bettors were hungry. It was late. Someone suggested White
Castle. A discussion broke out about how many burgers the biggest eater in the

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Castle. A discussion broke out about how many burgers the biggest eater in the
group, Ira the Whale, could eat.
When they got Ira the Whale to say he could eat 100 burgers (remember,
White Castle burgers are small), most of the group, not surprisingly, wanted to
bet against him. David was an exception. “I was a young guy, just getting
started. Fifty dollars was a big win or loss for me. There was about $2,000 out
against Ira the Whale. I bet $200 on him because I thought he could do it.”
When they got to White Castle, Ira the Whale decided to order the burgers
twenty at a time. David knew he was a lock to win as soon as Ira the Whale
ordered the first twenty, because Ira the Whale also ordered a milkshake and
fries.
After finishing the 100 burgers and after he and David collected their bets,
Ira the Whale ordered another twenty burgers to go, “for Mrs. Whale.”
Accountability is a willingness or obligation to answer for our actions or
beliefs to others. A bet is a form of accountability. If we’re in love with our own
opinions, it can cost us in a bet. Ira the Whale held the other gamblers
accountable for their beliefs about whether he could eat 100 White Castle
burgers. Accountability is why John Hennigan (briefly) moved to Des Moines.
After spending time in that kind of environment, you become hypervigilant
about your level of confidence in your beliefs. No
one is forced to make or take
such bets, but the prospect is a reminder that you can always be held accountable
for the accuracy of what you believe and say. It is truly putting your money
where your mouth is.
Being in an environment where the challenge of a bet is always looming
works to reduce motivated reasoning. Such an environment changes the frame
through which we view disconfirming information, reinforcing the frame change
that our truthseeking group rewards. Evidence that might contradict a belief we
hold is no longer viewed through as hurtful a frame. Rather, it is viewed as
helpful because it can improve our chances of making a better bet. And winning
a bet triggers a reinforcing positive update.
Accountability, like reinforcement of accuracy, also improves our decision-
making and information processing when we are away from the group because
we
know in advance
that we will have to answer to the group for our decisions.
Early in my poker career, my poker group recommended that a way to avoid the
effects of self-serving bias when I was losing was to have a preset “loss limit”—
if I lost $600 at the stakes I was playing, I would leave the game. The smart,
experienced players advising me knew that in the moment of losing, I might not
be my most rational self in assessing whether I was losing because I was getting

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unlucky or losing because I was playing poorly. A predetermined loss limit acts
as a check against irrationally chasing losses, but self-enforcement is a problem.
If you have more money in your pocket, you might still take it out. If you’re out
of money, casinos have ATMs and machines that let you get cash advances on
your credit cards. Poker players are also pretty liberal about lending money to
losing players.
I was much less likely to break a loss limit because I knew I was
accountable to my pod. If I reached my loss limit and my
inner voice said, “This
game is so good that I should put up more money and keep playing,” it also
reminded me I’d have to answer for the decision to a group of players I
respected. Accountability made me run that conversation in my head, in which I
started explaining how I was just getting unlucky and they would expose why I
was likely biased in my assessment, helping me resist the urge to buy more
chips. And, after leaving a losing game and going home, I could offset some of
the sting of losing by running the conversation where my pod would approve of
my decision to quit the game when I told them about it.
Imagining how the discussion will go helps us to spot more errors on our
own and catch them more quickly.
The group ideally exposes us to a diversity of
viewpoints
John Stuart Mill is one of the heroes of thinking in bets. More than one hundred
and fifty years after writing
On Liberty
, his thinking on social and political
philosophy remains startlingly current. One of the frequent themes in
On Liberty
is the importance of diversity of opinion. Diversity and dissent are not only
checks on fallibility, but the only means of testing the ultimate truth of an
opinion: “The only way in which a human being can make some approach to
knowing the whole of a subject, is by hearing what can be said about it by
persons of every variety of opinion, and studying all modes in which it can be
looked at by every character of mind. No wise man ever acquired his wisdom in
any mode but this; nor is it in the nature of human intellect to become wise in
any other manner.”
There is a simple beauty in Mill’s insight. On our own, we
have just one
viewpoint. That’s our limitation as humans. But if we take a bunch of people

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with that limitation and put them together in a group, we get exposed to diverse
opinions, can test alternative hypotheses, and move toward accuracy. It is almost
impossible for us, on our own, to get the diversity of viewpoints provided by the
combined manpower of a well-formed decision pod. To get a more objective
view of the world, we need an environment that exposes us to alternate
hypotheses and different perspectives. That doesn’t apply only to the world
around us: to view
ourselves
in a more realistic way, we need other people to fill
in our blind spots.
A group with diverse viewpoints can help us by sharing the work suggested
in the previous two chapters to combat motivated reasoning about beliefs and
biased outcome fielding. When we think in bets, we run through a series of
questions to examine the accuracy of our beliefs. For example:
Why might my belief not be true?
What other evidence might be out there bearing on my belief?
Are there similar areas I can look toward to gauge whether similar beliefs
to mine are true?
What sources of information could I have missed or minimized on the way
to reaching my belief?
What are the reasons someone else could have a different belief, what’s
their support, and why might they be right instead of me?
What other perspectives are there as to why things turned out the way they
did?
Just by asking ourselves these questions, we are taking a big step toward
calibration. But there is only so much we can do to
answer these questions on
our own. We only get exposed to the information we have been exposed to, only
live the experiences we have experienced, only think of the hypotheses that we
can conceive of. It’s hard to know what reasons someone else could have for
believing something different. We aren’t them. We haven’t had their
experiences. We don’t know what different information they have. But they do.
Much of our biased information processing stems from the amount of rope
that uncertainty affords us. Well-deployed diversity of viewpoints in a group can
reduce uncertainty due to incomplete information by filling in the gaps in what
we know, making life start to fit more neatly on a chessboard.
Others aren’t wrapped up in preserving our narrative, anchored by our
biases. It is a lot easier to have someone else offer their perspective than for you
to imagine you’re another person and think about what their perspective might

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to imagine you’re another person and think about what their perspective might
be. A diverse group can do some of the heavy lifting of de-biasing for us. A
poker table is a naturally diverse setting because we generally don’t select who
we play with for their opinions. Even better, when there is disagreement
stemming from the diverse opinions represented at a poker table, the discussion
may naturally progress toward betting on it. These are ideal circumstances for
promoting accuracy.
Numerous groups have recognized the need to engineer the kind of diversity
and encouragement of dissent that naturally occurs at a poker table. The State
Department, since the Vietnam War, has had a formal Dissent Channel, where
employees can have their dissenting views heard and addressed without fear of
penalty. The American Foreign Service Association, the professional
organization of foreign-service employees, has
four
separate awards it gives
annually to members “to recognize and encourage constructive dissent and risk-
taking in the Foreign Service.” The
Dissent Channel has been credited with a
policy change that helped end the genocidal war in Bosnia. In June 2016, fifty-
one State Department employees signed a memo calling for President Obama to
strengthen American military efforts in Syria. In late January 2017,
approximately one thousand employees signed a dissent cable in response to
President Trump’s executive order suspending immigration from seven Muslim-
majority countries. The Dissent Channel represents something hopeful in our
nation’s decision-making process. In an environment of increased polarization,
foreign-service employees can make their voices heard about policies with
which they disagree, and do it regardless of whether the administration is
Democrat or Republican. Allowing dissent has a value that transcends party
politics.
After September 11, the CIA created “red teams” that, according to
Georgetown law professor Neal Katyal in a
New York Times
op-ed, “are
dedicated to arguing against the intelligence community’s conventional wisdom
and spotting flaws in logic and analysis.” Senior Obama administration officials,
following the raid that killed Osama bin Laden, mentioned red-team analysis
among the methods used to measure the degree of confidence that bin Laden, in
the absence of visual or auditory confirmation, was in the compound subject to
the raid.
Dissent channels and red teams are a beautiful implementation of Mill’s
bedrock principle that we can’t know the truth of a matter without hearing the
other side. This commitment to diversity of opinion is something that we would

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be wise to apply to our own decision groups. For example, if a corporate strategy
group is figuring out how to integrate operations following a merger, someone
who initially opposed the merger would be good to have as part of the group.
Perhaps they have reasons why the two sales departments won’t mesh—
whatever their reasons,
they could help the majority move forward with a wiser
approach by taking those reasons into account.
Diversity is the foundation of productive group decision-making, but we
can’t underestimate how hard it is to maintain. We all tend to gravitate toward
people who are near clones of us. After all, it feels good to hear our ideas echoed
back to us. If there is any doubt about how easy it can be to fall into this
confirmatory drift, we can even see this tendency in groups we consider some of
the most dedicated to truthseeking: judges and scientists.
Federal judges: drift happens
Cass Sunstein, now a Harvard law professor, conducted a massive study with
colleagues when he was on the faculty at the University of Chicago Law School,
on ideological diversity in federal judicial panels. Sunstein recognized at the
outset that the U.S. Courts of Appeals are “an extraordinary and longstanding
natural experiment” in diversity. Appellate court panels are composed of three
judges randomly drawn from that circuit’s pool. Each circuit’s pool includes life-
tenured judges chosen (when an opening occurs or Congress recognizes the need
for additional judges) by the sitting president. In any particular appeal, you could
get a panel of three Democrat appointees, three Republican appointees, or a two-
to-one mix in either direction.
The study, encompassing over 6,000 federal appeals and nearly 20,000
individual votes, found, not surprisingly, that judicial voting generally followed
political lines. Pure, unaided open-mindedness, even by life-tenured judges
sworn to uphold the law, is hard.
When there was political diversity on the panels, the researchers found
several areas where that diversity improved the panel’s work. Even though, in
most cases, two politically similar judges could dictate the panel’s outcome,
there were significant differences between heterogeneous and homogeneous
panels. A single panelist from the other party had “a large disciplining effect.”
They found, for example, “strong evidence of ideological dampening” in
environmental cases. Democrat appointees, who overall voted for plaintiffs 43%

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environmental cases. Democrat appointees, who overall voted for plaintiffs 43%
of the time, voted for plaintiffs just 10% of the time when sitting with two
Republican appointees. Republican appointees, who overall voted for plaintiffs
20% of the time, voted for plaintiffs 42% of the time when seated with two
Democrat appointees. This held up across most of the twenty-five categories of
cases in which they had a sufficiently large sample to reach a conclusion.
The authors concluded that the result endorsed the importance of exposure
to diverse viewpoints: “What is necessary is reasonable diversity, or diversity of
reasonable views . . . and that it is important to ensure that judges, no less than
anyone else, are exposed to it, and not merely through the arguments of
advocates.”
Sunstein’s group found that federal appellate judges
need
the diverse
viewpoint of an opposing-party appointee. Judges, they found, followed the
human instinct of succumbing to groupthink. “Our data provide strong evidence
that like-minded judges also go to extremes: the probability that a judge will
vote in one or another direction is greatly increased by the presence of judges
appointed by the president of the same political party. In short, we claim to show
both strong conformity effects and group polarization within federal courts of
appeals.”
The growing polarization of the Supreme Court is a case in
point. Each
justice now has four clerks, all of whom have similar credentials: top-of-the-
class graduates of top law schools, law review editors, and clerkships with
federal appeals court judges. The clerks, over the years, have played an
increasingly important role in helping the justices with their intellectual
workload, discussing details of cases and drafting initial versions of opinions.
Prior to the appointment of Chief Justice Roberts in 2005, it was an informal
badge of honor, especially among some of the conservative members of the
court, that they hired clerks with ideological backgrounds that differed from
theirs. Bob Woodward and Scott Armstrong, in
The Brethren
, described how
Justice Powell “prided himself on hiring liberal clerks. He would tell his clerks
that the conservative side of the issues came to him naturally. Their job was to
present the other side, to challenge him. He would rather encounter a compelling
argument for another position in the privacy of his own chambers, than to meet it
unexpectedly at conference or in a dissent.”
Chief Justice Burger hired equally from the ranks of former clerks of
Democrat-and Republican-appointed judges. Chief Justice Rehnquist, who
served on the court with Burger and succeeded him, arrived at the court
suspicious of the role liberal clerks could have in influencing his opinion.

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According to
The Brethren
, however, that attitude disappeared almost
immediately. Rehnquist believed “the legal and moral interchanges that liberal
clerks thrived on were good for the Justices and for the Court.” Justice Scalia,
when he served on the D.C. circuit and in his early years on the Supreme Court,
was known for seeking out clerks with liberal ideologies.
As the Supreme Court has become more divided, this practice has all but
ceased. According to a
New York Times
article in 2010, only Justice Breyer
regularly employed clerks who had
worked for circuit judges appointed by
presidents of both parties. Since 2005, Scalia had hired no clerks with experience
working for Democrat-appointed judges. In light of the shift in hiring practices,
it should not be so surprising that the court has become more polarized. The
justices are in the process of creating their own echo chambers.
Justice Thomas, from 1986 to the time the article was written, was 84-for-84
in hiring clerks who had worked for Republican-appointed judges. Not
surprisingly, according to data compiled from the
Journal of Law, Economics,
and Organization
, he is the justice furthest from the ideological center of the
court, much further right than the most liberal-leaning justice (Sotomayor) is
left.
Thomas once said, “I won’t hire clerks who have profound disagreements
with me. It’s like trying to train a pig. It wastes your time, and it aggravates the
pig.”
*
That makes sense only if you believe the goal of a decision group is to
train people to agree with you. But if your goal is to develop the best decision
process, that is an odd sentiment indeed.
This polarization warns against forming a decision group that is a collection
of clones who share the same opinions and knowledge sources we do. The more
homogeneous we get, the more the group will promote and amplify confirmatory
thought. Sadly, that’s exactly what we drift toward. Even Supreme Court justices
do that. We are all familiar with this tendency in politics; it’s the complaint on
both sides of the political aisle. Conservatives complain that liberals live in an
echo chamber where they just repeat and confirm their point of view. They
aren’t open to
new information or ideas that don’t fit what they already believe.
That’s the exact same criticism liberals have of conservatives.
Although the Internet and the breadth of multimedia news outlets provide us
with limitless access to diverse opinions, they also give us an unprecedented
opportunity to descend into a bubble, getting our information from sources we
know will share our view of the world. We often don’t even realize when we are
in the echo chamber ourselves, because we’re so in love with our own ideas that
it all just sounds sensible and right. In political discourse, virtually everyone,

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it all just sounds sensible and right. In political discourse, virtually everyone,
even those familiar with groupthink, will assert, “I’m in the rational group
exchanging ideas and thinking these things through. The people on the other
side, though, are in an echo chamber.”
We must be vigilant about this drift in our groups and be prepared to fight it.
Whether it is the forming of a group of friends or a pod at work—or hiring for
diversity of viewpoint and tolerance for dissent when you are able to guide an
enterprise’s culture toward accuracy—we should guard against gravitating
toward clones of ourselves. We should also recognize that it’s really hard: the
norm is toward homogeneity; we’re all guilty of it; and we don’t even notice that
we’re doing it.
Social psychologists: confirmatory drift and
Heterodox Academy
In 2011, Jon Haidt, speaking to an audience of 1,000 social psychologists, noted
the lack of viewpoint diversity in their field. He reported that he could identify
only one conservative social psychologist with any degree of field-wide
recognition.
Surveys of sociologists’ professional organizations have found that 85%–
96% of members responding self-identified as left of center, voted for Obama in
2012, or scored left of center on a questionnaire of political views. (Most of the
remaining 4%–15% identified as centrist or moderate rather than conservative.)
The trend has a long tail, but it has been accelerating. In the 1990s, liberals
among social psychologists outnumbered conservatives 4-to-1. More recent
surveys show that the ratio has grown to greater than 10-to-1, sometimes far
greater. A tendency to hire for a conforming worldview combined with the
discouraging aspects of being so decisively outnumbered ideologically suggests
that, unchecked, this situation won’t get better. According to the surveys
establishing this trend toward homogeneity, about 10% of faculty respondents
identified as conservative, compared with just 2% of grad students and
postdoctoral candidates.
Haidt, along with Philip Tetlock and four others (social psychologists José
Duarte, Jarret Crawford, and Lee Jussim, and sociologist Charlotta Stern)
founded an organization called Heterodox Academy, to fight this drift toward
homogeneity of thought in science and academics as a whole. In 2015, they

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published their findings in the journal
Behavioral and Brain Sciences
(
BBS
),
along with thirty-three pieces of open peer commentary. The
BBS
paper
explained and documented the political imbalance in social psychology, how it
reduces the quality of science, and what can be done to improve the situation.
Social psychology is particularly vulnerable to the effects of political
imbalance. Social psychologists are researching many of the hot-button issues
dividing the political Left and Right: racism, sexism, stereotypes, and responses
to power and authority. Coming from a community composed almost entirely of
liberal-leaning scientists, the quality and impact of research can suffer.
The authors identified instances in which political values became
“embedded into research questions in ways that make some constructs
unobservable and unmeasurable, thereby invalidating attempts at hypothesis
testing.” This occurred in several experiments involving attitudes on
environmental issues and attempts to link ideology to unethical behavior. They
also identified the risk of researchers concentrating on topics that validated their
shared narrative and avoiding topics that contested that narrative, such as
stereotype accuracy and the scope and direction of prejudice. Finally, they
pointed to the obvious problem inherent in the legitimacy of research
characterizing conservatives as dogmatic and intolerant done by a discipline that
is over 10-to-1 liberal leaning.
First, the Heterodox Academy effort shows that there is a natural drift
toward homogeneity and confirmatory thought. We all experience this
gravitation toward people who think like we do. Scientists, overwhelmingly
trained and chartered toward truthseeking, aren’t immune. As the authors of the
BBS
paper recognized, “Even research communities of highly intelligent and
well-meaning individuals can fall prey to confirmation bias, as IQ is
positively
correlated with the number of reasons people find to support their own side in an
argument.” That’s how robust these biases are. We see that even judges and
scientists succumb to these biases. We shouldn’t feel bad, whatever our
situation, about admitting that we also need help.
Second, groups with diverse viewpoints are the best protection against
confirmatory thought. Peer review, the gold standard that epitomizes the open-
mindedness and hypothesis testing of the scientific method, “offers much less
protection against error when the community of peers is politically
homogeneous.” In other words, the opinions of group members aren’t much help
if it is a
group of clones. Experimental studies cited in the
BBS
paper found that
confirmation bias led reviewers “to work extra hard to find flaws with papers

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whose conclusions they dislike, and to be more permissive about methodological
issues when they endorse the conclusions.” The authors of the
BBS
paper
concluded that “[n]obody has found a way to eradicate confirmation bias in
individuals, but we can diversify the field to the point to where individual
viewpoint biases begin to cancel out each other.”
The
BBS
paper, and the continuing work of Heterodox Academy, includes
specific recommendations geared toward encouraging diversity and dissenting
opinions. I encourage you to read the specific recommendations, which include
things like a stated antidiscrimination policy (against opposing viewpoints),
developing ways to encourage people with contrary viewpoints to join the group
and engage in the process, and surveying to gauge the actual heterogeneity or
homogeneity of opinion in the group. These are exactly the kinds of things we
would do well to adopt (and, where necessary, adapt) for groups in our personal
lives and in the workplace.
Even among those who are committed to truthseeking, judges and
academics, we can see how strong the tendency is to seek out confirmation of
our beliefs. If you have any doubt this is true for all of us, put this book down for
a moment and check your Twitter feed for whom you follow. It’s a pretty safe
bet that the bulk of them are ideologically aligned with you. If that’s the case,
start following some people from the other side of the aisle.
Wanna bet (on science)?
If thinking in bets helps us de-bias, couldn’t we apply it to help solve the
Heterodox Academy problem? One might guess that scientists would be more
accurate if they had to bet on the likelihood that results would replicate as
compared to traditional peer review, which can be vulnerable to viewpoint bias.
Especially in an anonymous betting market, confirming the strength of your pre-
existing ideology or betting solely on the basis that replication of a study
confirms your own work or beliefs counts for nothing. The way a scientist would
be “right” in such a betting market is by using their skill in a superior way to
make the most objective bets on whether results would or would not replicate.
Researchers who knew in advance their work would be subject to a market test
would also face an additional form of accountability that would likely modulate
their reporting of results.

## Page 110

At least one study has found that, yes, a betting market where scientists
wager on the likelihood of experimental results replicating was more accurate
than expert opinion alone. In psychology, there has been a controversy over the
last decade about a potentially large number of published studies with results
subsequent researchers could not replicate. The Reproducibility Project:
Psychology has been working on replicating studies from top psychology
journals. Anna Dreber, a behavioral economist at the Stockholm School of
Economics, with several colleagues set up a betting market based on these
replication attempts. They recruited a bunch of experts in the relevant fields and
asked their opinions on the likelihood the Reproducibility Project would
replicate the results of forty-four studies. They then gave those
experts money to
bet on each study’s replication in a prediction market.
Experts engaging in traditional peer review, providing their opinion on
whether an experimental result would replicate, were right 58% of the time. A
betting market in which the traders were the exact same experts and those
experts had money on the line predicted correctly 71% of the time.
A lot of people were surprised to learn that the expert opinion expressed as a
bet was more accurate than expert opinion expressed through peer review, since
peer review is considered a rock-solid foundation of the scientific method. Of
course, this result shouldn’t be surprising to readers of this book. We know that
scientists are dedicated to truthseeking and take peer review seriously. Arguably,
there is already an implied betting element in the scientific process, in that
researchers and peer reviewers have a reputational stake in the quality of their
review. But we know that scientists, like judges—and like
us
—are human and
subject to these patterns of confirmatory thought. Making the risk explicit rather
than implicit refocuses us all to be more objective.
A growing number of businesses are, in fact, implementing betting markets
to solve for the difficulties in getting and encouraging contrary opinions.
Companies implementing prediction markets to test decisions include Google,
Microsoft, General Electric, Eli Lilly, Pfizer, and Siemens. People are more
willing to offer their opinion when the goal is to win a bet rather than get along
with people in a room.
Accuracy, accountability, and diversity wrapped into a group’s charter all
contribute to better decision-making, especially if the group promotes thinking
in bets. Now that we understand the elements of a good charter, we move on to
the rules of engagement
for a productive decision group, how to most effectively
communicate with one another. A pioneering sociologist actually designed a set

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of truthseeking norms for a group (scientists) that form a pretty good blueprint
for engagement. I don’t know if he was a bettor, but he was influenced by
something very relevant to thinking about bias, rationality, and the potential gulf
between perception and reality: he was a
magician.

## Page 112

CHAPTER 5
Dissent to Win
CUDOS to a magician
Meyer R. Schkolnick was born on the Fourth of July, 1910, in South
Philadelphia. He performed magic at birthday parties as a teenager and
considered a career as a performer. He adopted the performing name “Robert
Merlin.” Then a friend convinced him that a teen magician naming himself after
Merlin was too on the nose, so he performed as Robert Merton. When Robert K.
Merton (to distinguish him from his son, economist and Nobel laureate Robert
C. Merton) died in 2003, the
New York Times
called him “one of the most
influential sociologists of the 20th century.”
The founders of Heterodox Academy, in the
BBS
paper, specifically
recognized Merton’s 1942 and 1973 papers, in which he established norms for
the scientific community known by the acronym CUDOS: “An ideologically
balanced science that routinely resorted to adversarial collaborations to resolve
empirical disputes would bear a striking resemblance to Robert Merton’s
ideal-
type model of a self-correcting epistemic community, one organized around the
norms of CUDOS.” Per the
BBS
paper, CUDOS stands for
C
ommunism (data belong to the group),
U
niversalism (apply uniform standards to claims and evidence, regardless of
where they came from),
D
isinterestedness (vigilance against potential conflicts that can influence the
group’s evaluation), and

## Page 113

O
rganized
S
kepticism (discussion among the group to encourage
engagement and dissent).
If you want to pick a role model for designing a group’s practical rules of
engagement, you can’t do better than Merton. To start, he coined the phrase
“role model,” along with “self-fulfilling prophecy,” “reference group,”
“unintended consequences,” and “focus group.” He founded the science of
sociology and was the first sociologist awarded the National Medal of Science.
Merton began his academic career in the 1930s, studying the history of
institutional influences on the scientific community. To him, it was a story of
many periods of scientific advancement spurred on by geopolitical influences,
but also periods of struggle to maintain independence from those influences. His
life spanned both world wars and the Cold War, in which he studied and
witnessed nationalist movements in which people “arrayed their political selves
in the garb of scientists,” explicitly evaluating scientific knowledge based on
political and national affiliations.
In 1942, Merton wrote about the normative structure of science. He tinkered
with the paper over the next thirty-one
years, publishing the final version as part
of a book in 1973. This twelve-page paper is an excellent manual for developing
rules of engagement for any truthseeking group. I recognized its application to
my poker group and professional and workplace groups I’ve encountered in
speaking and consulting. Each element of CUDOS—communism, universalism,
disinterestedness, and organized skepticism—can be broadly applied and
adapted to push a group toward objectivity. When there is a drift toward
confirmation and away from exploring accuracy, it’s likely the result of the
failure to nurture one of Merton’s norms. Not surprisingly, Merton’s paper
would make an excellent career guide for anyone seeking to be a profitable
bettor, or a profitable decision-maker period.
Mertonian communism: more is more
The Mertonian norm of communism (obviously, not the political system) refers
to the communal ownership of data within groups. Merton argued that, in
academics, an individual researcher’s data must eventually be shared with the
scientific community at large for knowledge to advance. “Secrecy is the

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antithesis of this norm; full and open communication its enactment.” In science,
this means that the community has an agreement that research results cannot
properly be reviewed without access to the data and a detailed description of the
experimental design and methods. Researchers are entitled to keep data private
until published but once they accomplish that, they should throw the doors open
to give the community every opportunity to make a proper assessment. Any
attempt at accuracy is bound to fall short if the
truthseeking group has only
limited access to potentially pertinent information. Without all the facts,
accuracy suffers.
This ideal of scientific sharing was similarly described by physicist Richard
Feynman in a 1974 lecture as “a kind of utter honesty—a kind of leaning over
backwards. For example, if you’re doing an experiment, you should report
everything that you think might make it invalid—not only what you think is
right about it: other causes that could possibly explain your results . . .”
It is unrealistic to think we can perfectly achieve Feynman’s ideal; even
scientists struggle with it. Within our own decision pod, we should strive to
abide by the rule that “more is more.” Get all the information out there. Indulge
the broadest definition of what could conceivably be relevant. Reward the
process of pulling the skeletons of our own reasoning out of the closet. As a rule
of thumb, if we have an urge to leave out a detail because it makes us
uncomfortable or requires even more clarification to explain away, those are
exactly the details we must share. The mere fact of our hesitation and discomfort
is a signal that such information may be critical to providing a complete and
balanced account. Likewise, as members of a group evaluating a decision, we
should take such hesitation as a signal to explore further.
To the extent we regard self-governance in the United States as a
truthseeking experiment, we have established that openness in the sharing of
information is a cornerstone of making and accounting for decisions by the
government. The free-press and free-speech guarantees of the Constitution
recognize the importance of
self
-expression, but they also exist because we need
mechanisms to assure that information makes it to the public. The government
serves the people, so the people own the data and have a right to have the data
shared with them. Statutes like
the Freedom of Information Act have the same
purpose. Without free access to information, it is impossible to make reasoned
assessments of our government.
Sharing data and information, like the other elements of a truthseeking
charter, is done by agreement. Academics agree to share results. The government
shares information by agreement with the people. Without an agreement, we

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shares information by agreement with the people. Without an agreement, we
can’t and shouldn’t compel others to share information they don’t want to share.
We all have a right of privacy. Companies and other entities have rights to trade
secrets and to protect their intellectual property. But within our group, an
agreement to share details pertinent to assessing the quality of a decision is part
of a productive truthseeking charter.
If the group is discussing a decision and it doesn’t have all the details, it
might be because the person providing them doesn’t realize the relevance of
some of the data. Or it could mean the person telling the story has a bias toward
encouraging a certain narrative that they likely aren’t even aware of. After all, as
Jonathan Haidt points out, we are all our own best PR agents, spinning a
narrative that shines the most flattering light on us.
We have all experienced situations where we get two accounts of the same
event, but the versions are dramatically different because they are informed by
different facts and perspectives. This is known as the Rashomon Effect, named
for the 1950 cinematic classic
Rashomon
, directed by Akira Kurosawa. The
central element of the otherwise simple plot was how incompleteness is a tool
for bias. In the film, four people give separate, drastically different accounts of a
scene they all observed, the seduction (or rape) of a woman by a bandit, the
bandit’s duel with her husband (if there was a duel), and the husband’s death
(from losing the duel, murder, or suicide).
Even without conflicting versions, the Rashomon Effect reminds us that we
can’t assume one version of a story is accurate or complete. We can’t count on
someone else to provide the other side of the story, or any individual’s version to
provide a full and objective accounting of all the relevant information. That’s
why, within a decision group, it is helpful to commit to this Mertonian norm on
both sides of the discussion. When presenting a decision for discussion, we
should be mindful of details we might be omitting and be extra-safe by adding
anything that could possibly be relevant. On the evaluation side, we must query
each other to extract those details when necessary.
My consultation with the CEO who traced his company’s problems to firing
the president demonstrated the value of a commitment to data sharing. After he
described what happened, I requested a lot more information. As he got into
details of the hiring process for that executive and approaches to dealing with the
president’s deficiencies on the job, that led to further questions about those
decisions, which, in turn, led to more details being shared. He was identifying
what he thought was a bad decision, justified by his initial description of the
situation. After we got every detail out of all the dimensions of the decision, we
reached a different conclusion: the decision to fire the president had been quite

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reached a different conclusion: the decision to fire the president had been quite
reasonable strategically. It just happened to turn out badly.
Be a data sharer. That’s what experts do. In fact, that’s one of the reasons
experts become experts. They understand that sharing data is the best way to
move toward accuracy because it extracts insight from your listeners of the
highest fidelity.
You should hear the amount of detail a top poker player puts into the
description of a hand when they are workshopping that hand with another player.
A layperson would think, “That seems
like a lot of irrelevant, nitpicky detail.
Why are they saying all that stuff?” When two expert poker players get together
to trade views and opinions about hands, the detail is extraordinary: the positions
of everyone acting in the hand; the size of the bets and the size of the pot after
each action; what they know about how their opponent(s) has played when they
have encountered them in the past; how they were playing in the particular game
they were in; how they were playing in the most recent hands in that game
(particularly whether they were winning or losing recently); how many chips
each person had throughout the hand; what their opponents know about them,
etc.,
etc.
What the experts recognize is that the more detail you provide, the
better the assessment of decision quality you get. And because the same types of
details are always expected, expert players essentially work from a template, so
there is less opportunity to convey only the information that might lead the
listener down a garden path to a desired conclusion.
Hall of Fame football coach John Madden, in a documentary about Vince
Lombardi, told a story about how, as a young assistant coach, he attended a
coaching clinic where Lombardi spoke about one play: the power sweep, a
running play that he made famous with the Green Bay Packers in the 1960s.
Lombardi held the audience spellbound as he described that one play for eight
hours. Madden said, “I went in there cocky, thinking I knew everything there
was to know about football, and he spent eight hours talking about this one
play. . . . I realized then that I actually knew nothing about football.”
We are naturally reluctant to share information that could encourage others
to find fault in our decision-making. My group made this easier by making me
feel good about committing myself to improvement. When I shared details that
cast me in what
I perceived to be a bad light, I got a positive self-image update
from the approval of players I respected. In my consulting, I’ve encouraged
companies to make sure they don’t define “winning” solely by results or
providing a self-enhancing narrative. If part of corporate success consists of

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providing the most accurate, objective, and detailed evaluation of what’s going
on, employees will compete to win on those terms. That will reward better habits
of mind.
Agree to be a data sharer and reward others in your decision group for
telling more of the story.
Universalism: don’t shoot the message
The well-known advice “don’t shoot the messenger” is actually good shorthand
for the reasons why we want to protect and encourage dissenting ideas.
Plutarch’s
Life of Lucullus
provided an early, literal example: the king of
Armenia got advance notice that Lucullus’s troops were approaching. He killed
the messenger for delivering that message and, henceforth, messengers stopped
reporting such intelligence. Obviously, if you don’t like the message, you
shouldn’t take it out on the messenger.
The Mertonian norm of universalism is the converse. “Truth-claims,
whatever their source, are to be subjected to preestablished impersonal criteria.”
It means acceptance or rejection of an idea must not “depend on the personal or
social attributes of their protagonist.” “Don’t shoot the message,” for some
reason, hasn’t gotten the same historical or literary attention, but it addresses an
equally important decision-making issue: don’t disparage or
ignore an idea just
because you don’t like who or where it came from.
When we have a negative opinion about the person delivering the message,
we close our minds to what they are saying and miss a lot of learning
opportunities because of it. Likewise, when we have a positive opinion of the
messenger, we tend to accept the message without much vetting. Both are bad.
Whether the situation involves facts, ideas, beliefs, opinions, or predictions,
the substance of the information has merit (or lack of merit) separate from where
it came from. If you’re deciding the truth of whether the earth is round, it doesn’t
matter if the idea came from your best friend or George Washington or Benito
Mussolini. The accuracy of the statement should be evaluated independent of its
source.
I learned an early lesson in my poker career about universalism. I started
playing poker using that list of hands my brother Howard wrote on a napkin. I
treated this initial advice like a holy document. Therefore, when I saw someone
playing hands off-list, I immediately labeled them as a bad player. When I saw

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playing hands off-list, I immediately labeled them as a bad player. When I saw
such a player subsequently execute a strategy I didn’t have as part of my game, I
dismissed it. Doing that across the board (especially when I was labeling these
players as “bad” based on one view of a sound beginner’s strategy) was an
expensive lesson in universalism. For so many things going on at the table in the
first years of my poker career, I shot the message.
I was guilty of the same thing David Letterman admitted in his explanation
to Lauren Conrad. He spent a long time assuming people around him were idiots
before considering the alternative hypothesis, “Maybe I’m the idiot.” In poker, I
was the idiot.
As I learned that Howard’s list was just a safe way to get me started and not
the Magna Carta scrawled in crayon on a napkin, I developed an exercise to
practice and reinforce universalism. When I had the impulse to dismiss someone
as a bad player, I made myself find something that they did well. It was an
exercise I could do for myself, and I could get help from my group in analyzing
the strategies I thought those players might be executing well. That commitment
led to many benefits.
Of course, I learned some new and profitable strategies and tactics. I also
developed a more complete picture of other players’ strategies. Even when I
determined that the strategic choices of that player weren’t, in the end,
profitable, I had a deeper understanding of my opponent’s game, which helped
me devise counter-strategies. I had started thinking more deeply about the way
my opponents thought. And in some instances, I recognized that I had
underestimated the skills of certain players who I initially thought were
profitable for me to play against. That led me to make more objective decisions
about game selection. And my poker group benefited from this exercise as well
because, in workshopping the strategies with each other, we multiplied the
number of playing techniques we could observe and discuss. Admitting that the
people I played against had things to teach me was hard, and my group helped
me feel proud of myself when I resisted the urge to just complain about how
lucky my opponents were.
Nearly any group can create an exercise to develop and reinforce the open-
mindedness universalism requires. As an example, with politics so polarized, we
forget the obvious truth that no one has only good ideas or only bad ideas.
Liberals would do well to take some time to read and watch more conservative
news sources, and conservatives would do well to take some time to
read and
watch more liberal news sources—not with the goal of confirming that the other
side is a collection of idiots who have nothing of value to say but to specifically

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and purposely find things they
agree
with. When we do this, we learn things we
wouldn’t otherwise have learned. Our views will likely become moderated in the
same way judges from opposite sides of the political aisle moderate each other.
Even if, in the end, we don’t find much to agree with, we will understand the
opposing position better—and ours as well. We’ll be practicing what John Stuart
Mill preached.
Another way to disentangle the message from the messenger is to imagine
the message coming from a source we value much more or much less. If we hear
an account from someone we like, imagine if someone we didn’t like told us the
same story, and vice versa. This can be incorporated into an exploratory group’s
work, asking each other, “How would we feel about this if we heard it from a
much different source?” We can take this process of vetting information in the
group further, initially and intentionally omitting where or whom we heard the
idea from. Leading off our story by identifying the messenger could interfere
with the group’s commitment to universalism, biasing them to agree with or
discredit the message depending on their opinion of the messenger. So leave the
source out to start, giving the group the maximum opportunity to form an
impression without shooting (or celebrating) the message based on their opinion
of the messenger (separate from the expertise and credibility of the messenger).
John Stuart Mill made it clear that the
only
way to gain knowledge and
approach truth is by examining every variety of opinion. We learn things we
didn’t know. We calibrate better. Even when the result of that examination
confirms our initial
position, we understand that position better if we open
ourselves to every side of the issue. That requires open-mindedness to the
messages that come from places we don’t like.
Disinterestedness: we all have a conflict of interest,
and it’s contagious
Back in the 1960s, the scientific community was at odds about whether sugar or
fat was the culprit in the increasing rates of heart disease. In 1967, three Harvard
scientists conducted a comprehensive review of the research to date, published in
the
New England Journal of Medicine
, that firmly pointed the finger at fat as the
culprit. The paper was, not surprisingly, influential in the debate on diet and
heart disease. After all, the
NEJM
is and was a prestigious publication and the

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researchers were, all three, from Harvard. Blaming fat and exonerating sugar
affected the diets of hundreds of millions of people for decades, a belief that
caused a shift in eating habits that has been linked to the massive increase in
obesity rates and diabetes.
The influence of this paper and its negative effects on America’s eating
habits and health provides a stunning demonstration of the imperative of
disinterestedness. It was recently discovered that a trade group representing the
sugar industry had paid the three Harvard scientists to write the paper, according
to an article published in
JAMA Internal Medicine
in September 2016. Not
surprisingly, consistent with the agenda of the sugar industry that had paid them,
the researchers attacked the methodology of studies finding a link between sugar
and heart disease and defended studies finding no link. The scientists’ attacks on
and defenses of the
methodology of studies on fat and heart disease followed the
same pro-sugar pattern.
The scientists involved are all dead. Were they alive, it’s possible, if we
could ask them, that they may not have even consciously known they were being
influenced. Given human nature, they likely, at least, would have defended the
truth of what they wrote and denied that the sugar industry dictated or influenced
their thinking on the subject. Regardless, had the conflict of interest been
disclosed, the scientific community would have viewed their conclusions with
much more skepticism, taking into account the possibility of bias due to the
researchers’ financial interest. At the time, the
NEJM
did not require such
disclosures. (That policy changed in 1984.) That omission prevented an accurate
assessment of the findings, resulting in serious harm to the health of the nation.
We tend to think about conflicts of interest in the financial sense, like the
researchers getting paid by the sugar industry. But conflicts of interest come in
many flavors. Our brains have built-in conflicts of interest, interpreting the
world around us to confirm our beliefs, to avoid having to admit ignorance or
error, to take credit for good results following our decisions, to find reasons bad
results following our decisions were due to factors outside our control, to
compare well with our peers, and to live in a world where the way things turn
out makes sense. We are not naturally disinterested. We don’t process
information independent of the way we wish the world to be.
Remember the thought experiment I suggested at the beginning of the book
about what the headlines would have looked like if Pete Carroll’s pass call had
won the 2015 Super Bowl? Those headlines would have been about his
brilliance. People would have analyzed Carroll’s decision differently. Knowing

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how something
turned out creates a conflict of interest that expresses itself as
resulting.
Richard Feynman recognized that in physics—a branch of science that most
of us consider as objective as 2 + 2 = 4—there is still demonstrable outcome
bias. He found that if those analyzing data knew, or could even just intuit, the
hypothesis being tested, the analysis would be more likely to support the
hypothesis being tested. The measurements might be objective, but slicing and
dicing the data is vulnerable to bias, even unconsciously. According to Robert
MacCoun and physics Nobel laureate Saul Perlmutter in a 2015
Nature
article,
outcome-blind analysis has spread to several areas of particle physics and
cosmology, where it “is often considered the only way to trust many results.”
Because the idea—introducing a random variable so that those analyzing the
data could not surmise the outcome the researcher might be hoping for—is
hardly known in biological, psychological, and social sciences, the authors
concluded these methods “might improve trust and integrity in many sciences,
including those with high-stakes analyses that are easily plagued by bias.”
Outcome blindness enforces disinterestedness.
We can apply this idea of outcome blindness to the way we communicate
information as we workshop decisions about our far more ambiguous pursuits—
like describing a poker hand, or a family argument, or the results of a market test
for a new product. If the group is going to help us make and evaluate decisions
in an unbiased way, we don’t want to infect them in the way the data analysts
were infected if they could surmise the hypothesis being tested. Telling someone
how a story ends encourages them to be resulters, to interpret the details to fit
that outcome. If I won a hand, it was more likely my group would assess my
strategy as good. If I lost, the reverse would be true. Win a case at trial,
the
strategy is brilliant. Lose, and mistakes were made. We treat outcomes as good
signals for decision quality, as if we were playing chess. If the outcome is
known, it will bias the assessment of the decision quality to align with the
outcome quality.
If the group is blind to the outcome, it produces higher fidelity evaluation of
decision quality. The best way to do this is to deconstruct decisions before an
outcome is known. Attorneys can evaluate trial strategy before the verdict comes
in. Sales teams can evaluate strategy before learning whether they’ve closed the
sale. Traders can vet process prior to positions being established or prior to
options expiring. After the outcome, make it a habit when seeking advice to give
the details without revealing the outcome. In poker, it isn’t practical to analyze
hands before knowing how they turn out since the results come within seconds

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hands before knowing how they turn out since the results come within seconds
of the decisions. To address this, many expert poker players often omit the
outcome when seeking advice about their play.
This became such a natural habit that I didn’t realize, until I started
conducting poker seminars for players newer to the game, that this was not the
norm for everyone. When I used hands I had played as illustrations, I would
describe the hand up to the decision point I was discussing and no further,
leaving off how the hand ended. This was, after all, how I had been trained by
my poker group. When we finished the discussion, it was jarring to watch a
roomful of people look at me like I had left them teetering on the edge of a cliff.
“Wait! How did the hand turn out?”
I gave them the red pill: “It doesn’t matter.”
Of course, we don’t have to be describing a poker hand to use this strategy
to promote disinterestedness. Anyone can provide the narrative only up to the
point of the decision under consideration, leaving off the outcome so as not to
infect their listeners
with bias. And outcomes aren’t the only problem. Beliefs
are also contagious. If our listeners know what we believe to be true, they will
likely work pretty hard to justify our beliefs, often without even knowing they
are doing it. They will develop an ideological conflict of interest created by our
informing our listeners of our beliefs. So when trying to vet some piece of
information, some fact or opinion, we would do well to shield our listeners from
what our opinion is as we seek the group’s opinion.
Simply put, the group is less likely to succumb to ideological conflicts of
interest when they don’t know what the interest is. That’s MacCoun and
Perlmutter’s point.
Another way a group can de-bias members is to reward them for skill in
debating opposing points of view and finding merit in opposing positions. When
members of the group disagree, a debate may be of only marginal value because
the people debating are biased toward confirming their position, often creating
stalemate. If two people disagree, a referee can get them to each argue the
other’s position with the goal of being the best debater. This acts to shift the
interest to open-mindedness to the opposing opinion rather than confirmation of
their original position. They can’t win the debate if they can’t forcefully and
credibly argue the other side. The key is for the group to have a charter that
rewards objective consideration of alternative hypotheses so that winning the
debate feels better than supporting the pre-existing position. The group’s
reinforcement ought to discourage us from creating a straw-man argument when
we’re arguing against our beliefs, and encourage us to feel good about winning
the debate. This is one of the reasons it’s good for a group to have at least three

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the debate. This is one of the reasons it’s good for a group to have at least three
members, two to disagree and one to referee.
What I’ve generally found is that two people whose positions
on an issue are
far apart will move toward the middle after a debate or skilled explanation of the
opposing position. Engaging in this type of exchange creates an understanding of
and appreciation for other points of view much deeper and more powerful than
just listening to the other perspective. Ultimately, it gives us a deeper
understanding of our own position. Once again, we are reminded of John Stuart
Mill’s assertion that this kind of open-mindedness is the
only
way to learn.
Organized skepticism: real skeptics make
arguments and friends
Skepticism gets a bum rap because it tends to be associated with negative
character traits. Someone who disagrees could be considered “disagreeable.”
Someone who dissents may be creating “dissention.” Maybe part of it is that
“skeptical” sounds like “cynical.” Yet true skepticism is consistent with good
manners, civil discourse, and friendly communications.
Skepticism is about approaching the world by asking why things might not
be true rather than why they are true. It’s a recognition that, while there is an
objective truth, everything we believe about the world is not true. Thinking in
bets embodies skepticism by encouraging us to examine what we do and don’t
know and what our level of confidence is in our beliefs and predictions. This
moves us closer to what is objectively true.
A productive decision group would do well to organize around skepticism.
That should also be its communications guide, because true skepticism isn’t
confrontational. Thinking in bets demands
the imperative of skepticism. Without
embracing uncertainty, we can’t rationally bet on our beliefs. And we need to be
particularly skeptical of information that agrees with us because we know that
we are biased to just accept and applaud confirming evidence. If we don’t “lean
over backwards” (as Richard Feynman famously said) to figure out where we
could be wrong, we are going to make some pretty bad bets.
If we embrace uncertainty and wrap that into the way we communicate with
the group, confrontational dissent evaporates because we start from a place of
not being sure. Just as we can wrap our uncertainty into the way we express our

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beliefs (“I’m 60% sure the waiter is going to mess up my order”), when we
implement the norm of skepticism, we naturally modulate expression of dissent
with others. Expression of disagreement is, after all, just another way to express
our own beliefs, which we acknowledge are probabilistic in nature. Therefore,
overtly expressing the uncertainty in a dissenting belief follows. No longer do
we dissent with declarations of “You’re wrong!” Rather, we engage by saying,
“I’m not sure about that.” Or even just ask, “Are
you
sure about that?” or “Have
you considered this other way of thinking about it?” We engage this way simply
because that is faithful to uncertainty. Organized skepticism invites people into a
cooperative exploration. People are more open to hearing differing perspectives
expressed this way.
Skepticism should be encouraged and, where possible, operationalized. The
term “devil’s advocate” developed centuries ago from the Catholic Church’s
practice, during the canonization process, of hiring someone to present
arguments
against
sainthood. Just as the CIA has red teams and the State
Department has its Dissent Channel, we can incorporate dissent into our business
and personal lives. We can create a pod whose job (literally,
in business, or
figuratively, in our personal life) is to present the other side, to argue why a
strategy might be ill-advised, why a prediction might be off, or why an idea
might be ill informed. In so doing, the red team naturally raises alternate
hypotheses. Likewise, companies can implement an anonymous dissent channel,
giving any employee, from the mail room to the boardroom, a venue to express
dissenting opinions, alternative strategies, novel ideas, and points of view that
may disagree with the prevailing viewpoint of the company without fear of
repercussions. The company should do its best to reward this constructive
dissent by taking the suggestions seriously or the expression of diverse
viewpoints won’t be reinforced.
Less formally, look for opportunities to recruit a devil’s advocate on an ad
hoc basis. When seeking advice, we can ask specific questions to encourage the
other person to figure out reasons why we might be wrong. That way, they won’t
be as reticent to challenge the action we want to pursue; we’re
asking
for it, so
it’s not oppositional for them to disagree or give us advice contrary to what they
think we want to hear.
Make no mistake: the process of seeing ourselves and the world more
accurately and objectively is hard and makes us think about things we generally
avoid. The group needs rules of engagement that don’t make this harder by
letting members get away with being nasty or dismissive. And we need to be
aware that even a softer serve of dissent to those who have not agreed to the

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aware that even a softer serve of dissent to those who have not agreed to the
truthseeking charter can be perceived as confrontational. See David Letterman
for details.
Communicating with the world beyond our group
This chapter has focused primarily on forming truthseeking groups on our own
initiative or being part of such groups. Unless we have control over the culture
around us,
*
those of us more actively seeking dissent will generally be in the
minority when we are away from our group. That doesn’t mean that truthseeking
is off-limits in those settings. It just means we have to take the most
constructive, civil elements of truthseeking communication and introduce them
carefully. There are several ways to communicate to maximize our ability to
engage in a truthseeking way with anyone.
First, express uncertainty. Uncertainty not only improves truthseeking
within groups but also invites everyone around us to share helpful information
and dissenting opinions. Fear of being wrong (or of having to suggest someone
else is wrong) countervails the social contract of confirmation, often causing
people to withhold valuable insights and opinions from us. If we start by making
clear our own uncertainty, our audience is more likely to understand that any
discussion that follows will not involve right
versus wrong, maximizing our
truthseeking exchanges with those outside our chartered group.
Second, lead with assent. For example, listen for the things you agree with,
state those and be specific, and then follow with “and” instead of “but.” If there
is one thing we have learned thus far it is that we like having our ideas affirmed.
If we want to engage someone with whom we have some disagreement (inside
or outside our group), they will be more open and less defensive if we start with
those areas of agreement, which there surely will be. It is rare that we disagree
with everything that someone has to say. By putting into practice the strategies
that promote universalism, actively looking for the ideas that we agree with, we
will more naturally engage people in the process of learning with us. We will
also be more open-minded to what others have to say as well, enhancing our
ability to calibrate our own beliefs.
When we lead with assent, our listeners will be more open to any dissent
that might follow. In addition, when the new information is presented as
supplementing
rather than
negating
what has come before, our listeners will be

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much more open to what we have to say. The simplest rhetorical touches can
make a difference. If someone expresses a belief or prediction that doesn’t sound
well calibrated and we have relevant information, try to say
and
, as in, “I agree
with you that [insert specific concepts and ideas we agree with], AND . . .” After
“and,” add the additional information. In the same exchange, if we said, “I agree
with you that [insert specific concepts and ideas you agree with], BUT . . . ,” that
challenge puts people on the defensive. “And” is an offer to contribute. “But” is
a denial and repudiation of what came before.
We can think of this broadly as an attempt to avoid the language of “no.” In
the performance art of improvisation, the first advice is that when someone starts
a scene, you should respond
with “yes, and . . .” “Yes” means you are accepting
the construct of the situation. “And” means you are adding to it. That’s an
excellent guideline in any situation in which you want to encourage exploratory
thought. The important thing is to try to find areas of agreement to maintain the
spirit of partnership in seeking the truth. In expressing potentially contradictory
or dissenting information, our language ideally minimizes the element of
disagreement.
Third, ask for a temporary agreement to engage in truthseeking. If someone
is off-loading emotion to us, we can ask them if they are just looking to vent or if
they are looking for advice. If they aren’t looking for advice, that’s fine. The
rules of engagement have been made clear. Sometimes, people just want to vent.
I certainly do. It’s in our nature. We want to be supportive of the people around
us, and that includes comforting them when they just need some understanding
and sympathy. But sometimes they’ll say they are looking for advice, and that is
potentially an agreement to opt in to some truthseeking. (Even then, tread lightly
because people may say they want advice when what they really want is to be
affirmed.)
This type of temporary agreement is really just a reverse version of the kind
of temporary opting out that we did in my poker group when someone just had
to vent about an especially intense, still-raw loss. Flipping that on its head, it
doesn’t have to be offensive to ask, “Do you want to just let it all out, or are you
thinking of what to do about it next?”
Finally, focus on the future. As I said at the beginning of this book, we are
generally pretty good at identifying the positive goals we are striving for; our
problem is in the execution of the decisions along the way to reaching those
goals. People dislike engaging with their poor execution. That requires taking
responsibility for what is often a bad outcome, which, as David Letterman found

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out, will shut down the conversation. Rather than rehashing what has already
happened, try instead to engage about what the person might do so that things
will turn out better going forward. Whether it’s our kids, other family members,
friends, relationship partners, coworkers, or even ourselves, we share the
common trait of generally being more rational about the future than the past. It’s
harder to get defensive about something that hasn’t happened yet.
Imagine if David Letterman had said, “It’s too bad you have all these kooky
people creating all that drama in your life. Have you thought about how you
might get rid of all this drama in the future?” If Lauren Conrad had said
something “dramatic,” like “I’ve got so many problems I can’t even think about
the future,” or “I’m stuck with these people so there’s nothing I can do about it,”
that obviously would be a good time to end the discussion. But the more likely
result is that she would have engaged. And that focus on the future could get her
to circle back to figure out why all the drama occurred; she wouldn’t be able to
sensibly answer the question about the future without examining the past. When
we validate the other person’s experience of the past and refocus on exploration
of the future, they can get to their past decisions on their own.
This is a good approach to communicating with our children, who, with
their developing egos, don’t necessarily need a red pill shoved down their
throats. A child isn’t equipped to consent to the challenges of truthseeking
exchanges. But they can be nudged. I know, in
The

Matrix
, Morpheus took Neo
to visit the Oracle and, while waiting in the lobby, he saw children bending
spoons with their minds and engaging in other precocious
red-pill behavior. But
real-life kids are sensitive to feeling judged. And no real-life parent wants a kid
with the ability to mentally send cutlery flying across the room.
My son was expert at fielding bad test scores as the teacher’s fault. I had to
be careful not to Letterman him. Instead, I would tell him, “It must be hard to
have a teacher like that. Do you think there’s anything you can do to improve
your grade in the future?” That at once provided validation and led to productive
discussions about subjects like developing strategies for preparing for future
tests and setting up meetings with the teacher to figure out what the teacher was
looking for in assignments. Meeting with the teacher also created a good
impression that would likely be reflected in future grades. Ultimately, even with
our own kids’ decisions, rehashing outcomes can create defensiveness. The
future, on the other hand, can always be better if we can get them to focus on
things in their control.
These methods of communicating with people outside our truthseeking
group focus on future goals and future actions. When it works, they take a short

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group focus on future goals and future actions. When it works, they take a short
trip into the future, beyond the frustrations of the present and toward ways to
improve things they can control. Accountability to a truthseeking group is also,
in some ways, a time-travel portal. Because we know we will have to answer to
the group, we start thinking in advance of how that will go. Anticipating and
rehearsing those rational discussions can improve our initial decision-making
and analysis, at a time when we might not otherwise be so rational.
That leads to the final decision strategy of this book: ways to use time-travel
techniques for better decision-making. By recruiting past and future versions of
yourself, you can become your own buddy.

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CHAPTER 6
Adventures in Mental Time Travel
Let Marty McFly run into Marty McFly
Thanks to the success of the three Back to the Future movies, our go-to source
on the rules of time travel is more likely to be Doc Brown than Dr. Stephen
Hawking. The first rule, emphasized by the trilogy and repeated by nearly every
time-travel movie since, is “Whatever you do, don’t meet up with yourself!” Doc
Brown (Christopher Lloyd) explains to Marty McFly (Michael J. Fox) in
Back to
the Future: Part II
(1989) that “the encounter could create a time paradox, the
results of which could cause a chain reaction that would unravel the very fabric
of the space-time continuum and destroy the entire universe. Granted, that’s the
worst-case scenario. The destruction might, in fact, be very localized, limited to
merely our own galaxy.”
“Don’t meet up with yourself” has become an unquestioned element of the
“science” of time travel. In
Timecop
(1994), because “the same matter can’t
occupy the same space at the same time,”
Jean-Claude Van Damme’s character
destroys the villain by pushing his past and future versions together. The villain
turns into a liquefied blob and disappears from existence.
In real-life decision-making, when we bring our past-or future-self into the
equation, the space-time continuum doesn’t unravel. Far from turning us into a
liquefied blob, a visit from past or future versions of us helps present-us make
better bets. When making decisions, isolating ourselves from thinking about
similar decisions in the past and possible future consequences is frequently the
very thing that turns us into a blob, mired by in-the-moment thinking where the
scope of time is distorted. As decision-makers, we
want
to collide with past and
future versions of ourselves. Our capacity for mental time travel makes this

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possible. As is the case with accountability, such meetings can lead to better
decisions: at the moment of the decision, accountability to our group can pop us
briefly into the future to imagine the conversation about the decision we will
have with that group. Running that conversation will often remind us to stay on a
more rational path.
Just as we can recruit other people to be our decision buddies, we can recruit
other versions of ourselves to act as our own decision buddies. We can harness
the power of mental time traveling, operationalizing it, encouraging it, and
figuring out ways to cause that collision of past, present, and future as much as
possible. Present-us needs that help, and past-us and future-us can be the best
decision buddies for the job.
*
Poker players have unique decision challenges that get them thinking a lot
about how to get this collision of past-, present-and future-self to occur at the
moment of making a decision and executing on it. Because decisions in poker
are made so quickly, players don’t have the luxury of time in coordinating their
rational, long-term, strategic plans with their decisions at the poker table. And all
those decisions made under severe time constraints have immediate
consequences expressed as an exchange of poker chips. The constant exchange
of chips reminds players that there is risk in every decision. Of course, the
direction in which the chips flow in the short term only loosely correlates with
decision quality. You can win a hand after making bad decisions and lose a hand
after making good ones. But the mere fact that chips are changing hands is a
reminder that every decision has consequences—that all those execution
decisions you make along the way really matter.
Away from the poker table, we don’t feel or experience the consequences of
most of the decisions we make right away. If we are winning or losing to a
particular decision, the consequences may take time to reveal themselves. If we
make a losing eating decision, like substituting SnackWell’s for apples, there’s
no immediate outcome that lets us know there might have been a cost to that
choice. If we repeat that kind of decision enough, there will be consequences,
but they take time to play out. In business, if a leader ignores the ideas of an
intern because “What does an intern possibly know?” it could take years for that
intern to
become a successful competitor before that mistake becomes obvious.
If the trajectory of that business suffers because of the poverty of new ideas, the
owner of the business might never realize the effect of that attitude.
The best poker players develop practical ways to incorporate their long-term
strategic goals into their in-the-moment decisions. The rest of this chapter is

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devoted to many of these strategies designed to recruit past-and future-us to help
with all the execution decisions we have to make to reach our long-term goals.
As with all the strategies in this book, we must recognize that no strategy can
turn us into perfectly rational actors. In addition, we can make the best possible
decisions and still not get the result we want. Improving decision quality is about
increasing
our chances of good outcomes, not
guaranteeing
them. Even when
that effort makes a small difference—more rational thinking and fewer
emotional decisions, translated into an increased probability of better outcomes
—it can have a significant impact on how our lives turn out. Good results
compound. Good processes become habits, and make possible future calibration
and improvement.
Those methods involve a lot of mental time travel, and those poker players
could teach Marty McFly and Doc Brown a thing or two.
Night Jerry
For all the scientific research on the battle between our immediate desires and
long-term goals, a particularly succinct explanation comes from Jerry Seinfeld,
on why he doesn’t get enough
sleep: “I stay up late at night because I’m Night
Guy. Night Guy wants to stay up late. ‘What about getting up after five hours of
sleep?’ ‘That’s Morning Guy’s problem. That’s not my problem. I’m Night Guy.
I stay up as late as I want.’ So you get up in the morning: you’re exhausted,
you’re groggy. ‘Oooh, I hate that Night Guy.’ See, Night Guy always screws
Morning Guy.”
That’s a good example of how we struggle in the present to take care of our
future-self. Night Jerry is always going to want to stay up late and, if Morning
Jerry has no say in the decision, Night Jerry will get his way regardless of what’s
in Jerry’s longer-term best interest. When we make in-the-moment decisions
(and don’t ponder the past or future), we are more likely to be irrational and
impulsive.
*
This tendency we all have to favor our present-self at the expense of our
future-self is called temporal discounting.
*
We
are willing to take an irrationally
large discount to get a reward now instead of waiting for a bigger reward later.
An example of temporal discounting among adults includes a study from the
military drawdown in the 1990s that led tens of thousands of military employees

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to choose lump-sum retirement payments at drastically discounted rates instead
of guaranteed annuity payments. The men and women of the U.S. military took
lump-sum payments worth $2.5 billion, a 40% discount compared to the present
value of the annuity payments they would have received. (For additional sources
on temporal discounting, see the Selected Bibliography and Recommendations
for Further Reading.)
When Night Jerry stays up late, it’s because it benefits him
now
; he
discounts the benefits that come later from going to bed. Saving for retirement is
a temporal discounting problem: the gratification of spending discretionary
income is immediate. Putting it away for retirement means we have to wait
decades to get enjoyment from that money. We are built for temporal
discounting, for using the resources that are available to us now as
opposed to
saving them for a future version of us that we aren’t particularly in touch with in
the moment of the decision. Time traveling can get us in touch with that future
version of us. It can get future-us to remind present-us, “Hey, don’t discount!”
Or at least, “Don’t discount so much!”
When we think about the past and the future, we engage deliberative mind,
improving our ability to make a more rational decision. When we imagine the
future, we don’t just make it up out of whole cloth, inventing a future based on
nothing that we have ever seen or experienced. Our vision of the future, rather, is
rooted in our memories of the past. The future we imagine is a novel
reassembling of our past experiences. Given that, it shouldn’t be surprising that
the same neural network is engaged when we imagine the future as when we
remember the past. Thinking about the future is
remembering
the future, putting
memories together in a creative way to imagine a possible way things might turn
out. Those brain pathways include the hippocampus (a key structure for
memory) and the prefrontal cortex, which controls System 2, deliberative
decision-making. It is our cognitive control center.
*
By engaging those
pathways, Night Jerry can access memories like oversleeping and missing
appointments or dozing off during morning meetings that he can use to imagine
how tired Morning Jerry will be or what’s going to happen to Morning Jerry’s
schedule when he doesn’t want to get up or how his day will go when he can’t
pay attention.
Wouldn’t it be great if Morning Jerry could travel back in
time and tap
Night Jerry on the shoulder to tell him to go to bed? As it turns out, there’s an
app for that.
From advancements in photo techniques and virtual reality, there is software
that can show you a prediction of what you will look like decades into the future.

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that can show you a prediction of what you will look like decades into the future.
If you feel, like most adults, bad about seeing your parents age, these images of
future-you can be unsettling, like looking into a sadist-designed funhouse mirror.
Fortunately, they’ve found ways to put this age-progression technology to more
productive uses than just making us stare into the void of our own mortality.
Saving for retirement is a Night Jerry–versus–Morning Jerry problem. If
Night Jerry isn’t even thinking ahead to tomorrow morning, he’s certainly not
thinking ahead several decades to retirement. Retirement planning involves a
series of decisions in which our present-self can act to the detriment or benefit of
our future-self. When we set retirement goals, we are necessarily thinking about
our future-self’s goals—how much we need to save for that older version of us
to live comfortably. Our spending decisions, however, don’t seem to be
particularly focused on what’s best for our seventy-year-old-self. In fact, a quick
Google search on the topic quickly reveals that our retirement savings are
dangerously low. According to one study by the Center for Retirement Research
at Boston College, “roughly half of today’s working households will not be able
to maintain their standard of living in retirement.” Depending on which estimate
you read, the shortfall could be $6.8 to $14
trillion
.
Several organizations and companies with an interest in encouraging
retirement planning have resources that allow clients to “meet” their future-
selves as they make retirement decisions. In the simplest versions of these tools,
clients plug in their age, income, savings practices, and retirement goals. The
apps then
show the client the financial situation and lifestyle their future-self can
expect, compared with the present.
Prudential Retirement, AARP, and others have versions of these apps that
emphasize the consequences of retirement planning by
visually
introducing us to
our future-self. Bank of America Merrill Lynch in 2012 (for web-access
computing) and 2014 (for mobile devices) introduced Merrill Edge, which
includes a tool called “Face Retirement.” Customers upload a picture of
themselves and get to see, according to the press release, “a life-like 3D
animation of their future self, enabling them to envision every wrinkle they
could encounter at retirement age—and beyond.” Night Jerry gets a glimpse of
what Morning Jerry looks like without enough sleep to function.
This idea that seeing our aged future-self could help us make better
allocation decisions is based, in part, on research by Jeremy Bailenson and Laura
Carstensen of Stanford University’s Freeman Spogli Institute for International
Studies. They used immersive virtual-reality technology in a lab setting to
demonstrate how a visit from Morning Jerry will help Night Jerry make better
decisions. Subjects entered a virtual-reality environment, after which they were

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decisions. Subjects entered a virtual-reality environment, after which they were
asked to allocate $1,000 among accounts for various purposes, one of which was
a hypothetical retirement account. Subjects seeing a digital representation of
their present-self in the mirror allocated on average $73.90 to the retirement
account. Other subjects looking in that mirror saw an age-progressed version of
themselves. This latter group of subjects, on average, allocated $178.10 to the
retirement account. This is a startling example of how future-us can act as an
effective decision buddy for present-us.
Bringing our future-self into the decision gets us started thinking about the
future consequences of those in-the-moment
decisions. Fundamentally, Morning
Jerry and Night Jerry are living the same life, and getting Morning Jerry into
Night Jerry’s face will remind him of that. Seeing our aged-self in the mirror,
along with a spreadsheet showing us how future-us has to struggle to get by, is a
persuasive reminder to put aside some discretionary spending money for
retirement. It’s that tap on the shoulder from our future-self. “Hey, don’t forget
about me. I’m going to exist and I’d like you to please take that into account.”
We’re not perfectly rational when we ponder the past or the future and
engage deliberative mind, but we are more likely to make choices consistent
with our long-term goals when we can get out of the moment and engage our
past-and future-selves. We want Night Jerry and Morning Jerry colliding on the
decision of when to get some sleep. We want all those Marty McFlys to get the
additional perspective of all the other Marty McFlys. And we want our aged,
wrinkly self colliding with us when we decide between spending more money
now on something like a nicer car versus saving more money for retirement.
Moving regret in front of our decisions
Philosophers agree that regret is one of the most intense emotions we feel, but
they have argued about whether it is productive or useful. Nietzsche said that
remorse was “adding to the first act of stupidity a second.” Thoreau, on the other
hand, praised the power of regret: “Make the most of your regrets; never smother
your sorrow, but tend and cherish it till it comes to have a separate and integral
interest. To regret deeply is to live afresh.”
The problem isn’t so much whether regret is an
unproductive emotion. It’s
that regret occurs after the fact, instead of before. As Nietzsche points out, regret

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can do nothing to change what has already happened. We just wallow in remorse
about something over which we no longer have any control. But if regret
occurred before a decision instead of after, the experience of regret might get us
to change a choice likely to result in a bad outcome. Then we could embrace
Thoreau’s view and harness the power of regret because it would serve a
valuable purpose. It would be helpful, then, if we could get regret to do some
time traveling of its own, moving before our decisions instead of after them.
That way, regret might be able to keep us from making a bad bet. In addition, it
wouldn’t, as Nietzsche implied, rear its head later by causing us to make a
remorse-fueled second mistake.
Morning Jerry regretted Night Jerry’s decision to stay up late, but it was too
late for him to do anything about it. When we see the retirement-savings
shortfall in this country, there is no doubt that many of the future, retired
versions of us are going to regret the financial allocation decisions that younger-
us made, after it is too late to fix it. The age-progression imaging works to
address this issue of regret occurring when it’s already too late. By giving us a
look at the retirement-aged version of ourselves, it gives us a chance to
experience some regret that we didn’t plan adequately for retirement
before
we’ve made inadequate plans. That was one of the purposes of my loss limit in
poker. Because of the loss-limit agreement I had made with myself and my
group, I ran the conversation in my head that I’d be forced to have when I
explained why I kept playing beyond my limit. It gave me a chance to regret the
decision before I bought more chips.
One of our time-travel goals is to create moments like that, where we can
interrupt an in-the-moment decision and take some time to consider the decision
from the perspective of our
past and future. We can then create a habit routine
around these decision interrupts to encourage this perspective taking, asking
ourselves a set of simple questions at the moment of the decision designed to get
future-us and past-us involved. We can do this by imagining how future-us is
likely to feel about the decision or by imagining how we might feel about the
decision today if past-us had made it. The approaches are complementary;
whether you choose to travel to the past or travel to the future depends solely on
what approach you find most effective.
Business journalist and author Suzy Welch developed a popular tool known
as 10-10-10 that has the effect of bringing future-us into more of our in-the-
moment decisions. “Every 10-10-10 process starts with a question. . . . [W]hat
are the consequences of each of my options in ten minutes? In ten months? In

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ten years?” This set of questions triggers mental time travel that cues that
accountability conversation (also encouraged by a truthseeking decision group).
We can build on Welch’s tool by asking the questions through the frame of the
past: “How would I feel today if I had made this decision ten minutes ago? Ten
months ago? Ten years ago?” Whichever frame we choose, we draw on our past
experiences (including similar decisions we may have regretted) in answering
the questions, recruiting into the decision those less-reactive brain pathways that
control executive functioning.
In poker, because the decisions are all made in the moment and the
consequences are big and immediate, routines like 10-10-10 are a survival skill. I
recognized in poker that in the same way that I was not the best judge of how I
was playing after losing a certain amount of money, I was also not the best judge
of the quality of my poker after about six to eight hours of play. Just as we can
convince ourselves we are sober enough to drive,
it is easy for poker players to
convince themselves that they are alert enough to keep playing after many hours
of intense, intellectually taxing work. In my more rational moments, away from
the tables, I knew I would be better off if I played just six to eight hours per
session. When I reached that point in a session and considered continuing past
that time limit, I could use a 10-10-10-like strategy to recruit my past-and future-
self: How have I felt when I kept playing in the past? How has it generally
worked out? When I look back, do I feel I was playing my best? This routine of
asking myself these questions helped mitigate the in-the-moment risk that, as I
was losing my mental edge, I might try to convince myself that the game was so
great that I had to keep playing.
Moving regret in front of a decision has numerous benefits. First, obviously,
it can influence us to make a better decision. Second, it helps us treat ourselves
(regardless of the actual decision) more compassionately after the fact. We can
anticipate and prepare for negative outcomes. By planning ahead, we can devise
a plan to respond to a negative outcome instead of just reacting to it. We can also
familiarize ourselves with the likelihood of a negative outcome and how it will
feel. Coming to peace with a bad outcome in advance will feel better than
refusing to acknowledge it, facing it only after it has happened.
After-the-fact regret can consume us. Like all emotions, regret initially feels
intense but gets better with time. Time-travel strategies can help us remember
that the intensity of what we feel now will subside over time. And that helps
reduce the emotion we feel in the moment, making it less likely that we will
prove Nietzsche right and add a second act of stupidity to the first.

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A flat tire, the ticker, and a zoom lens
Imagine you are standing on a narrow strip of concrete on the shoulder of the
highway. Behind you is your car, hazard lights flashing. The rear tire on the
driver’s side is shredded. It is fully dark and the drizzle has turned into a cold,
heavy downpour. You’ve called roadside assistance, twice, and both times (after
long hold times) spoken with operators who’ve told you someone will arrive “as
soon as they get there after responding to your call.” You decide to change the
tire yourself, only to discover you have no jack. You’re soaked to the skin and
cold.
How does it feel
?
It likely feels like the worst moment of your life. You are
likely bemoaning how unlucky you are, wondering why these things always
happen to you. You are miserable and you can’t imagine feeling any other way.
*
That’s how it feels in the moment. But if the flat tire had happened a year
ago, do you think it would have an effect on your happiness today, or your
overall happiness over the past year? Not likely. It likely wouldn’t cause your
overall happiness to tick
up or down. It would probably have faded to a funny
story (or a story you try to make sound funny) told at cocktail parties.
In our decision-making lives, we aren’t that good at taking this kind of
perspective—at accessing the past and future to get a better view of how any
given moment might fit into the scope of time. It just feels how it feels in the
moment and we react to it. We want to create opportunities to take the broader
perspective prior to making decisions driven by the magnified feelings we have
in the moment. A 10-10-10 strategy does that, getting us to imagine the decision
or outcome in the perspective of the past and the future.
The flat tire isn’t as awful as it seems in the moment. This kind of time-
travel strategy calms down the in-the-moment emotions we have about an event,
so we can get back to using the more rational part of our brain. Recruiting past-
us and future-us in this way activates the neural pathways that engage the
prefrontal cortex, inhibiting emotional mind and keeping events in more rational
perspective. This discourages us from magnifying the present moment, blowing
it out of proportion and overreacting to it.
This overestimation of the impact of any individual moment on our overall
happiness is the emotional equivalent of watching the ticker in the financial
world. We make a long-term stock investment because we want it to appreciate
over years or decades. Yet there we are, watching a downward tick over a few
minutes, consumed by imagining the worst. What’s the volume? Is it heavier
than usual? Better check the news stories. Better check the message boards to

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than usual? Better check the news stories. Better check the message boards to
find out what rumors are circulating.
A stock like Berkshire Hathaway reveals why ticker watching isn’t a
particularly productive endeavor when you are investing
for the long run. Look
at this chart of Berkshire’s performance since 1964:
Now zoom in on a random day in late January 2017. The upticks and
downticks look large and potentially frightening. You can imagine sitting at the
low point around 11:30, feeling like your losses are spiraling.

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If you zoomed in on the performance of Berkshire Hathaway stock during
the banking crisis, September 2008 to March 2009, you would feel terrible most
days
:

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Yet we know from the first chart, the big picture, that all those minute-to-
minute and even day-to-day changes had little effect on the investment’s general
upward trajectory.
Our problem is that we’re ticker watchers of our own lives. Happiness
(however we individually define it) is not best measured by looking at the ticker,
zooming in and magnifying moment-by-moment or day-by-day movements. We
would be better off thinking about our happiness as a long-term stock holding.
We would do well to view our happiness through a wide-angle lens, striving for
a long, sustaining upward trend in our happiness stock, so it resembles the first
Berkshire Hathaway chart.
Mental time travel makes that kind of perspective possible. We can use our
past-and future-selves to pull us out of the
moment and remind us when we’re
watching the ticker, looking at our lives through that lens on extreme zoom.
When we view these upticks and downticks under the magnification of that
in-the-moment zoom lens, our emotional responses are, similarly, amplified.
Like the flat tire in the rain, we are capable of treating things that will have little
effect on our long-term happiness as having significant impact. Our decision-
making becomes reactive, focused on off-loading negative emotions or
sustaining positive emotions from the latest change in the status quo. We can see
how this can result in self-serving bias: fielding outcomes to off-load the

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how this can result in self-serving bias: fielding outcomes to off-load the
negative emotions we feel in the moment from a bad outcome by blaming them
on luck and sustaining the positive emotions from good outcomes by taking
credit for them. The decisions driven by the emotions of the moment can become
a self-fulfilling prophecy, degrading the quality of the bets we make, increasing
the chances of bad outcomes, and making things worse.
“Yeah, but what have you done for me lately?”
Watching the ticker doesn’t just magnify what has happened in the very recent
past. It distorts our view of it as well. To understand the additional element of
distortion, the casino is a great place to look.
Imagine that you go to a casino for an evening of blackjack with your
friends. In the first half hour, you go on a winning streak and are ahead $1,000.
You keep playing because you and your friends are having such a good time. For
the next hour and a half, it seems like you never win a hand. You lose back the
$1,000 and break even for the night. How are you feeling about that?
Now imagine that you lose $1,000 in the first half hour and stick around
playing with your friends because they are having a great time. In the next hour
and a half you go on a winning streak that erases the early loss, and you end up
breaking even for the night. How are you feeling about that?
I’m guessing you are pretty sad and morose about starting off with the big
win, only to break even. In the second example, you’re probably so happy that
the drinks are on you. While you took a different path to get there, in both cases
you didn’t win or lose a dime at the end of the two hours. But in one case you
are really sad about the result and the other really happy.
As they say in the infomercial world, “But, wait! There’s more!”
Imagine you go up that same $1,000 in the first half hour but now, over the
next hour and a half, you can’t seem to win a hand and lose $900 back, ending
the night with a $100 win. How does that feel? Now imagine that you lost that
same $1,000 in the first half hour but then went on a winning streak to end the
night down only $100. How does that feel? Most likely, you’re pretty glum
about the $100 win but still buying drinks for everyone after recovering from
that terrible start to only lose $100. So you’re sad that you won $100 and happy
that you lost $100.

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The way we field outcomes is path dependent. It doesn’t so much matter
where we end up as how we got there. What has happened in the recent past
drives our emotional response much more than how we are doing overall. That’s
how we can win $100 and be sad, and lose $100 and be happy. The zoom lens
doesn’t just magnify, it distorts. This is true whether we are in a casino, making
investment decisions, in a relationship, or on the side of
the road with a flat tire.
If we got a big promotion last week and have a flat tire right now, we are cursing
our lives, complaining about how unlucky we are. Our feelings are not a reaction
to the average of how things are going. We feel sad if we are breaking even (or
winning) on an investment that used to be valued much higher. In relationships,
even small disagreements seem big in the midst of the disagreement. The
problem in all these situations (and countless others) is that our in-the-moment
emotions affect the quality of the decisions we make in those moments, and we
are very willing to make decisions when we are not emotionally fit to do so.
Now imagine if you had gone for that night of blackjack a year ago. When
you think about the outcomes as having happened in the distant past, it is likely
your preference for the results reverses, landing in a more rational place. You are
now happier about the $100 win than about the $100 loss. Once we pull
ourselves out of the moment through time-traveling exercises, we can see these
things in proportion to their size, free of the distortion caused by whether the
ticker just moved up or down.
This is a constant challenge in poker. While the moving scoreboard has the
upside of reminding players that all their decisions have consequences, there is
also a downside. The scoreboard, like a stock ticker, reflects the most recent
changes, creating a risk that players get caught up in ticker watching, responding
emotionally and disproportionately to momentary fluctuations. Poker players
think about this problem a lot.
Tilt
Surfers have more than twenty terms to describe different kinds of waves. The
reason is that the type of wave, the way it breaks, the direction it’s coming from,
the bottom depth, etc., create differing challenges for surfers. There are
closeouts
(waves that break all at once) and
double-ups
(a type of wave created when two
waves meet to form one wave) and
reforms
(a wave that will break, then die

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down, then break again). Non-surfers just call all of these “waves.” On rare
occasions when we non-surfers need to be more specific, we just add a lot of
extra words. Those extra words don’t cost us much because it doesn’t come up
very often—maybe never. But for people involved in specialized activities, it’s
worth it to be able to communicate a complex concept in a single word that
laypeople would need lengthy phrases to convey. Having a nuanced, precise
vocabulary is what jargon is all about. It’s why carpenters have at least a dozen
names for different kinds of nails, and in the field of neuro-oncology, there are
more than 120 types of brain and central nervous system tumors.
Because poker players are in a constant struggle to keep in-the-moment
fluctuations in perspective, their jargon has a variety of terms for the concept
that “bad outcomes can have an impact on your emotions that compromise your
decision-making going forward so that you make emotionally charged, irrational
decisions that are likely to result in more bad outcomes that will then negatively
impact your decision-making going forward and so on.” The most common is
tilt
. Tilt is the poker player’s worst enemy, and the word instantly communicates
to other poker players that you were emotionally unhinged in your decision-
making
because of the way things turned out.
*
If you blow some recent event
out of proportion and react in a drastic way, you’re on tilt.
The concept of tilt comes from traditional pinball machines. To keep players
from damaging the machines by lifting them to alter the course of the ball, the
manufacturers placed sensors inside that disabled the machine if it was violently
jostled. The flippers stopped working, the lights went off, and the word “tilt”
flashed at numerous places on the layout. The origin of tilt in pinball is apt
because what’s going on in our brain in moments of tilt is like a shaken pinball
machine. When the emotional center of the brain starts pinging, the limbic
system (specifically the amygdala) shuts down the prefrontal cortex. We light
up . . . then we shut down our cognitive control center.
There are emotional and physiological signs of tilt. In poker, you can hear a
poker player on tilt from several tables away. Every several hands, you hear a
raised voice in an incredulous tone: “Seriously? Again?” or “I don’t know why I
bother playing. I should just hand over all my money.” (Imagine the inflection of
exasperation and a lot of swearing.) Along with these verbal cues, there are
physiological signs of tilt. We can feel our cheeks flush and our heart race. Our
respiration speeds up.
Tilt, of course, is not just limited to poker. Any kind of outcome has the
potential for causing an emotional reaction. We can be tempted to make a

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reactive, emotional decision in a
disagreement with a relationship partner, or
because of bad service in a restaurant, or a comment in the workplace, or making
a sale only to have it canceled, or having an idea dismissed. We’ve all had this
experience in our personal and professional lives: blowing out of proportion a
momentary event because of an in-the-moment emotional reaction.
By recognizing in advance these verbal and physiological signs that ticker
watching is making us tilt, we can commit to develop certain habit routines at
those moments. We can precommit to walk away from the situation when we
feel the signs of tilt, whether it’s a fight with a spouse or child, aggravation in a
work situation, or losing at a poker table. We can take some space till we calm
down and get some perspective, recognizing that when we are on tilt we aren’t
decision fit.
Aphorisms like “take ten deep breaths” and “why don’t you sleep on
it?” capture this desire to avoid decisions while on tilt. We can commit to asking
ourselves the 10-10-10 questions or things like, “What’s happened to me in the
past when I’ve felt this way?” or “Do I think it’s going to help me to be in this
state while I’m making decisions?” Or we can gain perspective by asking how or
whether this will have a real effect on our long-term happiness.
If you are part of a truthseeking pod, that pod can incorporate questions
designed to sniff out tilt and reduce the number of decisions we execute while on
tilt. We can incorporate vigilance around ticker watching when evaluating each
other’s decisions, including the most obvious question: “Do you think maybe
you are/were on tilt?” We can follow that with time-traveling questions like, “Do
you think this will really matter in the long run?” If we make the concept of tilt
and its negative impact on decision quality part of the discussion, it creates
accountability around tilt to the group. Ignoring the signals of emotional
decision-making
raises the prospect of having to answer for it. That, in turn, will
get us positive reinforcement from the group for recognizing the signs of tilt and
avoiding decision-making in that state. It also trains good habits of mind so we
can run these processes on our own, acting as our own decision buddy.
At the very beginning of my poker career, I heard an aphorism from some of
the legends of the profession: “It’s all just one long poker game.” That aphorism
is a reminder to take the long view, especially when something big happened in
the last half hour, or the previous hand—or when we get a flat tire. Once we
learn specific ways to recruit past and future versions of us to remind ourselves
of this, we can keep the most recent upticks and downticks in their proper
perspective. When we take the long view, we’re going to think in a more rational
way.

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Ulysses contracts: time traveling to precommit
The most famous traveler of antiquity, the Homeric hero Odysseus, was also a
mental time traveler. One of the legendary trials on his journey home involved
the island of the Sirens. Sailors passing the island became so entranced by the
Sirens’ song that they would steer toward the shore, crashing to their deaths on
the rocky shoal around the island. Aware of the fate that befell any sailor who
heard the song, Odysseus told his crew to tie his hands to the mast and fill their
ears with beeswax as they approached the island. They could then steer safely,
unaffected by the song they could not hear, while he would get to hear the
Sirens’ song without imperiling the ship.
The plan worked perfectly. This action—past-us preventing
present-us from
doing something stupid—has become known as a Ulysses contract. (Most
translations of Homer use the hero’s ancient Greek name, Odysseus. The time-
travel strategy uses the hero’s ancient Roman name, Ulysses.)
It’s the perfect interaction between past-you, present-you, and future-you.
Ulysses recognized that his future-self (along with his crew) would become
entranced by the Sirens and steer toward the rocks. So he had his crew fill their
ears with wax and tie his hands to the mast, literally binding his future-self to
better behavior. One of the simplest examples of this kind of contract is using a
ride-sharing service when you go to a bar. A past version of you, who
anticipated that you might decide irrationally about whether you are okay to
drive, has bound your hands by taking the car keys out of them.
Most illustrations of Ulysses contracts, like the original, involve
raising
a
barrier against irrationality. But these kinds of precommitment contracts can also
be designed to lower barriers that interfere with rational action. For example, if
we are trying to eat healthier, we might identify that an irrational decision point
occurs when we go to the mall with someone, agree to meet them in a couple
hours, and spend idle time in the food court. A barrier-
inducing
Ulysses contract
could involve us not going to the mall at all or budgeting our time tightly so we
have just enough time to accomplish our intended purpose. A barrier-
reducing
contract would be to precommit to carry healthy snacks in our bag, so we can
increase the probability, if we’re doing any idle eating, that we can make a better
choice since we have drastically reduced the effort it takes to grab a healthier
snack.
Ulysses contracts can come in varying levels of how much your hands are
bound, ranging from physically preventing acting on a decision to just

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committing in advance to certain actions
without any barriers save the
commitment itself. Regardless of the level of binding, precommitment contracts
trigger a decision-interrupt. At the moment when we consider breaking the
contract, when we want to cut the binding, we are much more likely to stop and
think.
When you are physically prohibited from deciding, you are interrupted in
the sense that you are prevented from acting on an irrational impulse; the option
simply isn’t there. That’s the brute-force way to do this kind of time traveling.
Past-Ulysses interrupted present-Ulysses’s decision by taking the decision,
literally, out of his hands.
In most situations, you can’t make a precommitment that’s 100% tamper-
proof. The hurdles aren’t necessarily high, but they nevertheless create a
decision-interrupt that may prompt us to do the bit of time travel necessary to
reduce emotion and encourage perspective and rationality in the decision. A
lawyer attending a settlement negotiation can make a precommitment, with the
client or other lawyers on their team, as to the lowest amount they would accept
in a settlement (or the highest amount they would agree to pay to settle). Home
buyers, understanding that in the moment they might get emotionally attached to
a home, can commit in advance to their budget. Once they decide on a house
they want to buy, they can decide in advance what the maximum amount they’d
be willing to pay for it is so that they don’t get caught up in the moment of the
bidding.
Throwing out all the junk food in our house makes it impossible for
midnight-us to easily, mindlessly, down a pint of ice cream. But as long as we
have a car or food delivery services, that kind of food is still available
somewhere. It just takes a lot more effort to get it. The same is true if we ask the
waiter not to put the bread basket on the table at the restaurant. We can still,
obviously, get bread, but now we have to ask the waiter to bring it. In fact, even
Ulysses had to rely on his crew to ignore him if, upon hearing the Sirens’ song,
he signaled them to free him.
Ulysses contracts can help us in several ways to be more rational investors.
When we set up an automatic allocation from our pay into a retirement account,
that’s a Ulysses contract. We could go through the trouble of changing the
allocation, but setting it up initially gives our goal-setting, System 2–self a
chance to precommit to what we know is best for our long-term future. And if
we want to change the allocation, we have to take some specific steps to do so,
creating a decision-interrupt.
Investment advisors do this with clients, determining in advance, as they are

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Investment advisors do this with clients, determining in advance, as they are
discussing the client’s goals, the conditions under which they would buy, sell,
hold, or press their positions on particular stocks. If the client later wants to
make an emotional decision in the moment (involving, for example, a sudden
rise or drop in the value of an investment), the advisor can remind the client of
the discussion and the agreement.
In all these instances, the precommitment or predecision doesn’t completely
bind our hands to the mast. An emotional, reactive, irrational decision is still
physically possible (though, to various degrees, more difficult). The
precommitments, however, provide a stop-and-think moment before acting,
triggering the potential for deliberative thought. Will that prevent an emotional,
irrational decision every time? No. Will we sometimes still decide in a reflexive
or mindless way? Of course. But it will happen less often.
Decision swear jar
We all know about the concept of a swear jar: if someone swears, they put a
dollar in the jar. The idea behind it is that it will make people mindful about
swearing and reduce how much they do it. A “decision swear jar” is a simple
kind of precommitment contract that we can apply to many of the key concepts
of this book. For the decision swear jar, we identify the language and thinking
patterns that signal we are veering from our goal of truthseeking. When we find
ourselves using certain words or succumbing to the thinking patterns we are
trying to avoid because we know they are signs of irrationality, a stop-and-think
moment can be can be created. You can think about this as a way to implement
accountability.
We have discussed several patterns of irrationality in the way we lodge
beliefs and field outcomes. From these, we can commit to vigilance around
words, phrases, and thoughts that signal that we might be not be our most
rational selves. Your list of those warning signs will be specific to you (or your
family, friends, or enterprise), but here is a sample of the kinds of things that
might trigger a decision-interrupt.
Signs of the illusion of certainty: “I know,” “I’m sure,” “I knew it,” “It
always happens this way,” “I’m certain of it,” “you’re 100% wrong,”
“You have no idea what you’re talking about,” “There’s no way that’s

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true,” “0%” or “100%” or their equivalents, and other terms signaling that
we’re presuming things are more certain than we know they are. This also
includes stating things as absolutes, like “best” or “worst” and “always” or
“never.”
Overconfidence: similar terms to the illusion of certainty.
Irrational outcome fielding: “I can’t believe how unlucky I got,” or the
reverse, if we have some default phrase for credit taking, like “I’m at the
absolute top of my game” or “I planned it perfectly.” This includes
conclusions of luck, skill, blame, or credit. It includes equivalent terms for
irrationally fielding the outcomes of others, like, “They totally had that
coming,” “They brought it on themselves,” and “Why do they always get
so lucky?”
Any kind of moaning or complaining about bad luck just to off-load it,
with no real point to the story other than to get sympathy. (An exception
would be when we’re in a truthseeking group and we make explicit that
we’re taking a momentary break to vent.)
Generalized characterizations of people meant to dismiss their ideas:
insulting, pejorative characterizations of others, like “idiot” or, in poker,
“donkey.” Or any phrase that starts by characterizing someone as “another
typical ________.” (Like David Letterman said to Lauren Conrad, he
dismissed everyone around him as an idiot, until he pulled himself into
deliberative mind one day and asked, “What are the odds that
everyone
is
an idiot?”)
Other violations of the Mertonian norm of universalism, shooting the
message because we don’t think much of the messenger. Any sweeping
term about someone, particularly when we equate our assessment of
an
idea
with a sweeping personality or intellectual assessment of the person
delivering the idea, such as “gun nut,” “bleeding heart,” “East Coast,”
“Bible belter,” “California values”—political or social issues. Also be on
guard for the reverse: accepting a message because of the messenger or
praising a source immediately after finding out it confirms your thinking.
Signals that we have zoomed in on a moment, out of proportion with the
scope of time: “worst day ever,” “the day from hell.”
Expressions that explicitly signal motivated reasoning, accepting or
rejecting information without much evidence, like “conventional wisdom”
or “if you ask anybody” or “Can you prove that it’s not true?” Similarly,

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look for expressions that you’re participating in an echo chamber, like
“everyone agrees with me.”
The word “wrong,” which deserves its own swear jar. The Mertonian
norm of organized skepticism allows little place in exploratory discussion
for the word “wrong.” “Wrong” is a conclusion, not a rationale. And it’s
not a particularly accurate conclusion since, as we know, nearly nothing is
100% or 0%. Any words or thoughts denying the existence of uncertainty
should be a signal that we are heading toward a poorly calibrated decision.
Lack of self-compassion: if we’re going to be self-critical, the focus
should be on the lesson and how to calibrate future decisions. “I have the
worst judgment on relationships” or “I should have known” or “How
could I be so stupid?”
Signals we’re being overly generous editors when we share a story.
Especially in our truthseeking group, are we straying from sharing the
facts to emphasize our version? Even outside our group, unless we’re
sharing a story purely for entertainment value, are we assuring that our
listener will agree with us? In general, are we violating the Mertonian
norm of communism?
Infecting our listeners with a conflict of interest, including our own
conclusion or belief when asking for advice or
informing the listener of
the outcome before getting their input.
Terms that discourage engagement of others and their opinions, including
expressions of certainty and also initial phrasing inconsistent with that
great lesson from improvisation—“yes, and . . .” That includes getting
opinions or information from others and starting with “no” or “but . . .”
This is by no means a complete list, but it provides a flavor of the kinds of
statements and thinking that should trigger vigilance on our part.
Once we recognize that we should watch out for particular words, phrases,
and thoughts, when we find ourselves saying or thinking those things, we are
breaking a contract, a commitment to truthseeking. These terms are signals that
we’re succumbing to bias. Because if we are cutting the binding when we catch
ourselves saying or thinking these things, it can trigger a moment of reflection,
interrupting us in the moment. Popping out of the moment like that can remind
us of why we took the trouble to list terms that signal potential decision traps.
The swear jar is a simple example of a Ulysses contract in action: we think
ahead to a hazard in our decision-making future and devise a plan of action
around that, or at least commit that we will take a moment to recognize we are

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around that, or at least commit that we will take a moment to recognize we are
veering away from truthseeking. Better precommitment contracts result from
better anticipation of what the future might look like, what kinds of decisions we
want to avoid, and which ones we want to promote. That takes thoughtful
reconnaissance.
Reconnaissance: mapping the future
Operation Overlord, the Allied forces operation to retake German-occupied
France starting in Normandy, was the largest seaborne invasion in military
history. It involved planning and logistics on an unprecedented scale. What if the
forces were delayed at the start by bad weather? What if the airborne landing
force had trouble communicating by radio due to the terrain? What if significant
numbers of paratroopers were blown off course? What if currents interfered with
the beach landings? What if the forces on the different beaches remained
separated? Countless things could go wrong, with tens of thousands of lives at
stake and, potentially, the outcome of the war.
All those things
did
go wrong, along with many other challenges
encountered on D-Day and immediately thereafter. The Normandy landings still
succeeded, though, because they prepared for as many potential scenarios as
possible. Reconnaissance has been part of advance military planning for as long
as horses have been used in battle. The modern military, of course, has evolved
from sending scouts by horse and reporting back to the main forces to planes,
drones, satellites, and other high-tech equipment gathering information about
what to expect in battle.
The Navy SEAL team that caught and killed Osama bin Laden wouldn’t
have entered his compound without knowing what they would find beyond the
walls. What buildings were there? What was their layout and purpose? What
differences might it make if they conducted the raid in different kinds of weather
or different times of day? What other people would be present and what risks
would they pose? What would they do if bin Laden wasn’t there? What was the
team trying to commit
to, given what they knew about each of those things (and,
of course, numerous others)? Just as they relied on reconnaissance, we shouldn’t
plan our future without doing advance work on the range of futures that could
result from any given decision and the probabilities of those futures occurring.
For us to make better decisions, we need to perform reconnaissance on the
future. If a decision is a bet on a particular future based on our beliefs, then

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future. If a decision is a bet on a particular future based on our beliefs, then
before we place a bet we should consider in detail what those possible futures
might look like. Any decision can result in a set of possible outcomes.
Thinking about what futures are contained in that set (which we do by
putting memories together in a novel way to imagine how things might turn out)
helps us figure out which decisions to make.
Figure out the possibilities, then take a stab at the probabilities. To start, we
imagine the range of potential futures. This is also known as scenario planning.
Nate Silver, who compiles and interprets data from the perspective of getting the
best strategic use of it, frequently takes a scenario-planning approach. Instead of
using data to make a particular conclusion, he sometimes takes
the approach of
discussing all the scenarios the data
could
support. In early February 2017, he
described the merits of scenario planning: “When faced with highly uncertain
conditions, military units and major corporations sometimes use an exercise
called scenario planning. The idea is to consider a broad range of possibilities for
how the future might unfold to help guide long-term planning and preparation.”
After identifying as many of the possible outcomes as we can, we want to
make our best guess at the probability of each of those futures occurring. When I
consult with enterprises on building decision trees and determining probabilities
of different futures, people frequently resist having to make a guess at the
probability of future events mainly because they feel like they can’t be certain of
what the likelihood of any scenario is. But that’s the point.

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The reason why we do reconnaissance is
because
we are uncertain. We
don’t (and likely can’t) know how often things will turn out a certain way with
exact precision. It’s not about approaching our future predictions from a point of
perfection. It’s about acknowledging that we’re already making a prediction
about the future every time we make a decision, so we’re better off if we make
that explicit. If we’re worried about guessing, we’re
already
guessing. We are
already guessing that the decision we execute will result in the highest likelihood
of a good outcome given the options we have available to us. By at least trying
to assign probabilities, we will naturally move away from the default of 0% or
100%, away from being sure it will turn out one way and not another. Anything
that moves us off those extremes is going to be a more reasonable assessment
than not trying at all. Even if our assessment results in a wide range, like the
chances of a particular scenario occurring being between 20% and 80%, that is
still better than not guessing at all.
This kind of reconnaissance of the future is something that experienced
poker players are very familiar with. Before making a bet, poker players
consider each of their opponents’ possible responses (fold, call, raise), and the
likelihood and desirability of each. They also think about what they will do in
response (if some or all of the opponents don’t fold). Even if you don’t know
much about poker, it should make sense that a player is better off considering
these things
before
they bet. The more expert the player, the further into the
future they plan. Before making that decision to bet, the expert player is
anticipating what they’ll do following each response, as well as how the action
they take now affects their future decisions on the hand. The best players think
beyond the current hand into subsequent hands: how do the actions of this hand
affect how they and their opponents make decisions on future hands? Poker
players really live in this probabilistic world of, “What are the possible futures?
What are the probabilities of those possible futures?” And they get very
comfortable with the fact that they don’t know exactly because they can’t see
their opponent’s cards.
This is true of most strategic thinking. Whether it involves sales strategies,
business strategies, or courtroom strategies, the best strategists are considering a
fuller range of possible scenarios, anticipating and considering the strategic
responses to each, and so on deep into the decision tree.
This kind of scenario planning is a form of mental time travel we can do on
our own. It works even better when we do it as part of a scenario-planning
group, particularly one that is open-minded to dissent and diverse points of view.

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Diverse viewpoints allow for the identification of a wider variety of scenarios
deeper into the tree, and for better estimates of their probability. In fact, if two
people in the group are really far off on an estimate
of the likelihood of an
outcome, that is a great time to have them switch sides and argue the other’s
position. Generally, the answer is somewhere in the middle and both people will
end up moderating their positions. But sometimes one person has thought of a
key influencing factor the other hasn’t and that is revealed only because the
dissent was tolerated.
In addition to increasing decision quality, scouting various futures has
numerous additional benefits. First, scenario planning reminds us that the future
is inherently uncertain. By making that explicit in our decision-making process,
we have a more realistic view of the world. Second, we are better prepared for
how we are going to respond to different outcomes that might result from our
initial decision. We can anticipate positive or negative developments and plan
our strategy, rather than being reactive. Being able to respond to the changing
future is a good thing; being surprised by the changing future is not. Scenario
planning makes us nimbler because we’ve considered and are prepared for a
wider variety of possible futures. And if our reconnaissance has identified
situations where we are susceptible to irrationality, we can try to bind our hands
with a Ulysses contract. Third, anticipating the range of outcomes also keeps us
from unproductive regret (or undeserved euphoria) when a particular future
happens. Finally, by mapping out the potential futures and probabilities, we are
less likely to fall prey to resulting or hindsight bias, in which we gloss over the
futures that did not occur and behave as if the one that did occur must have been
inevitable, because we have memorialized all the possible futures that could
have happened.
Scenario planning in practice
A few years ago, I consulted with a national nonprofit organization, After-
School All-Stars (ASAS), to work with them on incorporating scenario planning
into their budgeting.
*
ASAS, founded in 1992 by Arnold Schwarzenegger,
provides three hours of structured after-school programming for over 70,000
underserved youth in eighteen cities across the United States. They depend
heavily on grants for funding and were struggling with budget planning, given

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the uncertainty in the grant award process. To help them with planning, I asked
for a list of all their grant applications and how much each grant was worth.
They provided me with a list of all their outstanding grant applications and the
award amounts applied for. I told them that I didn’t see how much each grant
was worth in the information they provided. They pointed to the column of the
award amounts sought. At that point, I realized we were working from different
ideas about how to determine worth. The misunderstanding came from the
disconnect between the
expected value
of each grant and the amount they would
be awarded
if they got the grant.
*
Coming up with the expected value of each grant involves a simple form of
scenario planning: imagining the two possible futures that could result from the
application (awarded or declined) and the likelihood of each future. For
example, if they
applied for a $100,000 grant that they would win 25% of the
time, that grant would have an expected value of $25,000 ($100,000 × .25). If
they expected to get the grant a quarter of the time, then it wasn’t worth
$100,000; it was worth a quarter of $100,000. A $200,000 application with a
10% chance of success would have an expected value of $20,000. A $50,000
grant with a 70% chance of success would be worth $35,000. Without thinking
probabilistically in this way, determining a grant’s worth isn’t possible—it leads
to the mistaken belief that the $200,000 grant is worth the most when, in fact, the
$50,000 grant is. ASAS recognized that uncertainty was causing them problems
(to the point where they felt enslaved by it in their budgeting), but they hadn’t
wrapped uncertainty into their planning or resource-allocation process. They
were flying by the seat of their pants.
After I worked with the national office, ASAS made estimating the
likelihood of getting each grant part of their planning. The benefits they got from
scenario planning were immediate and substantial:
They created a more efficient and productive work stack. Before doing
this exercise, they naturally put a higher priority on applications seeking
larger dollar amounts, executing those first, putting more senior staff on
them, and being more likely to hire outside grant writers to get those
applications completed. By shifting to thinking about the probability of
getting each grant, they were now able to prioritize by how much the grant
was actually worth to the organization in making these decisions.
Thereafter, the higher
value
grants went to the top of the stack rather than
just the grants with the higher potential awards.

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They could budget more realistically. They had greater confidence in
advance estimates of the amount of money they could expect to receive.
Because coming up with an expected value required estimating the
likelihood of getting the grant, they increasingly focused on improving the
accuracy of their estimates. This prompted them to close the loop by going
back to the grantors. They had previously, after rejections, followed up
with grantors. Because they were now focusing on checking and
calibrating their probabilities, they expanded this to the grants they won.
Overall, their post-outcome reviews focused on understanding what
worked, what didn’t work, what was luck, and how to do better, improving
both their probability estimates and the quality of their grant applications.
They could think about ways to increase the probability of getting grants
and commit to those actions.
They were less likely to fall prey to hindsight bias because they had
considered in advance the probability of getting or not getting the grant.
They were less likely to fall prey to resulting because they had evaluated
the decision process in advance of getting or not getting the grant.
Finally, because of all the benefits ASAS received from incorporating
scenario planning into budgeting and grant applications, they expanded the
implementation of this type of scenario planning across departments,
making it part of their decision-making culture.
Grant prospecting is similar to sales prospecting, and this process can be
implemented for any sales team. Assign probabilities
for closing or not closing
sales, and the company can do better at establishing sales priorities, planning
budgets and allocating resources, evaluating and fine-tuning the accuracy of its
predictions, and protecting itself against resulting and hindsight bias.
A more complex version of scenario planning occurs when the number of
possible futures multiplies and/or we go deeper into the tree, considering what
we will do next in response to how things turn out and what the set of outcomes
of that next decision is and so on.
Consider the scenario planning involved in Seahawks coach Pete Carroll’s
much-criticized Super Bowl decision, trailing by four, twenty seconds
remaining, one time-out, second-and-goal at the Patriots’ one-yard line. Carroll
has two general choices, run or pass, and they in turn lead to multiple scenarios.
If Carroll calls for a run, these are the possible futures: (a) touchdown
(immediate win); (b) turnover-fumble (immediate loss); (c) tackle short of the
goal line; (d) offensive penalty; and (e) defensive penalty. Futures (c)–(e) branch

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goal line; (d) offensive penalty; and (e) defensive penalty. Futures (c)–(e) branch
into additional scenarios. The most likely failure scenario, by far, is that the
runner is tackled before reaching the end zone. Seattle could stop the clock with
its final time-out, but if they run the ball again and do not score, time will expire.
If Carroll calls for a pass, the possible futures are (a) touchdown (immediate
success); (b) turnover-interception (immediate failure); (c) incomplete pass; (d)
sack; (e) offensive penalty; and (f) defensive penalty. Again, the first two futures
essentially end the game and the others branch off into additional play calling
and additional outcomes.
The main difference between passing and running is that calling a pass
likely gives Seattle a total of three plays to score, instead of two if Carroll calls a
running play. An unsuccessful
run would require that Seattle use their final time-
out to stop the clock so they could run a second play. An incomplete pass would
stop the clock and leave Seattle with a time-out and the chance to call those
same two running plays. An interception, which negates the possibility of a
second or third offensive play, is only a 2%–3% probability, a small price to pay
for three chances to score rather than two. (A turnover caused by a fumble on a
running play is 1%–2%.)
*
Notice that the option of the extra play doesn’t reveal itself without this kind
of scouting of the future. Even after the fact, with lots of time to analyze the
decision, very few commentators saw this advantage.
The important thing is that we do better when we scout all these futures and
make decisions based on the probabilities and desirability of the different
futures. ASAS couldn’t guarantee it would get awarded every grant it applied
for, but it could, through good process, make better decisions about which grants
to prioritize and how much revenue to expect from the basket of grant
proposals
they submitted. Despite the outcry, I’d like to think that Pete Carroll didn’t lose
much sleep over his decision to call for Wilson to pass.
Reconnaissance of the future dramatically improves decision quality and
reduces reactivity to outcomes. So far, we have talked about thinking ahead to
what the future might look like. But it turns out that better decision trees, more
effective scenario planning, results from working backward rather than forward.
Backcasting: working backward from a positive
future

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All methods of imagining the future are not created equal. You know that
Chinese proverb, “A journey of a thousand miles starts with a single step”?
Turns out, if we were contemplating a thousand-mile walk, we’d be better off
imagining ourselves looking back from the destination and figuring how we got
there. When it comes to advance thinking, standing at the end and looking
backward is much more effective than looking forward from the beginning.
The distorted view we get when we look into the future from the present is
similar to the stereotypical view of the world by Manhattan residents, poked fun
at by the famous
New Yorker
cover. The cover was a drawing of a map from the
perspective of a person from New York. In that view, half the map covers a few
blocks of the city. While you can see all the buildings on Ninth Avenue and even
vehicles and people, the Hudson River and New Jersey are just horizontal strips.
The entire United States occupies the same amount of space as the distances
between Ninth
and Tenth avenues. Beyond a strip of the Pacific Ocean are three
tiny lumps, labeled “China,” “Japan,” and “Russia.”
When we forecast the future, we run the risk of a similar distortion. From
where we stand, the present and the immediate future loom large. Anything
beyond that loses focus.
Imagining the future recruits the same brain pathways as remembering the
past. And it turns out that remembering the future is a better way to plan for it.
From the vantage point of the present, it’s hard to see past the next step. We end
up over-planning for addressing problems we have right now. Implicit in that
approach is the assumption that conditions will remain the same, facts won’t
change, and the paradigm will remain stable. The world changes too fast to
assume that approach is generally valid. Samuel Arbesman’s
The Half-Life of
Facts
makes a book-length case for the hazards of assuming the future is going
to be like the present.
Just as great poker players and chess players (and experts in any field) excel
by planning further into the future than others, our decision-making improves
when we can more vividly imagine the future, free of the distortions of the
present. By working backward from the goal, we plan our decision tree in more
depth, because we start at the end.
When we identify the goal and work backward from there to “remember”
how we got there, the research shows that we do better. In a
Harvard Business
Review
article, decision scientist Gary Klein summarized the results of a 1989
experiment by Deborah Mitchell, J. Edward Russo, and Nancy Pennington. They
“found that prospective hindsight—imagining that an event has already occurred

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—increases the ability to correctly identify reasons for future outcomes by
30%.”
A huge urban planning project requires enormous amounts
of money,
materials, commitment—and the vision to work backward from a distant future
goal. When Frederick Law Olmsted designed Central Park, for example, he
recognized that a lot of the charm of the park, and a lot of enjoyment that people
would get from it, would take decades, as the landscape changed and matured.
People walking through Central Park when it opened to the public in 1858 would
have seen a lot of barren land. Even in 1873, when construction was
substantially completed, there was a lot of undergrown foliage. The trees,
shrubs, and plants were clearly recent transplants. None of those visitors would
recognize Central Park today. But Olmstead would because he was starting from
what it would develop into.
The most common form of working backward from our goal to map out the
future is known as backcasting. In backcasting, we imagine we’ve already
achieved a positive outcome, holding up a newspaper with the headline “We
Achieved Our Goal!” Then we think about how we got there.
Let’s say an enterprise wants to develop a three-year strategic plan to double
market share, from 5% to 10%. Each person engaged in the planning imagines
holding up a newspaper whose headline reads “Company X Has Doubled Its
Market Share over the Past Three Years.” The team leader now asks them to
identify the reasons they got there, what events occurred, what decisions were
made, what went their way to get the enterprise to capture that market share.
This enables the company to better identify strategies, tactics, and actions that
need to be implemented to get to the goal. It also allows it to identify when the
goal needs to be tweaked. Backcasting makes it possible to identify when there
are low-probability events that must occur to reach the goal. That could lead to
developing strategies to increase the chances those events occur or to
recognizing the goal
is too ambitious. The company can also make
precommitments to a plan developed through backcasting, including responses
to developments that can interfere with reaching the goal and identifying
inflection points for re-evaluating the plan as the future unfolds.
In planning a trial strategy after taking a new case, a trial lawyer can
imagine the headline of winning at trial. What favorable rulings did the lawyer
get along the way? How did the most favorable testimony play out? What kind
of evidence did the judge allow or throw out? What points did the jury respond
to?
If our goal is to lose twenty pounds in six months, we can plan how to

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If our goal is to lose twenty pounds in six months, we can plan how to
achieve that by imagining it’s six months from now and we’ve lost the weight.
What are the things we did to lose the weight? How did we avoid junk food?
How did we increase the amount of exercise we were doing? How did we stick
to the regimen?
Imagining a successful future and backcasting from there is a useful time-
travel exercise for identifying necessary steps for reaching our goals. Working
backward helps even more when we give ourselves the freedom to imagine an
unfavorable
future.
Premortems: working backward from a negative
future
If you know medical terms or watch forensic-crime dramas on TV, you are
familiar with a postmortem: that’s where a medical examiner determines the
cause of death. A premortem is an investigation into something awful, but
before
it happens. We all like to bask in an optimistic view of the future. We generally
are biased to overestimate the probability of good things happening.
Looking at
the world through rose-colored glasses is natural and feels good, but a little
naysaying goes a long way. A premortem is where we check our positive attitude
at the door and imagine not achieving our goals.
Backcasting and premortems complement each other. Backcasting imagines
a positive future; a premortem imagines a negative future. We can’t create a
complete picture without representing both the positive space and the negative
space. Backcasting reveals the positive space. Premortems reveal the negative
space. Backcasting is the cheerleader; a premortem is the heckler in the
audience.
Imagining a headline that reads “We Failed to Reach Our Goal” challenges
us to think about ways in which things could go wrong that we otherwise
wouldn’t if left to our own (optimistic, team-player) devices. For the company
with the three-year plan to double market share, the premortem headline is
“Company Fails to Reach Market Share Goal; Growth Again Stalls.” Members
of the planning team now imagine delays in new products, loss of key executives
or sales or marketing or technical personnel, new products by competitors,
adverse economic developments, paradigm shifts that could lead customers to do
without the product or rely on alternatives not on the market or in use,
etc.
A lawyer trying a case now considers favorable evidence being disallowed,

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A lawyer trying a case now considers favorable evidence being disallowed,
undiscovered evidence undermining the case, drawing an unsympathetic judge,
and the jury disliking or distrusting the main witnesses.
When we set a weight-loss goal and put a plan to reach that goal in place, a
premortem will reveal how we felt obligated to eat cake when it was somebody’s
birthday, how hard it was to resist the bagels and cookies in the conference
room, and how hard it was to find time for the gym or how easy it was to find
excuses to procrastinate going. There has been a massive amount written about
visualizing success as a way to achieve our goals. Because that’s such a common
element in self-help strategies, conducting a premortem (with its negative
visualization) may seem like a counterproductive way to succeed.
Despite the popular wisdom that we achieve success through positive
visualization, it turns out that incorporating
negative visualization
makes us
more
likely to achieve our goals. Gabriele Oettingen, professor of psychology at
NYU and author of
Rethinking Positive Thinking: Inside the New Science of
Motivation
, has conducted over twenty years of research, consistently finding
that people who imagine obstacles in the way of reaching their goals are more
likely to achieve success, a process she has called “mental contrasting.” Her first
study, of women enrolled in a weight-loss program, found that subjects “who
had strong positive fantasies about slimming down . . . lost twenty-four pounds
less
than those who pictured themselves more negatively. Dreaming about
achieving a goal apparently didn’t help that goal come to fruition. It impeded it
from happening. The starry-eyed dreamers in the study were less energized to
behave in ways that helped them lose weight.”
She repeated these results in different contexts. She recruited college
students who claimed to have an unrequited crush. She then prompted one group
to imagine positive scenarios of initiating a relationship, and another group to
imagine negative scenarios of how that would play out. The results were similar
to those in the weight-loss study: five months later, the subjects indulging in the
positive scenario planning were less likely to initiate the relationship. She found
the same results when studying job seekers, students before a midterm exam, and
patients undergoing hip replacement surgery.
Oettingen recognized that we need to have positive goals, but we are more
likely to execute on those goals if we think about the negative futures. We start a
premortem by imagining why we failed to reach our goal: our company hasn’t
increased its market share; we didn’t lose weight; the jury verdict came back for
the other side; we didn’t hit our sales target. Then we imagine why. All those

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reasons why we didn’t achieve our goal help us anticipate potential obstacles and
improve our likelihood of succeeding.
A premortem is an implementation of the Mertonian norm of organized
skepticism, changing the rules of the game to give permission for dissent. Being
a team player in a premortem isn’t about being the most enthusiastic cheerleader;
it’s about being the most productive heckler. “Winning” isn’t about the group
feeling good because everyone confirms their (and the organization’s) narrative
that things are going to turn out great. The premortem starts with working
backward from an unfavorable future, or failure to achieve a goal, so competing
for favor, or feeling good about contributing to the process, is about coming up
with the most creative, relevant, and actionable reasons for why things didn’t
work out.
The key to a successful premortem is that everyone feels free to look for
those reasons, and they are motivated to scour everything—personal experience,
company experience, historical precedent, episodes of
The Hills
, sports
analogies, etc.—to come up with ways a decision or plan can go bad, so the team
can anticipate and account for them.
Conducting a premortem creates a path to act as our own red team. Once we
frame the exercise as “Okay, we failed. Why did we fail?” that frees everyone to
identify potential points of failure they otherwise might not see or might not
bring up for fear of being viewed as a naysayer. People can express their
reservations
without it sounding like they’re saying the planned course of action
is wrong. Because of that, a planning process that includes a premortem creates a
much healthier organization because it means that the people who do have
dissenting opinions are represented in the planning. They don’t feel like they’re
shut out or not being heard. Everyone’s voice now has more value. The
organization is less likely to discourage dissent and thereby lose the value of
diverse opinions. Those who have reservations are less likely to have resentment
or regret build if things don’t work out; their voices were represented in the
strategic plan.
Incorporating this type of imagining of the negative space into a
truthseeking group reinforces a new habit routine of visualizing and anticipating
future obstacles. As always, when a group we are part of reinforces this kind of
thinking, we are more likely in our own thinking to consider the downside of our
decisions.
Imagining both positive and negative futures helps us build a more realistic
vision of the future, allowing us to plan and prepare for a wider variety of
challenges, than backcasting alone. Once we recognize the things that can go

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challenges, than backcasting alone. Once we recognize the things that can go
wrong, we can protect against the bad outcomes, prepare plans of action, enable
nimble responses to a wider range of future developments, and assimilate a
negative reaction in advance so we aren’t so surprised by it or reactive to it. In
doing so, we are more likely to achieve our goals.
Of course, when we backcast and imagine the things that went right, we
reveal the problems if those things
didn’t
go right. Backcasting doesn’t,
therefore, ignore the negative space so much as it overrepresents the positive
space. It’s in our optimistic nature (and natural in backcasting) to imagine a
successful future. Without a premortem, we don’t see as many paths to the future
in which we don’t reach our goals. A premortem forces us to build
out that side
of the tree where things don’t work out. In the process, we are likely to realize
that’s a pretty robust part of the tree.
Remember, the likelihood of positive and negative futures must add up to
100%. The positive space of backcasting and the negative space of a premortem
still have to fit in a finite amount of space. When we see how much negative
space there really is, we shrink down the positive space to a size that more
accurately reflects reality and less reflects our naturally optimistic nature.
We make better decisions, and we feel better about those decisions, once we
get our past-, present-, and future-selves to hang out together. This not only
allows us to adjust how optimistic we are, it allows us to adjust our goals
accordingly and to actively put plans in place to reduce the likelihood of bad
outcomes and increase the likelihood of good ones. We are less likely to be
surprised by a bad outcome and can better prepare contingency plans.
It may not feel so good during the planning process to include this focus on
the negative space. Over the long run, however, seeing the world more
objectively and making better decisions will feel better than turning a blind eye
to negative scenarios. In a way, backcasting without premortems is a form of
temporal discounting: if we imagine a positive future, we feel better
now
, but
we’ll more than compensate for giving up that immediate gratification through
the benefits of seeing the world more accurately, making better initial decisions,
and being nimbler about what the world throws our way.
Once we make a decision and one of those possible futures actually
happens, we can’t discard all that work, even—or especially—if it included
work on futures that did not occur. Forgetting about an unrealized future can be
dangerous to good decision-making.

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Dendrology and hindsight bias (or, Give the
chainsaw a rest)
One of the goals of mental time travel is keeping events in perspective. To
understand an overriding risk to that perspective, think about time as a tree. The
tree has a trunk, branches at the top, and the place where the trunk meets the
branches. The trunk is the past. A tree has only one, growing trunk, just as we
have only one, accumulating past. The branches are the potential futures.
Thicker branches are the equivalent of more probable futures, thinner branches
are less probable ones. The place where the top of the trunk meets the branches
is the present. There are many futures, many branches of the tree, but only one
past, one trunk.
As the future becomes the past, what happens to all those branches? The
ever-advancing present acts like a chainsaw. When one of those many branches
happens to be the way things turn out, when that branch transitions into the past,
present-us cuts off all those other branches that didn’t materialize and obliterates
them. When we look into the past and see only the thing that happened, it seems
to have been inevitable. Why wouldn’t it seem inevitable from that vantage
point? Even the smallest of twigs, the most improbable of futures—like the 2%–
3% chance Russell Wilson would throw that interception—expands when it
becomes part of the mighty trunk. That 2%–3%, in hindsight, becomes 100%,
and all the other branches, no matter how thick they were, disappear from view.
That’s hindsight bias, an enemy of probabilistic thinking.
Judge Frank Easterbrook, a leading jurist and member of the U.S. Court of
Appeals for the Seventh Circuit, warned about the
danger in the legal system of
assessing probabilities
after
one of the potential futures has occurred.
Jentz v.
ConAgra Foods
involved a “hot” grain bin owned by ConAgra and the company
it hired (named West Side) to investigate and deal with the cause of the burning
smell, smoke, and raised temperatures from the bin. After failing to solve the
problem, West Side’s foreman told ConAgra to call the fire department. He then
told some of his employees to remove tools from a tunnel to the bin that could
impair firefighters’ access.
While the workers were in the tunnel, the bin exploded, severely injuring the
West Side employees. Those injured sued ConAgra and West Side. The jury
awarded $180 million in compensatory and punitive damages. In addressing the
punitive damages against West Side, Judge Easterbrook, writing for the court,
pointed out that Illinois law required a “gross deviation” from the standard of

## Page 164

pointed out that Illinois law required a “gross deviation” from the standard of
care to award punitive damages. Finding no evidence in the record of any
foreseeable likelihood of explosion at the time the foreman ordered the workers
to remove tools from the tunnel, he concluded, “The verdict appears to be a
consequence of hindsight bias—the human tendency to believe that whatever
happened was bound to happen, and that everyone must have known it. If [the
foreman] believed that an explosion was imminent, then he is a monster; but of
that there is no evidence. Hindsight bias is not enough to support a verdict.”
Once we know there was an explosion, it’s difficult to imagine the actions
of the parties when the explosion was only one of several possible futures. The
members of the jury had a conflict of interest. When they heard the story of the
men entering the tunnel to retrieve the tools, they knew the outcome. The jury
lopped off the other branches of the tree, all the other ways things could have
turned out given the situation in the grain-storage
bin and, in hindsight, all they
could see was a confluence of circumstances with an inevitable tragedy at the
end.
Imagine how things would be different for the protagonists throughout this
book if more of us acted like Judge Easterbrook.
Steve Bartman could have used a stadium full of Cubs fans with Judge
Easterbrook’s perspective. They would have recognized that the Cubs losing the
game was only one of many ways it could have turned out. At the moment
Bartman and others around him reached for the foul ball, the branch of the future
where Steve Bartman touches the ball was a small one, and the chance of the
Cubs losing after that was the tiniest of twigs, requiring a number of unlikely on-
field developments (like the pitching ace giving up hit after hit, and the great-
fielding shortstop committing a rare error on an inning-ending double play). Just
as the foreman ordering workers into the tunnel didn’t cause the explosion,
Bartman touching the ball didn’t cause the loss. He just got an unlucky result,
based on all the things that happened over which he had no control after the foul
ball touched his hands.
Pete Carroll and the world of Monday Morning Quarterbacks could have
used the judge’s reminder that we tend to assume that, once something happens,
it was bound to happen. If we don’t try to hold all the potential futures in mind
before
one of them happens, it becomes almost impossible to realistically
evaluate decisions or probabilities
after
.
This turned out to be a big part of the problem for the CEO in the wake of
firing his president. Although he had initially characterized the decision as one
of his worst, when we reconstructed the tree, essentially picking the branches up

## Page 165

off the ground and reattaching them, it was clear that he and his company had
made a series of careful, deliberative decisions. Because
they led to a negative
result, however, the CEO had been consumed with regret. When he looked back
at his decisions, he couldn’t see all those branches and their likelihoods. He
could only see the trunk. All he saw was a bad result.
The CEO removed all those other branches with a chainsaw and ran them
through a wood-chipper. They disappeared and he acted as if they never existed.
That’s what hindsight bias is, and we’re all running amok through the forest with
a chainsaw once we get an outcome. Once something occurs, we no longer think
of it as probabilistic—or as
ever
having been probabilistic. This is how we get
into the frame of mind where we say, “I should have known” or “I told you so.”
This is where unproductive regret comes from.
By keeping an accurate representation of what could have happened (and
not a version edited by hindsight), memorializing the scenario plans and decision
trees we create through good planning process, we can be better calibrators
going forward. We can also be happier by recognizing and getting comfortable
with the uncertainty of the world. Instead of living at extremes, we can find
contentment with doing our best under uncertain circumstances, and being
committed to improving from our experience.
Reaction to the 2016 election provides another strong demonstration of what
happens when we lop branches off the tree. Hillary Clinton had been favored
going into the election, and her probability of winning, based on an
accumulation of the polls, was somewhere between 60% and 70%, according to
FiveThirtyEight.com. When Donald Trump won, pollsters got the Pete Carroll
treatment, maybe no one more than Nate Silver, founder of FiveThirtyEight.com
and a thoughtful analyzer of polling data. (“Nate Silver was wrong.” “The
pollsters missed it.” “Just
like Brexit, the bookies blew it.” Etc.) The press spun
this as a certain win for Clinton, despite the Trump branch of the tree being no
mere twig at 30%–40%. By the day after the election, the Clinton branch had
been severed, only the Trump branch remained, and how could pollsters and the
polling process have been so blind?
One of the things poker teaches is that we have to take satisfaction in
assessing the probabilities of different outcomes given the decisions under
consideration and in executing the bet we think is best. With the constant stream
of decisions and outcomes under uncertain conditions, you get used to losing a
lot. To some degree, we’re all outcome junkies, but the more we wean ourselves
from that addiction, the happier we’ll be. None of us is guaranteed a favorable
outcome, and we’re all going to experience plenty of unfavorable ones. We can

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outcome, and we’re all going to experience plenty of unfavorable ones. We can
always, however, make a good bet. And even when we make a bad bet, we
usually get a second chance because we can learn from the experience and make
a better bet the next time.
Life, like poker, is one long game, and there are going to be a lot of losses,
even after making the best possible bets. We are going to do better, and be
happier, if we start by recognizing that we’ll never be sure of the future. That
changes our task from trying to be right every time, an impossible job, to
navigating our way through the uncertainty by calibrating our beliefs to move
toward, little by little, a more accurate and objective representation of the world.
With strategic foresight and perspective, that’s manageable work. If we keep
learning and calibrating, we might even get good at
it.

## Page 167

ACKNOWLEDGMENTS
The list of people who made it possible for me to write this book is long. It
starts, of course, with my family: my father Richard, and my late mother Deedy,
who inspired in me a love of games, a love of teaching, and a love of writing—a
good brew for creating this book; my brother Howard, for being a great brother
and for his obvious influence in my poker career and my dedication to treating
the art of decision-making as a science, and vice versa—many of the ideas in
this book originated from conversations with him; and my sister Katy, for being
a supportive sister, an inspiring writer and poet who I hope to impress the littlest
bit, and for doing a careful read and edit of the book, encouraging me every step
of the way.
Levine Greenberg Rostan Literary Agency and Portfolio collaborated on the
business of getting this book published. It took a lot to get me into this project,
and a lot more to get this book out of me. Wonderful people in both
organizations did so much to help me and enhance the final product:
Jim Levine, my literary agent and hand-holder, who got me through the
proposal writing and has been there every step of
the way. Jim believed in the
project from the beginning, helped me work through many different ways to
attack the material, and patiently guided me through many iterations of the
proposal. His enthusiasm for this book never wavered, and kept me sane through
my own periods of self-doubt. And to Matthew Huff, Jim’s assistant, who has
been there since the very first meeting and offered invaluable help in the
proposal-writing process.
Niki Papadopoulos, my editor and therapist, who made this book
immeasurably better in so many ways, not the least of which by suggesting a
format that forced an organization of ideas that I could not have achieved
otherwise. I’m forever grateful. And to Leah Trouwborst for, along with Niki,
providing encouragement, editorial guidance, and occasional therapy. And to
Vivian Roberson for keeping the production trains running on time.

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Everyone in these acknowledgments (and many others) encouraged me to
write this book. I’d like to single out, for specific contributions in this regard,
Dan Ariely, who also introduced me to Jim Levine, and Charles Duhigg, who
also kindly shared his book proposal for
The Power of Habit
, which was an
invaluable guide to writing my own proposal. Dan and Charles played
significant roles from the time this project was in its infancy. Both gave
generously of their time and attention, and encouraged me to believe that poker
provided valuable insight into decision-making.
Glen Clarkson’s encouragement has also been remarkable. He has tried to
get me to write this book forever. I insisted on writing about poker strategy first.
He was a noodge in the most positive sense of the term, and he was right.
Among the people who inspired me in my education, several stand out:
Lila Gleitman, my advisor at Penn, who inspired in me a love of the study of
learning and has been a role model for scientific
endeavor—intrepid, funny,
smart, insightful, and passionate about her work. She taught me to think like a
scientist and, at eighty-eight, is still the most inspiring person I know. She has
also been incredibly generous in the time since she was my advisor, especially
given that I left the program before getting my PhD. She was happy for the life I
created after leaving and never once made me feel bad for not finishing. And to
the memory of her husband Henry, who was also my mentor, a master of
experimental design, and a character who looms so large in my intellectual
development.
Barbara Landau, who got me interested in and excited about psychology at
Columbia. I am grateful for the four years I was able to spend as her research
assistant while an undergrad. She also pushed me to continue my studies at
Penn, where she had studied with Lila and Henry Gleitman.
Jon Baron, who taught my first seminar on decision-making. Bob Rescorla,
for sharing his fascination with conditioning and guiding me in the depths of
learning about learning. And all the professors at Columbia and Penn that taught
me and nurtured in me the interest and curiosity to explore science, psychology,
behavior, learning, and decision-making.
Even after I took a leave of absence from school to play poker—for twenty
years—I never left the subjects that captivated me: how we learn and what we do
with the product of our learning. I am grateful to so many people I met along the
way in poker. I appreciate the poker community for welcoming a young girl into
the game, and helping me find mentors, friends, and unforgettable characters
who enriched my time in the game. And I am grateful to the game of poker
itself, which offered me something to be passionate about, rewarding me for

## Page 169

itself, which offered me something to be passionate about, rewarding me for
discoveries about its complexity, at the same time reminding me that, for every
layer I peeled off, there were always more layers beneath it.
My appreciation for poker would not be complete without especially
thanking Erik Seidel for innumerable reasons, including showing me what it
means to really strive to be a rational thinker.
Particular thanks to David Grey, for sharing his story about Ira the Whale;
Phil Hellmuth, for uttering one of the best quotes in poker history; and John
Hennigan, for sharing his story about moving to Des Moines on a bet. These
superlative players and many others also bestowed their expertise and friendship,
a truly wonderful gift. I was lucky to watch and learn from, in addition,
remarkable players including Chris Ferguson, Doyle Brunson, Chip Reese, Gus
Hansen, Huckleberry Seed, Ted Forrest, Andy Bloch, Mori Eskandani, Phil Ivey,
Bobby Buckler, Allen Cunningham, Danny Robison, and Chau Giang. (These
and so many other wonderful players I encountered over the years weren’t just
remarkable in their skills: they were remarkable in their variety of approaches
toward the similar goal of making good decisions.)
This book would not exist without the input and feedback I received from all
the companies, conferences, professional groups, and executives who have hired
me over the years, giving me an opportunity to workshop my ideas through
keynotes, retreats, consulting, and coaching. That started with Roger Lowe, who
took a chance on inviting a poker player to speak to options traders about how
poker could inform decision-making. Without his outside-the-box request back
in 2002, this book might never have happened. Those nascent ideas I expressed
at that retreat were the small beginnings of what bloomed over time into this
book.
During the time it took to write this book, countless friends in publishing,
academics, and business shared their expertise, knowledge, and passion,
engaging me in discussions, answering my questions, and leading me to further
information:
Colin Camerer, who took time out to talk to a stranger.
Stuart Firestein, for reminding me as I wavered about whether to write this
book that uncertainty is an interesting, exciting topic. He continues to be an
inspiration to me and a great friend. His joyfulness and enthusiasm are
contagious, even if they are impossible to duplicate.
Olivia Fox Cabane, for encouraging me and enthusiastically believing
uncertainty is an interesting topic to write about.
Victoria Gray, for introducing me to so many brilliant scholars through
Adventures of the Mind (including George Dyson and Stuart Firestein) and for

## Page 170

Adventures of the Mind (including George Dyson and Stuart Firestein) and for
being such a great friend.
Jon Haidt, whom I first met when we were first-year graduate students at
Penn, for taking time out after being swamped after the 2016 election to get on
the phone with me, and for reminding me to delve back into John Stuart Mill.
Maria Konnikova, who helped me through the process of writing this book
and has shown me a fresh perspective on how poker can inspire. We both share a
crush on Erik Seidel’s intellect.
Dave Lenowitz, for his intellectual curiosity and willingness to share ideas.
Robert MacCoun, for several great conversations on the topic of outcome
blindness.
Gary Marcus, for engaging me in some long conversations on subjects that
helped form the ideas of the book. I first met Gary when we were in grad school,
when I was a student of Lila’s and he was a student of Steven Pinker’s. We
reconnected years later after I started working on this book, and the
conversations I have been lucky to have with him about memory and time were
invaluable.
Gabriele Oettingen and her husband Peter Gollwitzer, psychology professors
at NYU, who were kind enough to have a
very long lunch with me to talk about
mental contrasting. That conversation was invaluable to this book.
Gerry Ohrstrom, for reintroducing me to Gary Marcus, who in turn
introduced me to Gabriele Oettingen and Peter Gollwitzer.
Joseph Sweeney, whose love of learning and devouring of material in this
space led to many long lunch conversations that informed this book and made it
better in so many ways.
Philip Tetlock, for a conversation that became three of the most informative
hours of my life; also for encouraging me to reacquaint myself with Robert
Merton’s scientific norms.
Joseph Kable, for kindly having lunch with me to talk about the brain
circuitry recruited in imagining the future.
Thank you to all my friends and colleagues at How I Decide, an educational
nonprofit foundation I cofounded with the mission to equip youth with better
decision-making and critical thinking skills (www.howidecide.org). Thanks to
the all the dedicated folks who do the heavy lifting, executive director Dave
Lenowitz and all the staff: Dan Donaldson, Dylan Gordon, Jillian Hardgrove,
Adriana Massara, Ramin Mohajer, and Joseph Sweeney. And thank you to all
the members of the board and advisory council. In addition to so many
individual contributions from these wonderful people to the book, their hard

## Page 171

individual contributions from these wonderful people to the book, their hard
work and dedication have been a constant inspiration for me to do my best at
understanding and teaching decision-making skills.
Thanks to the following people who read early portions or drafts of this
material and offered their comments: Jim Doughan, Paul Schoemaker, T. C.
Scornavacchi, Todd Simkin, and Joseph Sweeney.
A special thanks to Michael Craig—who gave me extraordinary and
invaluable editorial help. Without him this book would
never have happened. I
am grateful to him for his professional help and for being a good friend.
I am indebted for the help of Jenifer Sarver, who literally runs my
professional life and keeps the trains running on time; I’d fall apart without her.
I’m also thankful for Luz Stable’s vital role in helping me juggle my business
responsibilities while writing this book.
I appreciate that my friends graciously put up with my being in a black hole
during the writing of this book and patiently waited for me to stick my head back
up. I canceled plans more times than I can count and the understanding I got
back will not be forgotten.
This book truly would not have happened without Eric, who puts up with
me and inspires me, both in the writing of this book and in every other way.
Thank you to my stepchildren, who have made my life so much fuller, and have
always been patient and understanding.
Just as I benefited greatly from the foundation and continuous help provided
by my parents and my siblings, my most important influence continues to be my
wonderful, exceptional children. They put up with me. They put up with this
book. Teaching them has been the goal of my life, and yet it’s impossible to
even contemplate all the things they’ve taught me. They are awesome and
inspire me every
day.

## Page 172

NOTES
INTRODUCTION : WHY THIS ISN’T A POKER BOOK
On several occasions in the book, I refer to tournament poker results. In addition to cash games, poker can
be played in a tournament format. In tournaments, players pay an entry fee and receive tournament chips,
good only within the competition. They play at assigned tables, at stakes designed to rise on a pre-
established schedule, and are eliminated when they lose all their chips. The tournament winner ends up with
all the chips, but prize money is awarded based on order of finish. My source for tournament titles and
earnings is the Hendon Mob Database (pokerdb.thehendonmob.com), which includes results from over
300,000 events going back to the first World Series of Poker in 1970.
CHAPTER 1: LIFE IS POKER, NOT CHESS
Pete Carroll and the Monday Morning Quarterbacks
:
I refer throughout the book to Pete Carroll’s play-
call at the end of the Super Bowl and the reaction. The critical stories referred to by headlines are Chris
Chase, “What on Earth Was Seattle Thinking with Worst Play Call in NFL History?,”
USA Today
, February
1, 2015, http://ftw.usatoday.com/2015/02/seattle-seahawks-last-play-interception-marshawn-lynch-super-
bowl-malcolm-butler-play-clal-pete-carroll; Mark Maske, “‘Worst Play-Call in Super Bowl History’ Will
Forever Alter Perception of Seahawks, Patriots,”
Washington Post
, February 2, 2015,
https://
www.washingtonpost.com/news/sports/wp/2015/02/02/worst-play-call-in-super-bowl-history-will-
forever-alter-perception-of-seahawks-patriots; Alex Marvez, “Dumbest Call in Super Bowl History Could
Be Beginning of the End for Seattle Seahawks,” FoxSports.com, February 2, 2015,
http://www.foxsports.com/nfl/story/super-bowl-seattle-seahawks-pete-carroll-darrell-bevell-russell-wilson-
dumbest-call-ever-020215; Jerry Brewer, “Seahawks Lost Because of the Worst Call in Super Bowl
History,”
Seattle Times
, February 1, 2015,
http://old.seattletimes.com/html/seahawks/2025601887_brewer02xml.html; and Nicholas Dawidoff, “A
Coach’s Terrible Super Bowl Mistake,”
New Yorker
, February 2, 2015,
http://www.newyorker.com/news/sporting-scene/pete-carroll-terrible-super-bowl-mistake.
The stories explaining the potentially sound rationale for the play-call are Brian Burke, “Tough Call:
Why Pete Carroll’s Decision to Pass Was Not as Stupid as It Looked,” Slate.com, February 2, 2015,
http://www.slate.com/articles/sports/sports_nut/2015/02/why_pete_carroll_s_decision_to_pass_wasn_t_the_worst_play_call_ever.html,
and Benjamin Morris, “A Head Coach Botched the End of the Super Bowl, and It Wasn’t Pete Carroll,”
FiveThirtyEight.com, February 2, 2015, https://fivethirtyeight.com/features/a-head-coach-botched-the-end-
of-the-super-bowl-and-it-wasnt-pete-carroll. The description of Pete Carroll’s appearance on the
Today
show came from Chris Wesseling, “Pete Carroll Concedes ‘Worst Result of a Call Ever,” NFL.com,
February 5, 2015, http://www.nfl.com/news/story/0ap3000000469003/article/pete-carroll-concedes-worst-
result-of-a-call-ever.
The game information and statistics are from Pro-Football-Reference.com, though many of them also
appeared in accounts and analyses of the game.

## Page 173

Quick or dead: our brains weren’t built for rationality
:
For a good overview on our problems
processing data, including assuming causation when there is only a correlation and cherry-picking data to
confirm the narrative we prefer, see the
New York Times
op-ed by Gary Marcus and Ernest Davis, “Eight
(No, Nine!) Problems with Big Data,” on April 6, 2014.
In addition to the sources mentioned in this section and additional materials cited in the Selected
Bibliography and Recommendations for Further Reading, Colin Camerer was nice enough to spend two
hours on the phone talking with me about this subject. I highly recommend watching his outstanding TEDx
Talk, “Neuroscience, Game Theory, Monkeys,” which includes a fun look at where chimpanzees are
actually better at game theory than humans.
Dr. Strangelove
:
I met historian George Dyson (son of Freeman Dyson) at a mentoring conference for
youth called Adventures of the Mind. The conference was held at the Institute for Advanced Study. During
my speech, I mentioned—as I do in practically every speech—John von Neumann and told the students that
this site was hallowed ground for me because it’s where von Neumann worked. George heard me say that
and later e-mailed me a scan of one of von Neumann’s gambling markers.
The information about von Neumann, in addition to the sources mentioned in the section (which are
cited in the Selected Bibliography and Recommendations for Further Reading), are from the following
sources: Boston Public Library, “100 Most Influential Books of the Century,” posted on
TheGreatestBooks.org; Tim Hartford, “A Beautiful Theory,”
Forbes,
December 10, 2006; Institute for
Advanced Study, “John von Neumann’s Legacy,” IAS.edu; Alexander Leitch, “von Neumann, John,”
A
Princeton Companion
(1978); Robert Leonard, “From Parlor Games to Social Science: von Neumann,
Morgenstern, and the Creation of Game Theory 1928–1944,”
Journal of Economic Literature
(1995).
The quotes from reviews that greeted
Theory of Games
are from Harold W. Kuhn’s introduction to the
sixtieth anniversary edition.
The influences behind the title character in
Dr. Strangelove
either are alluringly vague or differ based
on who’s telling (or speculating). John von Neumann shared a number of physical characteristcs with the
character and is usually cited as an influence. Others named as influences include Wernher von Braun,
Herman Kahn, Edward Teller, and Henry Kissinger. Except for Kissinger, who was a relatively obscure
Harvard professor when the film was made, these are all conceivable models.
The influence of John von Neumann on game theory, and of game theory on modern economics, is
unquestioned. At least eleven Nobel laureates in economics have been cited for their work connected with
or influenced by game theory. NobelPrize.org has cited the following eleven winners of the Prize in
Economic Sciences (formally called “The Sveriges Riksbank Prize in Economic Sciences in Memory of
Alfred Nobel”), by year, field, and contribution: (1) John C. Harsanyi, (2) John F. Nash Jr., and (3)
Reinhard Selten (1994, game theory, “for their pioneering analysis of equilibria in the theory of non-
cooperative games”); (4) Robert J. Aumann and (5) Thomas C. Schelling (2005, game theory, “for having
enhanced our understanding of conflict and cooperation through game theory analysis”); (6) Leonid
Hurwicz, (7) Eric S. Maskin, and (8) Roger B. Myerson (2007, microeconomics, “for having laid the
foundations of mechanism design theory”); (9) Alvin E. Roth and (10)
Lloyd S. Shapley (2012, applied
game theory, “for the theory of stable allocations and the practice of market design”); and (11) Jean Tirole
(2014, industrial organization, microeconomics, “for his analysis of market power and regulation”).
Poker vs. chess
:
My brother Howard came from a chess background, but the movement of players from
chess into poker is relatively rare. In my opinion, the lack of uncertainty in chess compared with the great
uncertainty in poker is a barrier to transitioning from one to the other. In contrast, during my time in poker
there was a lot of crossover between backgammon and poker. Many of the greatest poker players are also
world-class backgammon players: Chip Reese, Huckleberry Seed, Jason Lester, Gus Hansen, Paul Magriel,
Dan Harrington, and Erik Seidel. The greater crossover with backgammon likely comes from the more
prominent element of uncertainty that backgammon and poker share. Poker players have to navigate the
luck in the deal of the cards. Backgammon players have to navigate the luck in the roll of the dice.

## Page 174

A lethal battle of wits
:
The scene between Westley and Vizzini from
The Princess Bride
should be
instantly familiar to generations of movie fans. The quotes from the lethal battle of wits are actually from
the novel, though the scenes are nearly identical in both formats. The only notable difference is one instance
where author-screenwriter William Goldman and director Rob Reiner expertly adapted Vizzini’s
overconfidence to the appropriate medium. When Vizzini introduces the man in black to his indescribable
intellect, movie-Vizzini gets right to the point, reciting the names of antiquity’s greatest minds and
concluding, by comparison: “MORONS!” In the novel, Goldman has Vizzini deliver a pompous, gasbaggy
speech. This is the speech in its entirety: “There are no words to contain all my wisdom. I am so cunning,
crafty and clever, so filled with deceit, guile and chicanery, such a knave, so shrewd, cagey as well as
calculating, as diabolical as I am vulpine, as tricky as I am untrustworthy . . . well, I told you there were not
words invented yet to explain how great my brain is, but let me put it this way: the world is several million
years old and several billion people have at one time or another trod upon it, but I, Vizzini the Sicilian, am,
speaking with pure candor and modesty, the slickest, sleekest, sliest and wiliest fellow who has yet come
down the pike.”
When talking about 4 coin flips versus 10,000, I was speaking relatively. There has actually been a lot
of work done on how many times you need to flip a coin to determine if the coin is fair. If you are
interested, you can read an explanation in
Wikipedia
, s.v. “Checking Whether a Coin Is Fair,” accessed June
1, 2017, https://en.wikipedia.org/wiki/Checking_whether_a_coin_is_fair.
Redefining wrong
:
For the quotes about how, in setting odds in advance of the Brexit vote, the
bookmakers got it “wrong,” see Jon Sindreu, “Big London Bets Tilted Bookmakers’ ‘Brexit’ Odds,”
Wall
Street Journal
, June 26, 2016, https://www.wsj.com/articles/big-london-bets-tilted-bookmakers-brexit-
odds-1466976156, and Alan Dershowitz, “Why It’s Impossible to Predict This Election,”
Boston Globe
,
September 13, 2016, https://www.bostonglobe.com/opinion/2016/09/13/why-impossible-predict-this-
election/Y7B4N39FqasHzuiO81sWEO/story.html. If you are interested in more details about the confusion
that follows when you declare someone “right” or “wrong” about a prediction, I wrote on the topic right
after the Brexit vote and again before the presidential election. “Bookies vs. Bankers on Brexit: Who’s
Gambling Now?,” Huff ingtonPost.com, July 13, 2016, http://www.huffingtonpost.com/entry/bookies-vs-
bankers-on-brexit-whos-gambling-now_us_57866312e4b0cbf01e9ef902, and “Even Dershowitz? Mistaking
Odds for Wrong When the Underdog Wins,”
Huffington Post
, September 21, 2016,
http://www.huffingtonpost.com/annie-duke/even-dershowitz-mistaking_b_12120592.html.
Nate Silver and his website, FiveThirtyEight.com, bore the brunt of the criticism for pollsters and
forecasters after the 2016 presidential election. Silver’s site updated, in real time, polling and forecasting
data on the election and had (depending on the date) the probability of a Clinton victory at approximately
60%–70%. If you Google (without the quotation marks) “Nate Silver got it wrong election,” 465,000 results
come up.
Politico
’s November 9 headline was “How Did Everyone Get It So Wrong?,”
http://www.politico.com/story/2016/11/how-did-everyone-get-2016-wrong-presidential-election-231036.
Gizmodo.com jumped on Silver even before the election, in a November 4 article by Matt Novak titled
“Nate Silver’s Very Very Wrong Predictions About Donald Trump Are Terrifying,”
http://paleofuture.gizmodo.com/nate-silvers-very-very-wrong-predictions-about-donald-t-1788583912,
including the declaration, “Silver has no f**king idea.”
CHAPTER 2: WANNA BET?
Hearing is believing
:
The quote about baldness is from Susan Scutti, “Going Bald Isn’t Your Mother’s
Fault; Maternal Genetics Are Not to Blame,”
Medical Daily
, May 18, 2015,
http://www.medicaldaily.com/going-bald-isnt-your-mothers-fault-maternal-genetics-are-not-blame-333668.
There are numerous lists of such common misconceptions, such as Emma Glanfield’s “Coffee Isn’t Made
from Beans, You Can’t See the Great Wall of China from Space and Everest ISN’T the World’s
Tallest
Mountain: The Top 50 Misconceptions That Have Become Modern Day ‘Facts,’”
Daily Mail
, April 22,

## Page 175

2015, http://www.dailymail.co.uk/news/article-3050941/Coffee-isn-t-beans-t-Great-Wall-China-space-
Everest-ISN-T-worlds-tallest-mountain-Experts-unveil-life-s-50-misconceptions-modern-day-facts.html;
Wikipedia,
s.v. “List of Common Misconceptions,” accessed June 27, 2017,
https://en.wikipedia.org/wiki/List_of_common_misconceptions.
“They saw a game”
:
The quotes from the school newspapers are as they appeared in Hastorf and Cantril’s
paper.
Redefining confidence
:
When you express uncertainty to someone who knows about communicating that
way, the mutual recognition is like a light switch turning on. When I was early in the process of writing this
book, I had lunch with Stuart Firestein. We had barely exchanged pleasantries when the waiter came to take
our order. The waiter was not a native English speaker, and I have a lot of dietary restrictions that are
difficult enough to communicate to someone who shares my first language. When the waiter walked away, I
said, “Well, that’s seventy-three percent.” Stuart started laughing, because he immediately knew what I
meant. “I think it’s lower than that,” he said. “Maybe somewhere in the forties that he’s going to get your
order right.” My declaration of uncertainty invited the discussion about whether my lunch order would be
correct. That seems like a small thing, but when you express uncertainty in this way, you will invite debate
about more significant topics.
CHAPTER 3: BET TO LEARN: FIELDING THE UNFOLDING FUTURE
Working backward is hard: the SnackWell’s Phenomenon
:
In discussing classical stimulus-response
experiments, I’m referring to the legendary work by B. F. Skinner. The Selected Bibliography and
Recommendations for Further Reading includes a citation to one of his principal experiments, as well as to
an article by psychologist Ogden Lindsley describing some of Skinner’s work.
“If it weren’t for luck, I’d win every one”
:
The Institute for Advanced Study’s web page on John von
Neumann’s legacy includes von Neumann’s quote about the trees passing him at 60 mph, and how one of
them stepped into his path. William Poundstone, who told several von Neumann stories in
Prisoner’s
Dilemma,
also mentioned JvN’s driving habits.
There are several versions of transcripts and videos of the Iowa Republican presidential primary
debate on January 28, 2016, such as Team Fix, “7th Republican Debate Transcript, Annotated: Who Said
What
and What It Meant,”
Washington Post
, January 28, 2016, https://www.washingtonpost.com/news/the-
fix/wp/2016/01/28/7th-republican-debate-transcript-annotated-who-said-what-and-what-it-meant.
People watching
:
Yogi Berra has proven a fertile source for quotes on such a variety of subjects that it’s
reasonable to wonder whether he actually said all the things attributed to him. Because he wrote a book
using this astute observation as its title, I feel I’m on safe ground considering this an actual quote, or at least
one Berra adopted as his own. Note the title of Yogi’s 2008 book (written with Dave Kaplan):
You Can
Observe a Lot by Watching: What I’ve Learned about Teamwork from the Yankees and Life
.
Published information about the Bartman play and its aftermath is plentiful, and the game and the play
are available on YouTube. The behavior of the fans at Wrigley Field and the quotes appear in Alex
Gibney’s 2014 ESPN Films documentary,
Catching Hell
.
Other people’s outcomes reflect on us
:
For some of the places in which Dawkins has written about natural
selection proceeding by competition among the phenotypes of genes, see
Current Problems in Sociobiology
and
The Greatest Show on Earth
, cited in the Selected Bibliography and Recommendations for Further
Reading.
For an examination of whether people would choose to earn $70,000 in 1900 or in 2010, see (and
listen to) “Would You Rather Be Rich in 1900, or Middle-Class Now?,” NPR.org, October 12, 2010,
http://www.npr.org/sections/money/2010/10/12/130512149/the-tuesday-podcast-would-you-rather-be-
middle-class-now-or-rich-in-1900.

## Page 176

Reshaping habit
:
Ivan Pavlov’s work is well known and summarized in every form of media. I included, in
the Selected Bibliography and Recommendations for Further Reading, one of countless possible ways to
learn more about Pavlov’s ubiquitous experiments, a book by Daniel Todes titled
Pavlov’s Physiology
Factory: Experiment, Interpretation, Laboratory Enterprise
.
If you don’t watch golf on television, golf analyst and former PGA Tour pro John Maginnes described
the “blame the green” stare in “Maginnes On Tap,” Golf.SwingBySwing.com, February 13, 2013,
http://golf.swingbyswing.com/article/maginnes-on-tapgolfers-in-hollywood. Phil Mickelson’s practice drill
of making one hundred straight three-foot putts has been described by legendary golf teacher David Pelz,
who has worked with Mickelson. “Dave Pelz and the 3 Foot Putting Circle,” GolfLife.com, June 13, 2016,
http://www.golflife.com/dave-pelz-3-foot-putting-circle.
CHAPTER 4: THE BUDDY SYSTEM
“Maybe you’re the problem, do you think?”
:
You can see for yourself David Letterman’s uncomfortable
interview of Lauren Conrad from the
Late Show

with David Letterman
on October 27, 2008, on YouTube.
The web response to the interview came from the following sources: Ryan Tate, “David Letterman to
Lauren Conrad: ‘Maybe You’re the Problem,’” Gawker.com, October 28, 2008,
http://gawker.com/5069699/david-letterman-to-lauren-conrad-maybe-youre-the-problem; Ayman, “Lauren
Conrad on David Letterman,” Trendhunter.com, October 30, 2008,
https://www.trendhunter.com/trends/lauren-conrad-interview-david-letterman; and “Video: Letterman
Makes Fun of Lauren Conrad & ‘The Hills’ Cast,” Starpulse.com, October 29, 2008,
http://www.starpulse.com/video-letterman-makes-fun-of-lauren-conrad-the-hills-cast-1847865350.html.
(Warning: these websites may not still be operating or hosting their prior content.)
Not all groups are created equal
:
My point in bringing up the group approach of Alcoholics Anonymous
is to demonstrate the obvious value people can get in working on difficult habits by enlisting a group,
illustrated in part by the story of founders Bill W. and Dr. Bob. My knowledge of the details of AA is based
on commonly available public sources. These start with AA’s website (aa.org), which includes the Big
Book, the Twelve Steps, its archives and history, and its eLibrary.
I first met Erik Seidel when I went off to New York for college. Howard was part of a study group of
exceptional poker players in New York, including Erik Seidel, Dan Harrington, Steve Zolotow, and Jason
Lester. These players all went on to successful poker careers, including earning seven World Series of
Poker bracelets and combined tournament earnings of nearly $18 million—that’s
excluding
Seidel’s eight
bracelets and $32 million; it was a remarkable study pod. I met these players when I visited Howard at the
Mayfair Club, where they played backgammon and then poker, the place where they all grew up together as
poker players.
The group ideally exposes us to a diversity of viewpoints
:
The Dissent Channel is codified in the
Department of State’s Foreign Affairs Manual, 2 FAM 071-075.1,
https://fam.state.gov/fam/02fam/02fam0070.html. Its history and origins were described in several news
stories about uses of the Dissent Channel in the Obama and Trump administrations. See Joseph Cassidy,
“The Syria Dissent Channel Message Means the System Is Working,”
Foreign Policy
, June 19, 2016;
Jeffrey Gettleman, “State Dept. Dissent Cable on Trump’s Ban Draws 1,000 Signatures,”
New York Times
,
January 31, 2017; Stephen Goldsmith, “Why Dissenting Viewpoints Are Good for Efficiency,”
Government
Technology
, July 26, 2016; Neal Katyal, “Washington Needs More Dissent Channels,”
New York Times
,
July 1, 2016; and Josh Rogin, “State Department Dissent Memo: ‘We Are Better Than This Ban’ ,”
Washington Post
, January 30, 2017. For a list of the four awards for constructive dissent, see “Constructive
Dissent Awards,” AFSA.org, http://www.afsa.org/constructive-dissent-awards.
The CIA’s acknowledgment of the red-team approach in the raid on Osama bin Laden was also
mentioned in Neal Katyal’s
New York Times
op-ed, above.

## Page 177

Federal judges: drift happens
:
The details of the growing homogeneity in the chambers of Supreme Court
justices comes from Adam Liptak’s September 6, 2010,
New York Times
article, “A Sign of the Court’s
Polarization: Choice of Clerks.” Justice Thomas’s hiring practices are described in the same article. The
measure of his ideological distance from the other justices can be found in Oliver Roeder’s January 30,
2017, FiveThirtyEight.com article, “How Trump’s Nominee Will Alter the Supreme Court.” Roeder’s
article introduced me to the data from a paper Lee Epstein and colleagues wrote in the
Journal of Law,
Economics, and Organization
. Justice Thomas’s remark about his hiring practices, including his adaptation
of the famous line often attributed to Mark Twain about teaching a pig to sing, has been widely reported,
including in David Savage’s profile, “Clarence Thomas Is His Own Man,” in the
Los Angeles Times
, July 3,
2011.
Wanna bet (on science)?
:
Several studies about corporate prediction markets mention the companies
studied or those known to be testing prediction markets. See Cowgill, Wolfers, and Zitzewitz, “Using
Prediction Markets to Track Information Flows.” Some studies also refer to some of the companies
anonymously. For an example of a study doing both, see Cowgill and Zitzewitz, “Corporate Prediction
Markets, Evidence from Google, Ford, and Firm X.” Both citations appear in the Selected Bibliography and
Recommendations for Further Reading
.
CHAPTER 5: DISSENT TO WIN
CUDOS to a magician
:
I wish I had the space or the excuse to share more details from Robert K. Merton’s
remarkable life. See the following stories celebrating his fascinating life: Jason Hollander, “Renowned
Columbia Sociologist and National Medal of Science Winner Robert K. Merton Dies at 92,”
Columbia
News
, February 25, 2003, http://www.columbia.edu/cu/news/03/02/robertKMerton.html; and Michael
Kaufman,
“Robert K. Merton, Versatile Sociologist and Father of the Focus Group, Dies at 92,”
New York
Times
, February 24, 2003, http://www.nytimes.com/2003/02/24/nyregion/robert-k-merton-versatile-
sociologist-and-father-of-the-focus-group-dies-at-92.html.
Mertonian communism
: more is more:
For an account of John Madden’s attendance at Vince Lombardi’s
eight-hour seminar on one play, see Dan Oswald’s
HR Hero
blog post, “Learn Important Lessons from
Lombardi’s Eight-Hour Session,” March 10, 2014. The documentary
Lombardi
was produced by NFL
Films and HBO and initially appeared for broadcast on HBO on December 11, 2010.
Communicating with the world beyond our group
:
“Yes, and . . .” is so fundamental to group
improvisation that it might be easier to list improv texts that
don’t
start with this rule. If you don’t have any
improv texts handy, an excellent, practical description of “yes, and . . .” appears in Tina Fey’s
autobiography,
Bossypants
.
CHAPTER 6: ADVENTURES IN MENTAL TIME TRAVEL
Night Jerry
:
I learned a great deal about the neural pathways involved in imagining the future and
remembering the past from a conversation with Joe Kable, psychology professor at Penn and primary
investigator at the Kable Lab at the university. One of Joe’s studies is cited in the Selected Bibliography and
Recommendations for Further Reading, though you should consider it just an introduction to his body of
work. In addition to his work, I recommend, as a good overview for readers trying to learn more about the
subject, the
Neuron
paper by Schacter and colleagues, cited in the Selected Bibliography and
Recommendations for Further Reading.
Our collective retirement-savings shortfall has been widely reported. For some excellent overviews of
the behavioral issues involved in retirement planning and the size of the shortfall, see Dale Griffin,
“Planning for the Future: On the Behavioral Economics of Living Longer,” Slate.com, August 2013,
http://www.slate.com/articles/health_and_science/prudential/2013/08/_planning_for_the_future_is_scary_but_why_is_that.html;

## Page 178

Mary Josephs, “How to Solve America’s Retirement Savings Crisis,”
Forbes
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crisis/#163d6e9015ae; and Gillian White, “The Danger of Borrowing Money from Your Future Self,”
Atlantic
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money-from-your-future-self/391077.
For a description of the Merrill Edge app, see Bank of America’s February 26, 2014, news release,
“New Merrill Edge Mobile App Uses 3D Technology to Put Retirement Planning in Your Hands,”
http://newsroom.bankofamerica.com/press-releases/consumer-banking/new-merrill-edge-mobile-app-uses-
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A flat tire, the ticker, and a zoom lens
:
For an interview with Professor Howard, including his fascination
with flat-tire stories, see his conversation with Somik Raha, “A Conversation with Professor Ron Howard:
Waking Up,” Conversations.org, October 17, 2013.
For an examination of Warren Buffett’s market prowess and Berkshire Hathaway’s stock performance
over the last fifty years, see Andy Kiersz, “Here’s How Badly Warren Buffett Has Beaten the Market,”
Business Insider
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was re-created from stock data from Yahoo! Finance and a study in
Financial Analysts Journal
by Meir
Statman and Jonathan Scheid, “Buffett in Foresight and Hindsight.”
Tilt
:
A list of basic surfing terms can be found on http://www.surfing-waves.com/surf_talk.htm and
https://www.swimoutlet.com/guides/different-wave-types-for-surfing. For an overview of all the different
kinds of nails, go to any hardware store, or see http://www.diynetwork.com/how-to/skills-and-know-
how/tools/all-about-the-different-types-of-nails. The number of types of brain tumors was reported on
http://braintumor.org/braintumor-information/understanding-brain-tumors/tumor-types.
Reconnaissance: mapping the future
:
There are innumerable descriptions of the planning and execution
of the D-Day invasion at Normandy, so you can look practically anywhere to see this monumental example
of scenario planning in practice. One such introduction to the subject is an interview that appeared in the
Daily Beast
with naval historian Craig Symonds, in connection with the release of his 2014 book on the
subject. See Marc Wortman, “D-Day Historian Craig Symonds Talks about History’s Most Amazing
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.
Also, see Nate Silver, “14 Versions of Trump’s Presidency, from #MAGA to Impeachment,”
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Backcasting: working backward from a positive future
:
For stories describing Olmsted’s genius in the
design of Central Park and his use of backcasting, see David Allan, “Backcasting to the Future,” CNN.com,
December 16, 2015, and Nathaniel Rich, “When Parks Were Radical,”
Atlantic
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Premortems: working backward from a negative future
:
In addition to Gabriele Oettingen’s books and
published work with her husband Peter Gollwitzer (see citations in the Selected Bibliography and
Recommendations for Further Reading), I recommend that you look at her website based on an application
of mental contrasting known by the acronym WOOP (Wish, Outcome, Obstacle, Plan), WoopMyLife.org.
WOOP provides numerous practical ways to implement mental contrasting.

## Page 179

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## Page 188

INDEX
The page numbers in this index refer to the printed version of this book. The link provided will take you to
the beginning of that print page. You may need to scroll forward from that location to find the
corresponding reference on your e-reader.
AARP,
185
accountability,
129
,
130
,
135
–37,
149
,
150
,
176
,
188
,
199
,
204
accuracy,
129
,
130
,
132
–34,
136
,
138
,
139
,
145
,
150
,
155
–56,
158
,
160
,
161
,
171
Adventures of the Mind,
243
n
After-School All-Stars (ASAS),
213
–15,
217
age-progression technology,
184
–87
agreement,
173
–74
Alcoholics Anonymous (AA),
127
–28,
132
–33,
248
n
All In: The Poker Musical,

91
all-or-nothing thinking,
see
black-and-white thinking
Alou, Moises,
98
,
99
American Foreign Service Association,
139
–40
Anguished English
(Lederer),
90
n
approval,
132
–34
Arbesman, Samuel,
67
–68,
219
Ariely, Dan,
89
Armstrong, Scott,
143
Ascent of Man, The
(Bronowski),
20
assent,
173
automobile accidents,
89
–90,
101
,
111
–12
backcasting,
218
–22,
225
,
226
backgammon,
244
n
Back to the Future movies,
177
Bailenson, Jeremy,
185
baldness,
49
,
51
Bank of America Merrill Lynch,
185
Bartman, Steve,
98
–100,
114
,
229
,
247
n
baseball,
84
Bartman play in,
98
–100,
114
,
229
,
247
n
Behavioral and Brain Sciences
(
BBS
),
146
–48,
153
,
154
behavioral economics,
11
behavioral research,
107
Belichick, Bill,
6
,
48

## Page 189

beliefs,
43
,
44
,
47
–72,
76
–77,
115
,
122
,
130
abstract,
50
–52,
65
–66
accuracy of,
138
based on hearing something,
49
–53,
65
–66
betting on,
61
–62,
64
–67,
72
,
79
–80,
84
,
209
confidence in,
67
–72,
135
,
169
confirmatory thought and,
128
as contagious,
168
expressing,
170
formation of,
49
–55,
59
,
61
,
65
–66,
80
information and,
49
–53,
55
–56,
59
–61,
66
,
70
,
94
,
138
–39
and processing experience,
57
–59
smart people and,
62
–64
stubbornness of,
59
–62
Believing Brain, The
(Shermer),
11
–12
Benghazi attack,
92
Berkshire Hathaway,
191
–93
Berra, Yogi,
96
,
247
n
bet(s),
111
–14
accountability and,
135
on beliefs,
61
–62,
64
–67,
72
,
79
–80,
84
,
209
decisions as,
3
–4,
43
–45,
67
definition of,
44
on future,
79
–80,
209
against ourselves,
45
–46
winning,
130
bias,
66
,
124
,
131
–32,
139
,
157
,
181
n,

207
blind-spot,
62
–63,
125
–26
confirmatory thought and,
128
–29,
132
,
141
–48,
155
exploratory thought and,
128
hindsight,
10
,
26
,
212
,
227
–31
Rashomon Effect and,
157
–58
self-serving,
89
–96,
102
,
103
,
108
,
110
–12,
115
,
132
,
136
,
194
Binion’s Horseshoe Casino,
101
,
124
n
bin Laden, Osama,
140
,
208
–9
black-and-white thinking,
29
–31,
34
,
67
,
94
–95,
97
,
105
blackjack,
194
–96
blind-spot bias,
62
–63,
125
–26
Bosnia,
140
Bossypants
(Fey),
250
n
brain,
11
–15,
53
,
107
,
110
,
116
,
122
,
132
,
165
,
183
,
188
,
191
,
198
,
219
brain tumors,
197
Brethren, The
(Woodward and Armstrong),
143
Brexit,
31
–32,
231
,
245
n
Breyer, Justice,
143
–44
Bronowski, Jacob,
20
Buddhist monks,
110
buddy system,
124
,
178
Burger, Justice,
143
Burke, Brian,
6

## Page 190

business start-ups,
29
,
35
“but,”
173
,
207
Caldwell, Charley,
57
Camerer, Colin,
13
, 242
n
Cantril, Hadley,
57
–58
car accidents,
89
–90,
101
,
111
–12
cardiovascular disease,
55
,
164
–65
Carroll, Pete,
5
–7,
10
,
11
,
22
,
33
,
35
,
46
,
48
,
165
–66,
216
–18,
229
,
230
,
241
n
–42
n
Carstensen, Laura,
185
Catching Hell,

99
Center for Retirement Research,
184
Central Park,
220
CEO’s president-firing decision,
8
–11,
33
,
43
,
48
,
158
,
229
–30
certainty:
illusion of,
204
,
206
,
207
see also
uncertainty
characterizations,
205
chess,
20
–23,
80
,
86
,
91
,
219
,
244
n
Chicago Cubs,
98
–100,
229
children,
45
,
175
–76
Christie, Chris,
92
–93,
110
CIA,
140
,
170
Citizen Kane,

64
–65,
68
–69
Clinton, Hillary,
32
–33,
92
,
230
–31,
245
n
Cocteau, Jean,
98
coelacanth,
67
–68
coin flips,
24
–26,
244
n
communication:
of information,
166
,
173
with world beyond our group,
172
–76
communism, Mertonian,
154
–60,
206
comparison and competition,
104
,
105
,
107
,
109
,
110
,
123
,
165
compassion,
102
–3,
105
,
113
–14
self-,
206
ConAgra,
228
–29
confidence in beliefs,
67
–72,
135
,
169
overconfidence,
204
confidence intervals,
72
confirmatory thought,
128
–29,
132
,
141
–48,
155
,
205
conflicts of interest,
164
–69,
206
–7
Conrad, Lauren,
119
–21,
125
,
161
,
175
,
205
consequences,
179
–80
Constitution,
156
Crawford, Jarret,
146
Crystal Lounge,
2
,
75
,
90
CUDOS,
153
–55
disinterestedness,
154
,
155
,
164
–69
Mertonian communism,
154
–60,
206

## Page 191

organized skepticism,
154
,
155
,
169
–71,
206
,
224
universalism,
154
,
155
,
160
–64,
173
,
205
Daily Princetonian,

57
Dartmouth-Princeton football game,
56
–59
data interpretation,
63
–64,
181
n
Dawkins, Richard,
92
n,

103
D-Day,
208
debating,
168
–69
decision groups (truthseeking groups),
124
–27,
188
,
205
,
206
,
225
accountability in,
129
,
130
,
135
–37
accuracy in,
129
,
130
,
132
–34,
136
,
139
charters for,
128
–31,
133
,
150
,
157
communicating with world beyond,
172
–76
debating in,
168
–69
diversity of viewpoints in,
137
–41
rules of engagement for,
150
–51,
154
–55;
see also
CUDOS
social approval in,
132
–34
tilt and,
199
–200
decisions,
2
–4,
44
,
231
as bets,
3
–4,
43
–45,
67
consequences of,
179
–80
moving regret in front of,
186
–89
in poker,
116
,
167
,
179
,
180
,
188
,
196
–98
decision swear jar,
204
–7
deliberative mind,
12
–14,
16
,
181
n,

183
,
186
Dershowitz, Alan,
32
Des Moines, Iowa,
37
–43,
46
,
48
,
62
,
66
,
79
,
135
detail,
158
–59
devil’s advocate,
170
–71
diabetes,
55
,
164
disinformation,
60
disinterestedness,
154
,
155
,
164
–69
dissent,
128
,
137
,
139
–40,
143
,
145
,
148
,
160
,
169
–73,
212
,
225
Dissent Channel,
139
–40,
170
diversity of opinions,
60
–61,
129
–30,
137
–42,
145
,
147
–48,
150
,
171
,
172
n,

211
,
225
doctors,
81
,
96
dog-to-human age ratio,
49
,
50
,
53
Doubleday, Abner,
53
Dreber, Anna,
149
Dr. Strangelove,

19
,
243
n
Duarte, José,
146
Duhigg, Charles,
106
–7,
109
,
115
Dyson, Freeman,
243
n
Dyson, George,
243
n
Easterbrook, Frank,
227
–29
echo chambers,
128
–29,
141
,
144
–45,
206
Eli Lilly,
150

## Page 192

Ellis Island,
53
emotions,
191
,
194
–96,
198
–200
employees,
48
hiring of,
42
–43,
145
,
172
n
Epstein, Theo,
100
ESPN,
91
,
99
evolution,
11
–13,
51
–52,
64
,
110
natural selection in,
91
n
–92
n,

103
experience:
learning and,
78
–80,
82
,
88
,
89
,
91
,
93
–95
processing of,
57
–59
experts,
28
,
78
–79,
149
–50,
158
–59
exploratory thought,
128
–29,
134
extinct species,
67
–68
Facebook,
61
fake news,
60
false positives and false negatives,
12
,
52
fat,
54
–55,
62
,
85
–86,
164
–65
feedback,
78
–82
Fey, Tina,
250
n
Feynman, Richard,
72
n,

156
,
166
,
170
filter bubble,
61
Firestein, Stuart,
27
,
246
n
FiveThirtyEight.com,
6
,
32
,
230
,
245
n
flat tire,
190
–91,
194
–96,
200
Florida Marlins,
98
–100
food and diet,
54
,
202
–3,
221
fat in,
54
–55,
62
,
85
–86,
164
–65
SnackWell’s,
85
–86,
179
football,
159
Super Bowl,
5
–7,
10
,
22
,
46
,
48
,
165
–66,
216
–18,
241
n
–42
n
Foreign Service,
139
–40
FoxSports.com,
6
Freedom of Information Act,
157
Freeman Spogli Institute for International Studies,
185
free press and free speech,
156
future,
46
,
79
–81,
88
,
174
,
178
age-progression software and,
184
–87
backcasting from,
218
–22,
225
,
226
betting on,
79
–80,
209
imagining,
183
,
221
negative, working backward from,
221
–26
reconnaissance and,
207
–12,
218
retirement planning and,
182
,
184
–86,
203
scenario planning and,
209
–18
see also
outcomes; time travel, mental
gambling,
4
,
39
,
45
,
194
–96

## Page 193

negotiated settlements in,
40
slot machines,
87
–88
see also
poker
game theory,
19
–21,
23
, 242
n,

243
n
General Electric,
150
generalized characterizations,
205
genes,
49
,
83
,
91
n
–92
n,

103
Gibney, Alex,
99
Gilbert, Daniel,
50
–52,
104
goals,
82
,
84
,
108
,
111
,
174
adjusting,
226
working backward from,
220
–24
Goldman, William,
26
,
244
n
golf,
83
,
109
,
247
n
Gonzalez, Alex,
100
Google,
52
–53,
61
,
150
,
184
grain bin explosion,
228
–29
grant prospecting,
213
–15,
217
–18
gratification, immediate or delayed,
182
n,

226
Green Bay Packers,
159
Grey, David,
135
groups:
for decision making and truthseeking,
see
decision groups
reasoning styles in,
128
–29
groupthink,
128
habits,
105
–11,
115
,
117
,
133
,
134
,
160
,
188
loops in,
106
–7,
109
Haidt, Jonathan,
104
,
129
–30,
145
–46,
157
Half-Life of Facts, The
(Arbesman),
67
–68,
219
Hamm, Mia,
109
Hansen, Gus,
244
n
happiness,
104
,
231
individual moments and,
190
–91,
193
–94,
199
Happiness Hypothesis, The
(Haidt),
104
Harrington, Dan,
244
n,

248
n
Harvard Business Review,

96
,
219
Hastorf, Albert,
57
–58
Hearst, William Randolph,
60
heart disease,
55
,
164
–65
Heider, Fritz,
89
Hellmuth, Phil,
90
–91
Hennigan, John,
37
–43,
48
,
79
,
135
Heterodox Academy,
146
–49,
153
,
172
n
Hills, The,

119
hindsight bias,
10
,
26
,
212
,
227
–31
home buyers,
202
Howard, Ronald,
190
n
Huxley, Aldous,
78
–79

## Page 194

Ignorance: How It Drives Science
(Firestein),
27
immigrants,
53
,
140
improvisation,
173
–74,
207
,
250
n
information,
28
,
34
,
70
,
136
beliefs and,
49
–53,
55
–56,
59
–61,
66
,
70
,
94
,
138
–39
communication of,
166
,
173
disinformation,
60
hidden and incomplete,
20
–23,
25
,
26
,
33
–35,
45
,
81
,
87
,
158
new,
55
–56,
61
,
70
,
173
processing of,
55
,
59
,
62
,
123
,
165
sharing of,
156
–60
source of,
161
Institute for Advanced Study,
243
n,

246
n
intellectual property,
157
International Academy of Trial Lawyers (IATL),
93
Internet,
60
–61,
145
investments,
44
–45,
191
–93,
195
,
196
,
203
IQ,
147
Ira the Whale,
135
irrationality,
see
rationality and irrationality
Ivey, Phil,
105
–6,
108
,
112
,
116
n
JAMA Internal Medicine,

164
Jenner, Brody,
120
Jentz
v
. ConAgra Foods,

228
–29
jobs,
41
–46
Johnson, Hollyn,
55
Journal of Experimental Psychology,

55
Journal of Law, Economics, and Organization,

144
Journal of the American Medical Association,

55
judges,
141
–44,
147
,
148
Jussim, Lee,
146
Kable, Joe,
250
n
Kahan, Dan,
58
,
62
–64,
181
n
Kahn, Herman,
243
n
Kahneman, Daniel,
12
,
14
,
36
,
52
,
61
,
181
n
Katyal, Neal,
140
Kazmaier, Dick,
56
–57
Kissinger, Henry,
243
n
Klein, Gary,
219
Kluge: The Haphazard Evolution of the Human Mind
(Marcus),
12
–13,
52
Kubrick, Stanley,
19
Kurosawa, Akira,
157
language,
52
,
197
Late Show with David Letterman,

119
–21,
123
,
125
,
161
,
171
,
175
,
205
lawyers,
28
–29,
93
,
110
,
167
,
202
,
221
,
222
learning,
2
–3,
67
,
77
–78,
80
,
82
,
105
,
108
,
110
,
113
,
115
,
116
,
169
,
173
,
231
from experience,
78
–80,
82
,
88
,
89
,
91
,
93
–95

## Page 195

loop in,
80
,
84
,
120
poker and,
78
by watching,
96
–97,
102
Lederer, Howard,
1
–2,
101
–2,
106
,
123
–24,
133
–34,
161
–62,
244
n
Lederer, Richard,
90
n
Lerner, Jennifer,
128
–29,
132
Lester, Jason,
244
n,

248
n
Letterman, David,
119
–21,
123
,
125
,
161
,
171
,
175
,
205
,
248
n
Life of Lucullus
(Plutarch),
160
Lombardi, Vince,
159
loss aversion,
36
low-fat diet,
54
–55,
62
,
85
–86,
164
–65
luck,
4
,
7
,
10
,
11
,
21
,
22
,
34
,
35
,
46
,
79
–80,
82
,
86
–92,
94
–98,
101
,
102
,
110
,
111
,
113
,
121
,
123
,
124
,
129
–
31,
194
,
205
skill vs.,
82
–85
Ludwig, David,
54
–55
Lynch, Marshawn,
5
,
7
,
217
n
Lyubomirsky, Sonja,
104
MacCoun, Robert,
90
,
166
,
168
Madden, John,
159
Maddon, Joe,
100
Magriel, Paul,
244
n
Marcus, Gary,
12
–13,
52
Marshmallow Test,
181
n
–82
n
math skills,
64
,
181
n
Matrix, The,

122
–23,
175
–76
Mauboussin, Michael,
83
n
Maxwell, James Clerk,
27
Medical Daily,

49
mental contrasting,
223
Merrill Edge,
185
Merton, Robert C.,
153
Merton, Robert K.,
151
,
153
–55
Meserve, Russell,
62
Mickelson, Phil,
109
,
247
n
Microsoft,
150
Mill, John Stuart,
137
,
140
,
163
,
169
Mischel, Walter,
181
n
–82
n
misconceptions, common,
49
Mitchell, Deborah,
219
Monday Morning Quarterback,
7
,
8
,
229
Montag, Heidi,
119
–20
Morgenstern, Oskar,
19
,
23
Morris, Benjamin,
6
motivated reasoning,
59
–61,
63
–64,
94
,
102
,
108
,
115
,
132
,
136
,
181
n,

206
MTV,
119
–21
Müller-Lyer illusion,
14
–15
Myerson, Roger,
19
–20

## Page 196

Nabisco,
85
,
86
nails,
197
narratives,
60
–62,
95
–96,
105
,
107
–9,
157
,
160
Nash, John,
19
National Medal of Science,
154
National Science Foundation,
1
natural selection,
91
n
–92
n,

103
Nature,

166
negotiated settlements,
40
,
202
New England Journal of Medicine
(
NEJM
),
164
,
165
New England Patriots,
5
–7,
48
,
216
–18
New York,
218
–20
New Yorker,

6
,
218
–19
New York Times,

140
,
143
,
153
Nick the Greek,
75
–78,
84
,
87
,
90
,
116
Nietzsche, Friedrich,
186
,
187
,
189
Night Jerry and Morning Jerry,
180
–87
Nobel Prize,
12
,
19
–20,
36
,
153
,
166
,
243
n
–44
n
Normandy landings,
208
Obama, Barack,
140
,
146
obesity and weight gain,
55
,
85
–86,
164
Odysseus,
200
–201
Oettingen, Gabriele,
223
–24
Olmsted, Frederick Law,
220
On Liberty
(Mill),
137
Operation Overlord,
208
optimism,
226
outcomes,
78
–82,
86
,
88
,
95
,
108
,
113
–14,
134
,
166
–68,
175
,
226
,
231
blindness to,
166
–67
fielding,
82
–85,
87
,
89
–91,
95
,
103
,
105
,
111
–15,
121
,
194
,
195
,
205
negative, preparing for,
189
,
226
see also
future
Pariser, Eli,
61
past,
178
,
181
,
183
,
186
and moving regret in front of decisions,
186
–89
see also
time travel, mental
Pavlov, Ivan,
107
–8,
134
peer review,
72
,
147
–50
Pennington, Nancy,
219
Perlmutter, Saul,
166
,
168
perspective,
227
Pfizer,
150
physics,
166
pinball,
198
Pleasure of Finding Things Out, The
(Feynman),
72
n
Plutarch,
160

## Page 197

poker,
1
–4,
7
,
15
–18,
28
,
30
–31,
33
,
35
,
37
–38,
43
,
47
,
66
–67,
75
,
81
–82,
90
–91,
101
–3,
105
–6,
111
,
115
,
116
,
123
–24,
129
,
167
,
219
,
231
belief formation and,
53
chess vs.,
20
–23,
80
,
244
n
decisions in,
116
,
167
,
179
,
180
,
188
,
196
–98
diversity of opinions and,
139
learning and,
78
long hours of playing,
188
–89
loss limits in,
136
–37,
187
napkin list of hands in,
101
–2,
161
–62
possible futures and,
211
scoreboard in,
196
seminars on,
167
six and seven of diamonds in,
53
,
59
–60,
121
strategic plans and long view in,
179
,
180
,
200
strategy group for,
124
,
126
–27,
131
,
133
–34,
136
–37,
155
,
167
,
174
suited connectors in,
53
–54
Texas Hold’em,
53
tilt in,
197
–98
time constraints in,
179
tournaments,
241
n
watching in,
97
workshopping in,
158
–59
political beliefs,
63
–64,
141
–45,
162
–63,
205
social psychologists and,
145
–47
Pollan, Michael,
85
pollsters,
32
,
230
–31,
245
n
Poundstone, William,
19
,
246
n
Powell, Justice,
143
Power of Habit, The
(Duhigg),
106
–7
Pratt, Spencer,
119
–20
precommitments (Ulysses contracts),
200
–203,
212
,
221
decision swear jar,
204
–7
Predictably Irrational
(Ariely),
89
n
prediction markets,
149
–50
premortems,
221
–26
president-firing decision,
8
–11,
33
,
43
,
48
,
158
,
229
–30
presidential election of 2016,
32
–33,
61
n,

230
–31,
245
n
Princess Bride, The,

23
–26,
244
n
Princeton Alumni Weekly,

57
Princeton-Dartmouth football game,
56
–59
Prisoner’s Dilemma
(Poundstone),
19
,
246
n
privacy,
157
Prospect Theory,
36
Prudential Retirement,
185
psychology,
145
–47,
149
Pulitzer, Joseph,
60
p-values,
72

## Page 198

Rashomon,

157
Rashomon Effect,
157
–58
rationality and irrationality,
11
,
43
,
51
,
64
,
181
n,

183
,
204
Ulysses contracts and,
201
,
203
words, phrases, and thoughts that signal irrationality,
204
–7
rats,
87
reconnaissance,
207
–12,
218
red teams,
140
,
170
–71
Reese, Chip,
244
n
reflexive mind,
12
–14,
16
,
181
n
regret,
186
–89,
212
,
225
,
230
Rehnquist, Justice,
143
Reiner, Rob,
244
n
relationships,
195
,
196
,
199
,
223
relocating,
38
–43,
45
,
46
Reproducibility Project: Psychology,
149
–50
resulting,
7
–11,
26
,
166
Rethinking Positive Thinking: Inside the New Science of Motivation
(Oettingen),
223
retirement,
182
,
184
–86,
203
Righteous Mind, The: Why Good People Are Divided by Politics and Religion
(Haidt),
129
–30
risk,
20
,
34
,
39
,
42
–44,
46
–47,
66
,
111
,
179
Roberts, Justice,
143
Russo, J. Edward,
219
sales,
167
,
215
–16
Scalia, Justice,
143
,
144
scenario planning,
209
–18
schadenfreude,
102
–3
Schwarzenegger, Arnold,
213
science,
67
–68,
72
,
147
,
149
–51,
154
–55
bias and,
166
peer review and,
72
,
147
–50
sharing in,
155
–56
Seattle Seahawks,
5
–7,
22
,
46
,
48
,
216
–18
Seattle Times,

6
Seed, Huckleberry,
244
n
Seidel, Erik,
3
,
123
–24,
130
–31,
244
n,

248
n
Seifert, Colleen,
55
Seinfeld, Jerry,
180
–87
self-compassion,
206
self-expression,
156
self-image and self-deception,
89
,
123
Selfish Gene, The
(Dawkins),
92
n
self-narratives,
see
narratives
self-serving bias,
89
–96,
102
,
103
,
108
,
110
–12,
115
,
132
,
136
,
194
September
11
attacks,
140
settlements, negotiated,
40
,
202
Shepperd, James,
95
n
Shermer, Michael,
11
–12

## Page 199

shooting the messenger or message,
160
–64,
205
Siemens,
150
Silver, Nate,
32
–33,
209
–10,
230
,
245
n
skepticism,
154
,
155
,
169
–71,
206
,
224
skill,
7
,
9
,
48
–49,
88
–91,
94
–96,
103
,
111
,
113
,
121
,
122
luck vs.,
82
–85
Skinner, B. F.,
246
n
Slate.com,
6
slot machines,
87
–88
smart, being,
62
–64,
147
SnackWell’s,
85
–86,
179
Social and Personality Psychology Compass,

95
n
social approval,
132
–34
social contract,
121
,
125
social media,
60
–61,
148
social psychologists,
145
–47
social scientists,
172
n
sociology,
154
Sotomayor, Justice,
144
sports,
108
–9
baseball,
see
baseball
football,
see
football
golf,
83
,
109
Stanford Law Review,

58
Stanford University,
181
n
–82
n,

185
Stanovich, Keith,
62
start-up companies,
29
,
35
State Department,
139
–40,
170
statistics and data interpretation,
63
–64,
181
n
Stockholm School of Economics,
149
stock tickers, ticker watching,
191
–93,
196
,
199
,
200
strategic thinking,
211
Stumbling on Happiness
(Gilbert),
50
,
52
,
104
Success Equation, The: Untangling Skill and Luck in Business, Sports, and Investing
(Mauboussin),
83
n
sugar,
54
,
85
,
164
–65
suited connectors,
53
–55
Sunstein, Cass,
141
–42
Super Bowl,
5
–7,
10
,
22
,
46
,
48
,
165
–66,
216
–18,
241
n
–42
n
Supreme Court,
142
–44
surfers,
197
swear jar, decision,
204
–7
sweeping terms,
205
Syria,
140
System
1
and System
2
,
12
,
181
n,

183
,
203
Teller, Edward,
243
n
temporal discounting,
181
–83,
226
10-10-10 process,
188
–89,
191
,
199
Tetlock, Phil,
126
n,

128
–29,
132
,
146

## Page 200

Texas Hold’em,
53
Theory of Games and Economic Behavior
(von Neumann and Morgenstern),
19
“They Saw a Game: A Case Study” (Hastorf and Cantril),
56
–59
Thinking, Fast and Slow
(Kahneman),
12
,
52
Thomas, Justice,
144
Thoreau, Henry David,
186
ticker watching,
191
–93,
196
,
199
,
200
tilt,
197
–200
Time,

56
Timecop,

177
–78
time travel, mental,
176
,
177
–231
backcasting,
218
–22,
225
,
226
decision swear jar,
204
–7
flat tire scenario and,
190
–91,
194
–96,
200
moving regret in front of decisions,
186
–89
Night Jerry and Morning Jerry,
180
–87
perspective and,
227
premortems,
221
–26
temporal discounting,
181
–83,
226
10-10-10 process,
188
–89,
191
,
199
tilt and,
197
–200
and time as tree,
227
–31
Ulysses contracts (precommitment),
200
–203
see also
future; past
Today,

7
Trivers, Robert,
91
n
–92
n
Trump, Donald,
32
–33,
140
,
230
–31,
245
n
truth,
137
,
169
truthseeking,
55
,
70
,
108
,
110
,
112
,
115
,
117
,
120
–23,
125
,
126
,
147
,
150
–51,
156
–57,
172
,
204
,
207
accountability and,
176
agreement to engage in,
174
communication and,
172
truthseeking groups,
see
decision groups
Tulving, Endel,
178
n
–79
n
tumors,
197
Tversky, Amos,
36
Twain, Mark,
144
n
Twitter,
148
type I and type II errors,
12
,
52
Ulysses contracts,
see
precommitments
uncertainty,
20
,
26
–30,
36
,
47
,
67
–73,
80
,
87
,
94
,
115
,
139
,
170
,
230
,
231
denial of,
206
expressing,
172
–73,
246
n
hidden and incomplete information,
20
–23,
25
,
26
,
33
–35,
45
,
81
,
87
illusion of certainty,
204
,
206
,
207
luck,
see
luck
universalism,
154
,
155
,
160
–64,
173
,
205
University of Pennsylvania,
1

## Page 201

USA Today,

5
visual illusions,
14
–15,
64
,
91
visualization,
223
,
225
von Braun, Wernher,
243
n
von Neumann, John,
18
–20,
23
,
90
,
243
n,

246
n
Wall Street Journal,

32
Washington Post,

6
watching,
96
–97,
102
weight gain and obesity,
55
,
85
–86,
164
weight loss,
221
–23
Welch, Suzy,
188
Welles, Orson,
60
West, Richard,
62
White Castle,
135
Wilson, Russell,
5
,
48
,
218
,
227
winning,
112
,
130
,
160
,
224
WKRP in Cincinnati,

47
,
49
Woodward, Bob,
143
World Poker Tour,
37
World Series, Bartman play and,
98
–100,
114
,
229
,
247
n
World Series of Poker (WSOP),
1
,
2
,
37
,
90
–91,
106
,
123
n
–24
n,

248
n
“Would You Rather” game,
104
–5
wrong, being,
61
,
71
,
94
,
114
,
206
,
245
n
fear of being or suggesting,
172
–73
redefining,
30
–36,
73
“yes, and . . .,”
173
–74,
207
,
250
n
zero-sum games,
45
,
103
Zolotow, Steve,
248
n

## Page 202

*
Technically, they are continually evolving, but not fast enough to do us any good in our lifetimes.

## Page 203

*
The deal in Texas Hold’em begins with two cards, facedown, to each player. Following an initial round of
betting, all additional cards are community cards, dealt faceup. If there are two or more players remaining
after the conclusion of the betting rounds, the winner is the player who makes the highest hand from a
combination of their two hidden cards and the community cards dealt during the hand.
When players make their initial betting decision, there are still three more betting rounds and five
community cards to be dealt. Even with so many cards yet to be dealt, there is a significant advantage to
having a strong two-card combination. The best starting hand, of course, would be two aces. The worst is a
seven and a two of different suits.

## Page 204

*
In fairness, after the 2016 presidential election, Facebook is attempting to address this, as are some other
sites.

## Page 205

*
Legendary physicist Richard Feynman encapsulated this way that scientists communicate uncertainty and
how they strive to avoid the extremes of right and wrong when he said, “Statements of science are not of
what is true and what is not true, but statements of what is known to different degrees of certainty. . . .
Every one of the concepts of science is on a scale graduated somewhere between, but at neither end of,
absolute falsity or absolute truth.” (This appears in a collection of his short works,
The Pleasure of Finding
Things Out
.)

## Page 206

*
It’s impossible to have a detailed discussion about outcomes and learning without going into detail on
what’s luck and what’s skill (and what’s a combination), which I do when necessary. For a treatment that
more fully explores the differences between skill and luck, I recommend Michael Mauboussin’s
The
Success Equation: Untangling Skill and Luck in Business, Sports, and Investing
, along with other sources
cited in the Selected Bibliography and Recommendations for Further Reading.

## Page 207

*
Ariely, a professor of psychology and behavioral economics at Duke University, is simultaneously a
leading researcher in the discipline of behavioral economics and responsible for introducing millions of
people to the practical aspects of behavioral economics through popular TED Talks, best-selling books, a
blog, a card game, and even an app. His most popular book is titled
Predictably Irrational
.

## Page 208

*
I lifted these from an article by Robert MacCoun (described in the following paragraph) and repeat them
without guilt. First, they are incredibly amusing and informative; the greater crime would be not sharing
them. Second, MacCoun acknowledged that he got them from the book
Anguished English
, written by my
father, Richard Lederer.

## Page 209

*
Because self-serving bias promotes an inaccurate view of the world, it raises the question of how self-
serving bias has survived natural selection. There may be an evolutionary basis for this potentially costly
self-deception. People who are self-confident attract better mates, improving the chances their genes get
passed on. Because we are good at detecting deception, to deceive others about our self-confidence, we had
to first deceive ourselves. As evolutionary biologist Robert Trivers noted in his foreword to the original
1976 edition of Richard Dawkins’s
The Selfish Gene
, the evolution of self-deception is much more
complicated than previously imagined. “Thus, the conventional view that natural selection favors nervous
systems which produce ever more accurate images of the world must be a very naïve view of mental
evolution.” Dawkins, in turn, considered Trivers, for his work, one of the heroes of his groundbreaking
book, devoting four chapters of
The Selfish Gene
to developing Trivers’s ideas.

## Page 210

*
This is a systematic bias, not a guarantee that we
always
grab credit or
always
deflect blame. There are
some people, to be sure, who exhibit the opposite of self-serving bias, treating everything bad that happens
as their fault and attributing anything good in their lives to luck. That pattern is much rarer (and more likely
in women). Several sources in the Selected Bibliography and Recommendations for Further Reading
describe these aspects of self-serving bias. James Shepperd and colleagues, in particular, surveyed the
literature in
Social and Personality Psychology Compass
for the motivations and explanations behind self-
serving bias. Their survey includes research on self-serving bias in women. In addition to being a potential
symptom of depression, that pattern isn’t any better because it is equally inaccurate. All bad things can’t be
your fault and all good things can’t be due to luck, just as the reverse can’t be true. If we can’t find a way to
value accuracy in fielding outcomes, we are going to throw away a lot of learning opportunities regardless
of which kind of error we make.

## Page 211

*
The Cubs had an outstanding season in 2015 and won the World Series in 2016. Since the day after the
2003 incident, Bartman had refused all opportunities to comment or become part of the subsequent story—
that is until August 2017, when the Cubs offered and Bartman accepted a World Series ring. Bartman used
the opportunity to issue a statement about how we treat each other. NPR.org quoted part of his statement:
“‘Although I do not consider myself worthy of such an honor,’ Steve Bartman said in a statement, ‘I am
deeply moved and sincerely grateful . . . I humbly receive the ring not only as a symbol of one of the most
historic achievements in sports, but as an important reminder for how we should treat each other in today’s
society.’”

## Page 212

*
Ivan Pavlov’s work was so revolutionary that “behavioral research” as we commonly understand it didn’t
even exist. Pavlov was a physician and physiologist, researching the canine digestive system.

## Page 213

*
These numbers are obviously made up but are at least a decent approximation in reality. If the worst poker
player in the world finds 0 of 100 learning opportunities, the best poker player in the world is nowhere near
100 for 100. Remember, Phil Ivey (who has earned over $20 million in tournament poker and potentially a
much higher figure in high-stakes cash games) still obsesses over mistakes he made in some of his biggest
triumphs.

## Page 214

*
Erik is one of the best and most respected poker players of all time. He has won (at this writing) eight
World Series of Poker championship bracelets and over $30 million in tournament poker winnings. When I
started playing in the World Series at Binion’s Horseshoe in the early nineties, he had already won events
there three years running.

## Page 215

*
Thanks to Phil Tetlock for giving me that great turn of phrase.

## Page 216

*
Thomas was paraphrasing a quote often attributed to Mark Twain: “Never try to teach a pig to sing. It
wastes your time and annoys the pig.”

## Page 217

*
When we are in a position of influence over an enterprise’s hiring and culture, the same ideas apply.
Hiring to a truthseeking charter and shaping a culture that rewards people for exploratory thought and
expression of diverse viewpoints will serve an enterprise well. In fact, if we don’t actively promote such a
policy, we risk
discouraging
truthseeking due to people with diverse viewpoints feeling isolated or selecting
out. One of the chief concerns of Heterodox Academy is figuring out how to get more conservatives to
become social scientists or engage in exploratory thought with social scientists. That’s a tough sell: no one
likes the idea of being the lone holdout in a real-life version of
12 Angry Men
, especially with their
reputation and livelihood at stake.

## Page 218

*
There is an entire field of study on mental time travel and its benefits to decision-making. Neuroscientist
Endel Tulving, a psychology professor at the University of Toronto, pioneered analysis and research into
chronesthesia, the term for mental time travel through the ability to be aware of our past or future. For
further material on the neuroscience of time travel and its decision-making benefits, see the Selected
Bibliography and Recommendations for Further Reading.

## Page 219

*
Of course, being in deliberative mind is no guarantee of rationality. As I’ve noted regarding Dan Kahan’s
work on motivated reasoning, people performing complicated tasks with statistics—clearly a deliberative-or
System 2–type task—were susceptible to reasoning to make the math come out consistent with their prior
beliefs. And the people with the most math skill had the strongest tendency to do that. Daniel Kahneman
has also recognized that System 2 should not be considered immune to bias.
We are capable of all kinds of irrationality in deliberative mind. If we get out of reflexive mind, however,
we can reduce the likelihood of emotionally driven decisions and decrease the influence of bias through
self-reflection and vigilance. One way to do this is to take advantage of mental time-travel strategies.

## Page 220

*
From four-year-olds to adults, temporal discounting is a universal issue. The most famous experiment
about the difficulty (and importance) of being patient, known as the Marshmallow Test, was performed by
professor Walter Mischel and colleagues at Stanford starting in the early 1960s. At Stanford’s Bing Nursery
School, they offered children a choice between a smaller reward (like one marshmallow) that they could
have immediately, or a larger reward (like two marshmallows) if they were willing to wait, alone, for up to
twenty minutes. The children used every imaginable trick to wait for the larger reward. They made faces,
covered their eyes, turned their chairs around, cupped their hands around the marshmallow without
touching it, covered their mouths, smelled the marshmallow, and carried on wordless conversations (from
nearly imperceptible admonitions to animated arguments). Mischel and his colleagues saw struggles that
“could bring tears to your eyes, have you applauding their creativeness and cheering them on, and give you
fresh hope” for the potential of young children.
Subsequent studies following up on the marshmallow kids have shown that the ability to delay
gratification is correlated with markers of success throughout adolescence and into adulthood: higher SAT
scores, better social and cognitive functioning ratings, lower body mass index, lower likelihood of
addiction, better sense of self-worth, and higher ability to pursue goals and adapt to frustration and stress.

## Page 221

*
For a good overview on the research in this area, see “The Future of Memory: Remembering, Imagining,
and the Brain,” by Daniel Schacter and colleagues, cited in the Selected Bibliography and
Recommendations for Further Reading.

## Page 222

*
Professor Ronald Howard, director of the Decisions and Ethics Center at Stanford and the founder of
decision analysis, uses countless entertaining variations of how decision bias gets exposed in the common
but bothersome flat-tire situation. My favorite is his version where a guy gets a flat tire in front of a mental
hospital. A patient from the hospital watches through the fence as the guy, affected by having an audience,
steps on the hub cap holding the four nuts from the tire he removed, and they roll down a sewer. The guy
feels angry, flustered, helpless. The patient calls through the fence, “Why don’t you remove one nut from
each of the other three tires and put those three on the spare?” The guy says, “That’s a brilliant idea. What
are you doing in a place like this?” The patient tells him, “I may be crazy, but I’m not stupid.”

## Page 223

*
Tilt doesn’t just result from bad outcomes, although that is the more likely impetus. Poker players also
talk about winner’s tilt, where a series of good outcomes distorts decision-making, particularly in causing a
player to play as if their win rate is not a momentary fluctuation from the mean but will continue at that rate
in the future. In the euphoric, in-the-moment feeling of a big uptick, winners can make irrational in-game
decisions or overestimate their level of skill and accomplishment and commit themselves to play for higher
stakes.

## Page 224

*
I joined the ASAS board of directors in 2009. The consulting was part of my work as a board member.

## Page 225

*
I was asking them, for each grant, to calculate the expected value, which is the average long-run value
calculated by multiplying the probability of each possible outcome by the likelihood each outcome will
occur and taking the sum of those values.

## Page 226

*
NFL teams have the advantage of advanced analytics, but a fan with basic knowledge of the game can
plug in general probabilities. (These don’t include adjustment for New England’s defense or data specific to
short-yardage situations.) If Wilson drops back to pass, he is about 8% to get sacked (significant loss of
yardage, use of the final time-out), 55% to complete the pass (touchdown), 35% to throw incomplete
(stopping the clock for two additional plays, which can include an unsuccessful running play because of the
remaining time-out), and 2% to throw an interception.
If Wilson hands off to Marshawn Lynch, he either gains a yard and scores, is stopped short of the goal,
or fumbles. Lynch fumbles 1%–2% of the time. After that, advanced analytics are necessary (and may be
based on small samples). But we can guess. On fourth-down running plays (presumably short-yardage
situations), in 13 career attempts he has 2 touchdowns and 7 first downs. For his career, at the time of this
writing, on third-or fourth-and-short rushing plays, he has 121 attempts, 11 touchdowns, and 70 first downs.

## Page 227

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