This is hard, because success is easier to stare at. People tend to avoid asking friends who were just fired rude questions; we’re hesitant to interrogate divorced colleagues about what precisely went wrong. But calibrating your base rate requires learning from both the accomplished and the humbled.
So the next time a friend misses out on a promotion, ask him why. The next time a deal falls through, call up the other side to find out what you did wrong. The next time you have a bad day or you snap at your spouse, don’t simply tell yourself that things will go better next time. Instead, force yourself to really figure out what happened.
Then use those insights to forecast more potential futures, to dream up more possibilities of what might occur. You’ll never know with 100 percent certainty how things will turn out. But the more you force yourself to envision potential futures, the more you learn about which assumptions are certain or flimsy, the better your odds of making a great decision next time.
Annie knows a lot about Bayesian thinking from graduate school, and she uses it in poker games. “When I play against someone I’ve never met before, the first thing I do is start thinking about base rates,” she told me. “To someone who has never studied Bayes’ rule, the way I play might seem like I’m prejudiced, because if I’m sitting across from, say, a forty-year-old businessman, I’m going to assume all he cares about is telling his friends he played against pros and he doesn’t really care about winning, so he’ll take lots of risks. Or, if I’m sitting across from a twenty-two-year-old in a poker T-shirt, I’m going to assume he learned to play online so he’s got a tight, limited game.
“But the difference between prejudice and Bayesian thinking is that I try to improve my assumptions as we go along. So once we start playing, if I see that the forty-year-old is a great bluffer, that might mean he’s a professional hoping everyone will underestimate him. Or, if the twenty-two-year-old is trying to bluff every hand, it probably means he’s some rich kid who doesn’t know what he’s doing. I spend a lot of time updating my assumptions because, if they’re wrong, my base rate is off.”
With Annie’s brother out of the competition, there are only two players left at the Tournament of Champions table: Annie and Phil Hellmuth. Hellmuth is a card room legend, a television celebrity known as “the Poker Brat.” “I’m the Mozart of poker,” he told me. “I can read other players probably better than anyone playing, maybe anyone in the world. It’s white magic, instinct.”
Annie is at one end of the table, Hellmuth at the other. “I had a good idea of how Phil viewed me at that point,” Annie said later. “He’s told me before that he has a low opinion of my creativity, that he thinks I’m more lucky than smart, that I’m too scared to bluff when it matters.”
That’s a problem for Annie, because she wants Phil to think she’s bluffing. The only way she can lure him into a big pot is by convincing him she’s bluffing when, in fact, she isn’t. To win this tournament, Annie needs to force Phil to change his assumptions of her.
Phil, though, has a different plan. He believes he’s the stronger player. He believes he can read Annie. “I have this capacity to learn very, very quickly,” he told me. “When I know what people are doing, I can control the table.” Those aren’t idle boasts. Hellmuth has won fourteen poker championships.
Annie and Phil have roughly equal piles of chips. For the next hour, they play hand after hand, neither gaining a clear advantage. Phil keeps subtly trying to throw Annie off, to make her mad or lose her cool.
“I would have preferred to play your brother,” he says.
“This is all right,” Annie replies. “I’m just happy to be in the finals.”
Annie bluffs Phil four times. “I wanted him to reach the breaking point where he says, ‘Screw this, she’s bluffing me hand after hand and I gotta fight back,’ ” Annie said. But Phil doesn’t seem shaken. He doesn’t overreact.
Finally, Annie gets the hand she’s been waiting for. The dealer gives her a king and a nine. Phil receives a king and a seven. In the middle of the table, the dealer lays down a communal king, six, nine, and jack.
Phil knows he has a pair of kings. But unbeknownst to him, Annie has two pair—kings and nines. Neither sees what the other is holding.
It’s Annie’s bet, and she raises $120,000. Phil, thinking his pair of kings is likely the strongest hand at the table, matches it. Then Annie goes all in, bringing the pot to $970,000.
The bet is now to Phil.
He starts muttering to himself. “This is unbelievable,” he says out loud. “Really unbelievable. She might not even know how strong I am here. I’m not sure she fully even understands the value of the hand.”
He stands up.
“I don’t know,” he says, pacing around the table. “I don’t know, I have a bad feeling about this hand.” He folds.
Phil flips over his king, showing Annie that he had a pair. Then Annie strikes: She casually turns over one of her cards—but not both—showing Phil her pair of nines, but not revealing that she also had a pair of kings.
“I wanted to force him to change his assumptions about me,” Annie later said. “I wanted him to think I was bluffing with a pair of nines.”
“Wow, did you really just push in with a nine?” Phil says to Annie. “That’s so reckless, especially against someone like me. Maybe I acted too fast.”
The players ready for the next hand. Annie has $1,460,000 in chips; Phil has $540,000. The dealer gives them their cards. Annie has a king and a ten; Phil a ten and an eight. The first communal cards come out as a two, ten, and seven.
Phil has a pair of tens, with an eight backing it up. It’s a good hand. Annie also has a pair of tens, with a king, slightly better.
Phil pushes $45,000 into the pot. Annie raises $200,000. It’s an aggressive move. But Phil is starting to believe that Annie is playing recklessly. He thinks he sees a pattern he didn’t expect from her: She’s bluffing and bluffing and bluffing again. Phil’s base rate is gradually shifting.
Phil looks at the pile of chips on the table. Maybe his assumption that Annie is too scared to bluff at critical moments is wrong? Maybe Annie is bluffing right now? Maybe she’s finally overplayed her hand?
“I’m all in,” Phil says, pushing his stack into the middle of the table.
“I call,” Annie says.
Both players turn over their cards.
“Shit,” Phil says, seeing that they both have a pair of tens—and that Annie has the high card, a king to Phil’s eight.
The dealer puts a seven on the table, benefiting neither player.
Annie is now standing, gripping her cheeks. Phil is also on his feet, breathing hard. “Give me an eight, please,” he says. It’s the only card that will keep him in the game. The dealer turns over the final communal card. It’s a three.
Annie wins the $2 million. Phil is out. The game is over. Annie is the champion.
Later, she will tell people that winning this tournament changed her life. It made her, in effect, the most famous female poker player on earth. In 2010, she went on to win the National Heads-Up Poker Championship. Today, she holds a record for World Series of Poker profits. In total, she’s won more than $4 million. She doesn’t worry about her mortgage anymore. She doesn’t have panic attacks. In 2009, she appeared on a season of Celebrity Apprentice. She was a little nervous before the filming started, but not too much. There were no anxiety breakdowns. She doesn’t play in many poker tournaments these days. She spends most of her time giving lectures to businesspeople about how to think probabilistically, about how to embrace uncertainty, about how, if you commit to a Bayesian outlook, you’ll make better decisions in life.
“A lot of poker comes down to luck,” Annie told me. “Just like life. You never know where you’ll end up. When I checked myself into the psych hospital my sophomore year, there’s no way I would have guessed I would end up as a professional poker player. But you have to be comfortable not knowing exactly where life is going. That’s h
ow I’ve learned to keep the anxiety away. All we can do is learn how to make the best decisions that are in front of us, and trust that, over time, the odds will be in our favor.”
How do we learn to make better decisions? In part, by training ourselves to think probabilistically. To do that, we must force ourselves to envision various futures—to hold contradictory scenarios in our minds simultaneously—and then expose ourselves to a wide spectrum of successes and failures to develop an intuition about which forecasts are more or less likely to come true.
We can develop this intuition by studying statistics, playing games like poker, thinking through life’s potential pitfalls and successes, or helping our kids work through their anxieties by writing them down and patiently calculating the odds. There are numerous ways to build a Bayesian instinct. Some of them are as simple as looking at our past choices and asking ourselves: Why was I so certain things would turn out one way? Why was I wrong?
Regardless of our methods, the goals are the same: to see the future as multiple possibilities rather than one predetermined outcome; to identify what you do and don’t know; to ask yourself, which choice gets you the best odds? Fortune-telling isn’t real. No one can predict tomorrow with absolute confidence. But the mistake some people make is trying to avoid making any predictions because their thirst for certainty is so strong and their fear of doubt too overwhelming.
If Annie had stayed in academics, would any of this have mattered? “Absolutely,” she said. “If you’re trying to decide what job to take, or whether you can afford a vacation, or how much you need to save for retirement, those are all predictions.” The same basic rules apply. The people who make the best choices are the ones who work hardest to envision various futures, to write them down and think them through, and then ask themselves, which ones do I think are most likely and why?
Anyone can learn to make better decisions. We can all train ourselves to see the small predictions we make every day. No one is right every time. But with practice, we can learn how to influence the probability that our fortune-telling comes true.
* * *
*1 Poker is a game of odds within odds. While this example provides an explanation of probabilistic thinking (and the concept of “pot odds”), it is worth noting that a full analysis of this hand is slightly more complex (and would take into account, for instance, the other players at the table). For a more nuanced analysis, please see the notes for chapter 6.
*2 Bayes’ rule, which was first postulated by the Reverend Bayes in a posthumously published 1763 manuscript, can be so computationally complex that for centuries most statisticians essentially ignored the work because they lacked tools to perform the calculations it demanded. Starting in the 1950s, however, as computers became more powerful, scientists found they could use Bayesian approaches to forecast events that were previously thought unpredictable, such as the likelihood of a war, or the odds that a drug will be broadly effective even if it has only been tested on a handful of people. Even today, though, calculating a Bayesian probability curve can, in some cases, tie up a computer for hours.
INNOVATION
How Idea Brokers and Creative Desperation Saved Disney’s Frozen
The audience starts lining up an hour before the screening room doors open. They are directors and animators, story editors and writers, all of them Disney employees, all eager to see a rough draft of the movie everyone is talking about.
As they settle into their chairs and the lights dim, two sisters appear on the screen against an icy landscape. Anna, the younger character, quickly establishes herself as bossy and uptight, obsessed with her upcoming wedding to the handsome Prince Hans and her coronation as queen. Elsa, her older sister, is jealous, evil—and cursed. Everything she touches turns to ice. She was passed over for the throne because of this power and now, as she runs away from her family to a crystal palace high in the mountains, she nurses a bitter grudge. She wants revenge.
As Anna’s wedding day approaches, Elsa plots with a snarky snowman named Olaf to claim the crown for herself. They try to kidnap Anna but their plan is foiled by the square-jawed, dashing Prince Hans. Bitter Elsa, in a rage, orders an army of snow monsters to descend upon the town and destroy it. The villagers repel the invaders, but when the smoke clears, casualties are discovered: Princess Anna’s heart has been partially frozen by her evil sister—and Prince Hans is missing.
The second half of the film follows Anna as she searches for her prince, desperately hoping that his kiss will heal her damaged heart. Meanwhile, Elsa prepares to attack again—and this time floods the village with vicious snow creatures. The monsters, however, are soon out of her control. They begin to threaten everyone, including Elsa herself. The only way to survive, Anna and Elsa realize, is for them to join forces. Through cooperation, they defeat the creatures and the sisters learn that working together is better than struggling apart. They become friends. Anna’s heart thaws. Peace returns. Everyone lives happily ever after.
The name of the movie is Frozen, and it is scheduled to be released in just eighteen months.
Normally, when a movie screening ends at Disney, there’s applause. Often, people cheer or shout. There are usually boxes of tissues inside the screening room because, at Disney, a good cry is the sign of a successful film.
This time, there is no crying. There are no cheers. The tissues go untouched. As everyone files out, they are very, very quiet.
—
After the screening ended, the film’s director, Chris Buck, and about a dozen other Disney filmmakers gathered in one of the studio’s dining rooms to discuss what they had just seen. This was a meeting of the studio’s “story trust,” a group responsible for providing feedback on films as they go through production. As the story trust prepared to discuss the latest draft of Frozen, people served themselves from a buffet of Swedish meatballs. Buck didn’t get anything to eat. “The last thing I was feeling was hungry,” he told me.
Disney’s chief creative officer, John Lasseter, kicked off the conversation. “You’ve got some great scenes here,” he said, and mentioned some of the things he particularly liked: The battles were thrilling. The dialogue between the sisters was witty. The snow monsters were terrifying. The film had a good, fast pace. “It’s an exciting movie, and the animation is going to be amazing,” he said.
And then he began listing the film’s flaws. The list was long.
“You haven’t dug deep enough,” he said after detailing a dozen problems. “There’s not enough for the audience to connect with because there’s no character to root for. Anna’s too uptight and Elsa’s too evil. I didn’t find myself liking anyone in the movie until the very end.”
When Lasseter was done speaking, the rest of the story trust chimed in, pointing out other problems: There were logical holes in the plot—why, for instance, does Anna stick with Prince Hans when he doesn’t seem like such a catch? Also, there were too many characters to keep track of. The plot twists were foreshadowed way too much. It didn’t seem believable that Elsa would kidnap her sister and then attack the town without trying something less dramatic first. Anna seemed really whiny for someone who lives in a castle, is marrying a prince, and would soon be queen. One member of the story trust—a writer named Jennifer Lee—particularly disliked Elsa’s cynical sidekick. “I f’ing hate Olaf,” she had scribbled in her notes. “Kill the snowman.”
The truth was, Buck wasn’t surprised by all the criticisms. His team had sensed the movie wasn’t working for months. The film’s screenwriter had restructured the script repeatedly, first with Anna and Elsa as strangers rather than sisters, then with Elsa, the cursed sister, assuming the throne and Anna upset at being a “spare, rather than an heir.” The songwriters on the film—a husband-and-wife team behind such Broadway hits as Avenue Q and The Book of Mormon—were exhausted from writing and discarding song after song. They said they couldn’t figure out how to make jealousy and revenge into lighthearted themes.
There were versions
of the movie where the sisters were normal townspeople rather than royalty, and others where the sisters reconciled over a shared love of reindeer. In one script, they were raised apart. In another, Anna was jilted at the altar. Buck had introduced characters to explain the origins of Elsa’s curse, and had tried creating another love interest. Nothing worked. Every time he solved one problem—by making Anna more likable, for instance, or Elsa less bitter—dozens of others popped up.
“Every movie sucks at first,” said Bobby Lopez, one of Frozen’s songwriters. “But this was like a puzzle where every piece we added upset how everything else fit. And we knew time was running out.”
While most animated projects are given four or five years to mature, Frozen was on an accelerated schedule. The movie had been in full production for less than a year, but because another Disney movie had recently collapsed, executives had moved Frozen’s release date to November 2013, just a year and a half away. “We had to find answers fast,” said Peter Del Vecho, the film’s producer. “But they couldn’t feel clichéd or like a bunch of stories jammed together. The movie had to work emotionally. It was a pretty stressful time.”
This conundrum of how to spur innovation on a deadline—or, put another way, how to make the creative process more productive—isn’t unique to filmmaking, of course. Every day, students, executives, artists, policy makers, and millions of other people confront problems that require inventive answers delivered as quickly as possible. As the economy changes, and our capacity to achieve creative insights becomes more important than ever, the need for fast originality is even more urgent.
Smarter Faster Better: The Secrets of Being Productive in Life and Business Page 20
