How We Decide (10 page)

The 76ers were shocked by the evidence. Andrew Toney, the shooting guard, was particularly hard to convince: he was sure that he was a streaky shooter who went through distinct hot and cold periods. But the statistics told a different story. During the regular season, Toney made 46 percent of all his shots. After hitting three shots in a row—a sure sign that he was "in the zone"—Toney's field-goal percentage
dropped
to 34 percent. When Toney thought he was hot, he was actually freezing cold. And when he thought he was cold, he was just getting warmed up: after missing three shots in a row, Toney made 52 percent of his shots, which was significantly higher than his normal average.

But maybe the 76ers' team was a statistical outlier. After all, according to a survey conducted by the scientists, 91 percent of serious NBA fans believed in the hot hand. They just
knew
that players were streaky. So Tversky and Gilovich decided to analyze another basketball team: the Boston Celtics. This time, they looked at free-throw attempts too, not just field goals. Once again, they found absolutely no evidence of hot hands. Larry Bird was just like Andrew Toney: after he made several free throws in a row, his free-throw percentage actually declined. Bird got complacent and started missing shots he should have made.

Why do we believe in streaky shooters? Our dopamine neurons are to blame. Although these cells are immensely useful—they help us predict events that are actually predictable—they can also lead us astray, especially when we are confronted with randomness. Look, for example, at this elegant little experiment: A rat was put in a T-shaped maze with a few morsels of food placed on either the far right or the far left side of the enclosure. The placement of the food was random, but the dice were rigged: over the long run, the food was placed on the left side 60 percent of the time. How did the rat respond? It quickly realized that the left side was more rewarding. As a result, it always went to the left of the maze, which resulted in a 60 percent success rate. The rat didn't strive for perfection. It didn't search for a unified theory of the T-shaped maze. It just accepted the inherent uncertainty of the reward and learned to settle for the option that usually gave the best outcome.

The experiment was repeated with Yale undergraduates. Unlike the rat, the students, with their elaborate networks of dopamine neurons, stubbornly searched for the elusive pattern that determined the placement of the reward. They made predictions and then tried to learn from their prediction errors. The problem was that there was nothing to predict; the apparent randomness was real. Because the students refused to settle for a 60 percent success rate, they ended up with a 52 percent success rate. Although most of the students were convinced that they were making progress toward identifying the underlying algorithm, they were, in actuality, outsmarted by a rat.

The danger of random processes—things like slot machines and basketball shots—i's that they take advantage of a defect built into the emotional brain. Dopamine neurons get such a visceral thrill from watching a hot player sink another jumper or from winning a little change from a one-armed bandit or from correctly guessing the placement of a food morsel that our brains completely misinterpret what's actually going on. We trust our feelings and perceive patterns, but the patterns don't actually exist.

Of course, it can be extremely hard to reconcile perceptions of streaks and runs with the statistical realities of an unruly world. When Apple first introduced the shuffle feature on its iPods, the shuffle was truly random; each song was equally as likely to get picked as any other. However, the randomness didn't
appear
random, since some songs were occasionally repeated, and customers concluded that the feature contained some secret patterns and preferences. As a result, Apple was forced to revise the algorithm. "We made it less random to make it feel more random," said Steve Jobs, the CEO of Apple.
^*
Or consider Red Auerbach, the legendary Celtics coach. After being told about Tversky's statistical analysis of the hot hand, he reportedly responded with a blunt dismissal. "So he makes a study," Auerbach said. "I couldn't care less."
^†
The coach refused to consider the possibility that the shooting streaks of the players might be a fanciful invention of his brain.

But Auerbach was wrong to disregard the study; the belief in illusory patterns seriously affects the flow of basketball games. If a team member had made several shots in a row, he was more likely to get the ball passed to him. The head coach would call a new set of plays. Most important, a player who thinks he has a hot hand has a distorted sense of his own talent, which leads him to take riskier shots, since he assumes his streak will save him. (It's the old bane of overconfidence.) Of course, the player is also more likely to miss these riskier shots. According to Tversky and Gilovich, the best shooters always think they're cold. When their feelings tell them to take the shots because they've got the hot hands, they don't listen.

THIS DEFECT IN
the emotional brain has important consequences. Think about the stock market, which is a classic example of a random system. This means that the past movement of any particular stock cannot be used to predict its future movement. The inherent randomness of the market was first proposed by the economist Eugene Fama in the early 1960s. Fama looked at decades of stock-market data in order to prove that no amount of knowledge or rational analysis could help anyone figure out what would happen next. All of the esoteric tools used by investors to make sense of the market were pure nonsense. Wall Street was like a slot machine.

The danger of the stock market, however, is that sometimes its erratic fluctuations can actually look predictable, at least in the short term. Dopamine neurons are determined to solve the flux, but most of the time there is nothing to solve. And so brain cells flail against the stochasticity, searching for lucrative patterns. Instead of seeing the randomness, we come up with imagined systems and see meaningful trends where there are only meaningless streaks. "People enjoy investing in the market and gambling in a casino for the same reason that they see Snoopy in the clouds," says the neuroscientist Read Montague. "When the brain is exposed to anything random, like a slot machine or the shape of a cloud, it automatically imposes a pattern onto the noise. But that isn't Snoopy, and you haven't found the secret pattern in the stock market."

One of Montague's recent experiments demonstrated how an unrestrained dopamine system can, over time, lead to dangerous stock-market bubbles. The brain is so eager to maximize rewards that it ends up pushing its owner off a cliff. The experiment went like this: Subjects were each given a hundred dollars and some basic information about the "current" state of the stock market. Then the players chose how much of their money to invest and nervously watched as their stock investments either rose or fell in value. The game continued for twenty rounds, and the subjects got to keep their earnings. One interesting twist was that instead of using random simulations of the stock market, Montague relied on distillations of data from history's famous markets. Montague had people "play" the Dow of 1929, the Nasdaq of 1998, the Nikkei of 1986, and the S&P 500 of 1987. This let the scientists monitor the neural responses of investors during what had once been real-life bubbles and crashes.

How did the brain deal with the fluctuations of Wall Street? The scientists immediately discovered a strong neural signal that seemed to be driving many of the investment decisions. This signal emanated from dopamine-rich areas of the brain, such as the ventral caudate, and it was encoding fictive-error learning, or the ability to learn from what-if scenarios. Take, for example, this situation: A player has decided to wager 10 percent of his total portfolio in the market, which is a rather small bet. Then he watches as the market rises dramatically in value. At this point, the fictive-error learning signal starts to appear. While he enjoys his profits, his ungrateful dopamine neurons are fixated on the profits he
missed,
as the cells compute the difference between the best possible return and the actual return. (This is a modified version of the prediction-error signal discussed earlier.) When there is a big difference between what actually happened and what might have happened—which is experienced as a feeling of regret—the player, Montague found, is more likely to do things differently the next time around. As a result, investors in the experiment adapted their investments to the ebb and flow of the market. When markets were booming, as they were in the Nasdaq bubble of the late 1990s, investors kept increasing their investments. Not to invest was to drown in regret, to bemoan all the money that might have been earned if they'd only made better decisions.

But fictive-error learning isn't always adaptive. Montague argues that these computational signals are also a main cause of financial bubbles. When the market keeps going up, people are led to make larger and larger investments in the boom. Their greedy brains are convinced that they've solved the stock market, and so they don't think about the possibility of losses. But just when investors are most convinced that the bubble isn't a bubble—many of Montague's subjects eventually put all of their money into the booming market—the bubble bursts. The Dow sinks, the Nasdaq implodes, the Nikkei collapses. All of a sudden, the same investors who'd regretted
not
fully investing in the market and had subsequently invested more were now despairing of their plummeting net worth. "You get the exact opposite effect when the market heads down," Montague says. "People just can't wait to get out, because the brain doesn't want to regret staying in." At this point, the brain realizes that it's made some very expensive prediction errors, and the investor races to dump any assets that are declining in value. That's when you get a financial panic.

The lesson here is that it's silly to try to beat the market with your brain. Dopamine neurons weren't designed to deal with the random oscillations of Wall Street. When you spend lots of money on investment-management fees, or sink your savings into the latest hot mutual fund, or pursue unrealistic growth goals, you are slavishly following your primitive reward circuits. Unfortunately, the same circuits that are so good at tracking juice rewards and radar blips will fail completely in these utterly unpredictable situations. That's why, over the long run, a randomly selected stock portfolio will beat the expensive experts with their fancy computer models. And why the vast majority of mutual funds in any given year will
underperform
the S&P 500. Even those funds that do manage to beat the market rarely do so for long. Their models work haphazardly; their successes are inconsistent. Since the market is a random walk with an upward slope, the best solution is to pick a low-cost index fund and wait. Patiently. Don't fixate on what might have been or obsess over someone else's profits. The investor who does nothing to his stock portfolio—who doesn't buy or sell a single stock—outperforms the average "active" investor by nearly 10 percent. Wall Street has always searched for the secret algorithm of financial success, but the secret is, there is no secret. The world is more random than we can imagine. That's what our emotions can't understand.

Deal or No Deal
is one of the most popular television game shows of all time. The show has been broadcast in more than forty-five different countries, from Great Britain to Slovakia to America. The rules of the game couldn't be simpler: a contestant is confronted with twenty-six sealed briefcases each full of varying amounts of cash, from a penny to a million dollars. Without knowing the amount of money in any of the briefcases, the contestant chooses a single one, which is then placed in a lockbox. Its contents won't be revealed until the game is over.

The player then proceeds to open the remaining twenty-five briefcases one at a time. As the various monetary amounts are revealed, the contestant gradually gets an idea of how much money his or her own briefcase might contain, since all the remaining amounts are displayed on a large screen. It's a nerve-racking process of elimination, as each player tries to keep as many of the big monetary sums on the board for as long as possible. Every few rounds, a shadowy figure known as the Banker makes the player an offer for the sealed briefcase. The contestant can either accept the deal and cash out or continue to play, gambling that the unopened briefcase contains more money than the Banker has offered. As the rounds continue, the tension becomes excruciating. Spouses start crying, and children begin screaming. If the wrong briefcase is picked, or the best deal is rejected, a staggering amount of money can evaporate, just like that.

For the most part,
Deal or No Deal
is a game of dumb luck. Although players develop elaborate superstitions about the briefcases—odd numbers are better; even numbers are better; ones held by blond models are better—the monetary amounts in them are randomly distributed. There is no code to crack, no numerology to decipher. This is just fate unfolding in front of a national television audience.

And yet,
Deal or No Deal
is also a game of difficult decisions. After the Banker makes an offer, the contestant has a few minutes—usually the length of a commercial break—to make up his mind. He must weigh the prospect of sure money against the chances of winning one of the larger cash prizes. It's almost always a hard call, a moment full of telegenic anxiety.

There are two ways to make this decision. If the contestant had a calculator handy, he could quickly compare the average amount of money he might expect to win against the Banker's offer. For example, if there were three remaining briefcases, one containing $1, one containing $10,000, and one containing $500,000, then the player should, at least in theory, accept any offer over $170,000, since that is the average of the money in all three briefcases. Although offers in the early rounds are generally unfairly low—the producers don't want people to quit before it gets dramatic—as the game goes on, the offers made by the Banker become more and more reasonable, until they are essentially asymptotic with the mathematical average of the money still available. In this sense, it is extremely easy for a contestant on
Deal or No Deal
to determine whether or not to accept an offer. He just needs to add up all the remaining monetary amounts, divide that number by the number of briefcases left, and see if that figure exceeds the offer on the table. If
Deal or No Deal
were played like this, it would be a thoroughly rational game. It would also be extremely boring. It's not fun to watch people do arithmetic.