How We Decide

How We Decide by Jonah Lehrer Page A

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Authors: Jonah Lehrer
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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

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