The WSJ reports that the Fed is considering getting serious about popping financial bubbles:
Not so long ago, Federal Reserve officials were confident they knew what to do when they saw bubbles building in prices of stocks, houses or other assets: Nothing.
Now, as Fed Chairman Ben Bernanke faces a confirmation hearing Thursday on a second four-year term, he and others at the central bank are rethinking the hands-off approach they’ve followed over the past decade. On the heels of a burst housing-and-credit bubble, Mr. Bernanke now calls financial booms “perhaps the most difficult problem for monetary policy this decade.”
With Asian property prices soaring and gold prices busting records almost daily, the debate comes at a critical time. Mr. Bernanke wants to use his powers as a bank regulator to stamp out bubbles, but the Senate Banking Committee, which will grill him later this week, is considering stripping the Fed of its regulatory power.
This strikes me as an important move, and not just because we’re still trying to get out from under the rubble of a useless real estate boom. Financial history is largely a history of financial bubbles, from the Tulip mania of the Dutch Golden Age to the South Sea Bubble to the recent dot com disaster. We seem to be constantly careening from one irrational exuberance to another, with predictably depressing results. All is speculation.
The question, of course, is how to identify bubbles in the first place, which Bernanke associates with “undue risk taking”. Call me crazy, but I think neuroscience can help. I’d suggest that the Fed call up Read Montague, a neuroscientist at the Baylor College of Medicine, who has done some incredibly interesting work on why the brain is so vulnerable to bubbles. Montague first grew interested in bubbles by accident, after helping to decipher the mechanics of dopamine neurons, which are constantly learning about the world by making predictions – if this, then that – and then comparing these predictions to what actually happens. Here’s a snippet from How We Decide:
Although dopamine neurons excelled at measuring the mismatch between their predictions of rewards and those that actually arrived — these errors provided the input for learning — they’d learn much quicker if they could also incorporate the prediction errors of others. Montague called this a “fictive error learning signal,” since the brain would be benefiting from hypothetical scenarios: “You’d be updating your expectations based not just on what happened, but on what might have happened if you’d done something differently.” As Montague saw it, this would be a very valuable addition to our cognitive software. “I just assumed that evolution would use this approach, because it’s too good an idea not to use,” he says.
The question, of course, is how to find this “what if” signal in the brain. Montague’s clever solution was to use the stock market. After all, Wall Street investors are constantly comparing their actual returns against the returns that might have been, if only they’d sold their shares before the crash or bought Google stock when the company first went public.
The experiment went like this: Each subject was given $100 and some basic information about the “current” state of the stock market. After choosing how much money to invest, the players watched nervously as their investments either rose or fell in value. The game continued for 20 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 famous historical markets. Montague had people “play” the Dow of 1929, the Nasdaq of 1998, and the S&P 500 of 1987, so the neural responses of investors reflected real-life bubbles and crashes.
The scientists immediately discovered a strong neural signal that drove many of the investment decisions. The signal was fictive learning. Take, for example, this situation. A player has decided to wager 10 percent of her total portfolio in the market, which is a rather small bet. Then she watches as the market rises dramatically in value. At this point, the regret signal in the brain — a swell of activity in the ventral caudate, a reward area rich in dopamine neurons — lights up. While people enjoy their earnings, their brain is fixated on the profits they missed, figuring out the difference between the actual return and the best return “that could have been.” The more we regret a decision, the more likely we are to do something different the next time around. As a result investors in the experiment naturally adapted their investments to the ebb and flow of the market. When markets were booming, as in the Nasdaq bubble of the late 1990s, people perpetually increased their investments.
But fictive 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 naturally inclined to make larger and larger investments in the boom. And then, 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 collapses. At this point investors race to dump any assets that are declining in value, as their brain realizes that it made some very expensive prediction errors. That’s when you get a financial panic.
Such fictive-error learning signals aren’t relevant only for stock market investors. Look, for instance, at addiction. Dopamine has long been associated with addictive drugs, such as cocaine, that overexcite these brain cells. The end result is that addicts make increasingly reckless decisions, forgoing longterm goals for the sake of an intensely pleasurable short-term fix. “When you’re addicted to a drug, your brain is basically convinced that this expensive white powder is worth more than your marriage or life,” Montague says. In other words addiction is a disease of valuation: Dopamine cells have lost track of what’s really important.
Montague wanted to know which part of the dopamine system was distorted in the addicted brain. He began to wonder if addiction was, at least in part, a disease of fictive learning. Addicted smokers will continue to smoke even when they know it’s bad for them. Why can’t they instead revise their models of reward?
Last year Montague decided to replicate his stock market study with a large group of chronic smokers. It turned out that smokers were perfectly able to compute a “what if” learning signal, which allowed them to experience regret. Like nonsmokers they realized that they should have invested differently in the stock market. Unfortunately, this signal had no impact on their decision making, which led them to make significantly less money during the investing game. According to Montague, this data helps explain why smokers continue to smoke even when they regret it. Although their dopamine neurons correctly compute the rewards of an extended life versus a hit of nicotine — they are, in essence, asking themselves, “What if I don’t smoke this cigarette?” — their brain doesn’t process the result. That feeling of regret is conveniently ignored. They just keep on lighting up.
One day, it might be possible to diagnose bubbles not by trying to decipher spikes in housing prices, but by sticking people in scanners and studying the intensity of their financial regret. Have their fictive-error learning signals pushed them to pursue excessive risk? Have they stopped thinking about the possibility of losses? Because these are the two essential psychological ingredients of every bubble: a greedy remorse that leads us to seek more and more profits, and a temporary suspension of loss aversion, so that we stop considering the possibility that, yes, even Cisco stock sometimes declines.