Everything Is Obvious_ _Once You Know the Answer - Duncan J. Watts [74]
In generating predictions like this one, prediction markets exploit a phenomenon that New Yorker writer James Surowiecki dubbed the “wisdom of crowds”—the notion that although individual people tend to make highly error-prone predictions, when lots of these estimates are averaged together, the errors have a tendency to cancel out; hence the market is in some sense “smarter” than its constitutents. Many such markets also require participants to bet real money, thus people who know something about a particular topic are more likely to participate than people who don’t. What’s so powerful about this feature of prediction markets is that it doesn’t matter who has the relevant market information—a single expert or a large number of nonexperts, or any combination in between. In theory, the market should incorporate all their opinions in proportion to how much each is willing to bet. In theory, in fact, no one should be able to consistently outperform a properly designed prediction market. The reason is that if someone could outperform the market, they would have an incentive to make money in it. But the very act of making money in the market would immediately shift the prices to incorporate the new information.3
The potential of prediction markets to tap into collective wisdom has generated a tremendous amount of excitement among professional economists and policy makers alike. Imagine, for example, that a market had been set up to predict the possibility of a catastrophic failure in deep-water oil drilling in the Gulf prior to the BP disaster in April 2010. Possibly insiders like BP engineers could have participated in the market, effectively making public what they knew about the risks their firms were taking. Possibly then regulators would have had a more accurate assessment of those risks and been more inclined to crack down on the oil industry before a disaster took place. Possibly the disaster could have been averted. These are the sorts of claims that the proponents of prediction markets tend to make, and it’s easy to see why they’ve generated so much interest. In recent years, in fact, prediction markets have been set up to make predictions as varied as the likely success of new products, the box office revenues of upcoming movies, and the outcomes of sporting events.
In practice, however, prediction markets are more complicated than the theory suggests. In the 2008 presidential election, for example, one of the most popular prediction markets, Intrade, experienced a series of strange fluctuations when an unknown trader started placing very large bets on John McCain, generating large spikes in the market’s prediction for a McCain victory. Nobody figured out who was behind these bets, but the suspicion was that it was a McCain supporter or even a member of the campaign. By manipulating the market prices, he or she was trying to create the impression that a respected source of election forecasts was calling the election for McCain, presumably with the hope of creating a self-fulfilling prophecy. It didn’t work. The spikes were quickly reversed by other traders, and the mystery bettor ended up losing money; thus the market functioned essentially as it was supposed to. Nevertheless, it exposed a potential vulnerability of the theory, which assumes that rational traders will not deliberately lose money. The problem is that if the goal of a participant is instead to manipulate perceptions of people outside the market (like the media) and if the amounts involved are relatively small (tens of thousands of dollars, say, compared