Chapter One, Part V

If allowing people to bet on sporting events effectively creates a kind of machine that’s good at predicting the outcome of those events, an obvious question follows: Wouldn’t people betting on other kinds of events be equally good, as a group, at predicting them? Why confine ourselves to knowing what the chances are of Los Angeles beating Sacramento if there’s a way we could know what the chances are of, say, George W. Bush beating John Kerry?

We do have a well-established way of knowing what George W. Bush’s chances are: the poll. If you want to know how people are going to vote, you just ask them. Polling is, relatively speaking, accurate. It has a solid methodology behind it, and is statistically rigorous. But there’s reason to wonder if a market such as the betting market—one that allowed the people participating in it to rely on many different kinds of information, including but not limited to polls—might at the very least offer a competitive alternative to Gallup. That’s why the Iowa Electronic Markets (IEM) project was created.

Founded in 1988 and run by the College of Business at the University of Iowa, the IEM features a host of markets designed to predict the outcomes of elections—presidential, congressional, gubernatorial, and foreign. Open to anyone who wants to participate, the IEM allows people to buy and sell futures contracts” based on how they think a given candidate will do in an upcoming election. While the IEM offers many different types of contracts, two are most common. One is designed to predict the winner of an election. In the case of the California recall in 2003, for instance, you could have bought an “Arnold Schwarzenegger to win” contract, which would have paid you $1 when Schwarzenegger won. Had he lost, you would have gotten nothing. The price you pay for this kind of contract reflects the market’s judgment of a candidate’s chances of victory If a candidate’s contract costs 50 cents, it means, roughly speaking, that the market thinks he has a 50 percent chance of winning. If it costs 80 cents, he has an 80 percent chance of winning, and so on.

The other major kind of IEM contract is set up to predict what percentage of the final popular vote a candidate will get. In this case, the payoffs are determined by the vote percentage: if you’d bought a George W Bush contract in 2000, you would have received 48 cents (he got 48 percent of the vote) when the election was over.

If the IEM’s predictions are accurate, the prices of these different contracts will be close to their true values. In the market to predict election winners, the favorite should always win, and bigger favorites should win by bigger margins. Similarly in the voteshare market, if George W Bush were to end up getting 49 percent of the vote in 2004, then the price of a George W. Bush contract in the run-up to the election should be close to 49 cents.

So how has the IEM done? Well, a study of the IEM’s performance in forty-nine different elections between 1988 and 2000 found that the election-eve prices in the IEM were, on average, off by just 1.37 percent in presidential elections, 3.43 percent in other U.S. elections, and 2.12 percent in foreign elections. (Those numbers are in absolute terms, meaning that the market would have been off by 1.37 percent if, say, it had predicted that Al Gore would get 48.63 percent of the vote when in reality he got 50 percent.) The IEM has generally outperformed the major national polls, and has been more accurate than them even months in advance of the actual election. Over the course of the presidential elections between 1988 and 2000, for instance, 596 different polls were released. Three-fourths of the time, the IEM's market price on the day each of those polls was released was more accurate. Polls tend to be very volatile, with vote shares swinging wildly up and down. But the IEM forecasts, though ever-changing, are considerably less volatile, and tend to change dramatically only in response to new information. That makes them more reliable as forecasts.

What’s especially interesting about this is that the IEM isn’t very big—there have never been more than eight hundred or so traders in the market—and it doesn’t, in any way, reflect the makeup of the electorate as a whole. The vast majority of traders are men, and a disproportionate—though shrinking—number of them are from Iowa. So the people in the market aren’t predicting their own behavior. But their predictions of what the voters of the country will do are better than the predictions you get when you ask the voters themselves what they’re going to do.

The IEM’s success has helped inspire other similar markets, including the Hollywood Stock Exchange (HSX), which allows people to wager on box-office returns, opening-weekend performance, and the Oscars. The HSX enjoyed its most notable success in March of 2000. That was when a team of twelve reporters from The Wall Street Journal assiduously canvassed members of the Academy of Motion Pictures Arts and Sciences in order to find out how they had voted. The Academy was not happy about this. The organization’s president publicly attacked the Journal for trying to scoop us before Oscar night,” and the Academy urged members not to talk to reporters. But with the Journal promising anonymity more than a few people—356, or about 6 percent of all members—disclosed how they had filled out their ballots. The Friday before the ceremony, the Journal published its results, forecasting the winners in the six major Oscar categories—Best Picture, Best Director, Best Actor and Best Actress, Best Supporting Actor and Best Supporting Actress. And when the envelopes were opened, the Journal’s predictions--—-much to the Academy’s dismay—turned out to he pretty much on target, with the paper picking five of the six winners. The HSX, though, had done even better, getting all six of the six right. In 2002, the exchange, perhaps even more impressively picked thirty-five of the eventual forty Oscar nominees.

The HSX’s box-office forecasts are not as impressive or as accurate as the IBM’s election forecasts. But Anita Elberse, a professor of marketing at Harvard Business School, has compared the HSX’s forecasts to other Hollywood prediction tools, and found that the HSX’s closing price the night before a movie opens is the single best available forecast of its weekend box office. As a result, the HSX’s owner Cantor Index Holdings, is now marketing its data to Hollywood studios.

One of the interesting things about markets like the IEM and the HSX is that they work fairly well without much—or any—money at stake. The IEM is a real-money market, but the most you can invest is $500, and the average trader has only $50 at stake. In the HSX, the wagering is done entirely with play money. All the evidence we have suggests that people focus better on a decision when there are financial rewards attached to it (which may help explain why the IEM’s forecasts tend to be more accurate). But David Pennock—a researcher at Overture who has studied these markets closely—found that, especially for active traders in these markets, status and reputation provided incentive enough to encourage a serious investment of time and energy in what is, after all, a game.

As the potential virtues of these decision markets have become obvious, the range of subjects they cover has grown rapidly. At the Newsfutures and TradeSports exchanges, people could bet, in the fall of 2003 on whether or not Kobe Bryant would he convicted of sexual assault, on whether and when weapons of mass destruction would be found in Iraq, and on whether Arid Sharon would remain in power longer than Yassir Arafat. Ely Dahan, a professor at UCLA, has experimented with a classroom-decision market in which students bought and sold securities representing a variety of consumer goods and services, including SUVs, ski resorts, and personal digital assistants. (In a real-life market of this kind, the value of a security might depend on the first-year sales of a particular SUV) The market’s forecasts were eerily similar to the predictions that conventional market research had made (but the classroom research was much cheaper). In the fall of 2003, meanwhile, MITs Technology Review set up a site called Innovation Futures, where people could wager on future technological developments. And Robin Hanson, an economics professor at George Mason University who was one of the first to write about the possibility of using decision markets in myriad contexts, has suggested that such markets could be used to guide scientific research and even as a tool to help governments adopt better policies.

Some of these markets will undoubtedly end up being of little use, either because they’ll fail to attract enough participants to make intelligent forecasts or because they’ll be trying to predict the unpredictable. But given the right conditions and the right problems, a decision market’s fundamental characteristics—diversity, independence, and decentralization—are guaranteed to make for good group decisions. And because such markets represent a relatively simple and quick means of transforming many diverse opinions into a single collective judgment, they have the chance to improve dramatically the way organizations make decisions and think about the future.

In that sense, the most mystifying thing about decision markets is how little interest corporate America has shown in them. Corporate strategy is all about collecting information from many different sources, evaluating the probabilities of potential outcomes, and making decisions in the face of an uncertain future. These are tasks for which decision markets are tailor-made. Yet companies have remained, for the most part, indifferent to this source of potentially excellent information, and have been surprisingly unwilling to improve their decision making by tapping into the collective wisdom of their employees. We’ll look more closely at people’s discomfort with the idea of the wisdom of crowds, but the problem is simple enough: just because collective intelligence is real doesn’t mean that it will be put to good use.

A DECISION MARKET is an elegant and well-designed method for capturing the collective wisdom. But the truth is that the specific method that one uses probably doesn’t matter very much. In this chapter, we’ve looked at a host of different ways of tapping into what a group knows: stock prices, votes, point spreads, pari-mutuel odds, computer algorithms, and futures contracts. Some of these methods seem to work better than others, but in the end there’s nothing about a futures market that makes it inherently smarter than, say, Google or a pari-mutuel pool. These are all attempts to tap into the wisdom of the crowd, and that’s the reason they work The real key it turns out, is not so much perfecting a particular method, but satisfying the conditions—diversity, independence, and decentralization—that a group needs to be smart. As well see in the chapters that follow that’s the hardest, but also perhaps the most interesting, part of the story.

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