Prediction markets got their start almost 150 years ago in the form of wagering on presidential elections. According to ­researchers Paul W. Rhode and Koleman S. Strumpf of the University of North Carolina, Chapel Hill, there were “large and often well-organized markets” in the period between 1868 and 1940. Rhode and Strumpf’s study found that the market did “an admirable job in forecasting elections in a period before scientific polling.”

In 1988, the University of Iowa College of Business revived the tradition of wagering on presidential elections with the world’s first electronic prediction market. Since then, ­researchers there have set up increasingly complicated markets to study their behavior and accuracy. In its first three elections, the Iowa Electronic Markets were off by an average of only 1.37 percent.

By 2006, electronic markets were predicting some elections in the United States with stunning success. A commercial market­place, Tradesports, let the public wager on all 33 U.S. Senate contests held that year. Not a single public opinion poll predicted all 33 correctly, but the bettors at Tradesports collectively did. The site even forecast the Virginia and Montana contests, which were decided by mere tenths of a percent—a few thousand votes out of hundreds of thousands.

Formal research in prediction markets goes back at least to the storied economist Friedrich Hayek. As early as 1948, Hayek wrote about the ways that free markets emit information. By the late 1980s, Robin Hanson at George Mason University, in Fairfax, Va., and other researchers elsewhere had begun to study market behavior under controlled laboratory conditions.

So how do prediction markets typically work? First of all, they use real money. That’s important for keeping bettors honest. The price you pay is set by the market’s opinion on the odds of that outcome. If, for example, you have to pay 33 cents for a bet that former U.S. Senator Fred Thompson of Tennessee will be the Republican candidate for president of the United States next year (the price in mid-July), and 40 cents to bet on Rudolph Giuliani, former mayor of New York, then the market says there is a 1 in 3 chance of Thompson getting the nomination, while Giuliani’s chances are 2 in 5.

Corporate prediction markets work the same way. Real money or some other trinkets are still necessary, because they reduce the chances that participants will lie, out of boredom or to advance their agendas in some way. Using real money is a double-edged sword, however—it can also motivate people to manipulate the market, by virtue of being able to influence the outcome of events in the real world. For example, in the Microsoft market, if enough money were on the line, a programmer could deliberately introduce bugs into the code that would affect its release date, just as a college basketball star can throw a key tournament game. For that reason, and because companies don’t want to run afoul of insider trading laws, some markets limit the amount of money involved. Others use fake money, issuing modest prizes or honoring the winner in some other way.

In corporate prediction markets, the company involved ­usually subsidizes the wagers by giving participants initial stakes, real or otherwise. But even though they’re not risking their own money, the bettors generally don’t lie or misrepresent their beliefs with their bets. It’s human nature to want to win more money. And in addition to the financial reward for success, prediction markets are public forums, and winners can take pride in their success.

The first experimental corporate markets were at Hewlett-Packard Co., in Palo Alto, Calif. From 1997 to 1999 a researcher there, Kay-Yut Chen, with the help of Charles R. Plott, an economics professor at Caltech, let selected individuals bet on future sales of some of the company’s printer products. They found prediction markets to be “a considerable improvement over the HP official forecast.”

HP is no longer merely experimenting with prediction markets. Today, a market is used to predict the future cost of dynamic random access memory chips. “HP is the largest DRAM buyer in the world,” says Leslie Fine, a game theorist who works in the Information Dynamics Lab at the company’s HP Labs. “DRAM accounts for between 7 and 10 percent of the price of a new computer. Our profit margins are often less than that, so we’re intensely interested in its price.” Prior to using a betting system, Fine says, about 25 managers used to attend “endless meetings” each month to forge the next corporate prediction, which was then used by those who purchase the chips.

Last year, Fine and her colleagues assembled a group of 14 executives, “none of whom should have had the big picture.” After 3 hours of training in prediction markets, she set them loose on an internal company Web site, where they spent about an hour a month making their bets. The result? Besides spending far less time, the executives were more accurate. The endless meetings had produced predictions that were, on average, 4 percent off from the actual future prices of DRAM, while the prediction markets missed by 2.5 percent.

Illustration: Viktor Koen

Prediction markets work so well because they ferret out those confident enough to back up their beliefs with cash. Suppose a marketer wants to predict what his company’s sales figures will be in the next quarter. He can set up a prediction market that puts the question directly to his salespeople, ­marketers, accountants, and others. The market gives those people an incentive to express their knowledge. The hope of winning money smokes out people who think they know the right answer, so the group of bettors is self-selecting.

Of course, some people don’t know as much as they think they do, and some will make lousy bets. But as it turns out, that’s a feature, not a problem. As long as there are people with both money and expertise, they will trump the bad betting of the ill-informed and overconfident with additional wagers of their own.

In the Microsoft case, for example, if the manager who believed, incorrectly, in the original release date of November bet accordingly, the payoff for November would go down, and the payoff for all the other months would go up. That higher payoff would raise the stakes for the people who were sure the product would ship later than November, and they would bet more money on one or more of those other months. Eventually, an equi­librium would be reached—which might not be different from the state of the betting before the bad November bet was made.

A market that consisted only of experts who were always right wouldn’t see much action, because bettors couldn’t win much. Imagine a poker game where almost all the cards are face up and everyone is a good player. As soon as the player with the best hand makes a raise, the other players drop out.

One way to inspire more betting is for the house to throw some initial money into the pot or to give the players some chips. That’s why most corporate markets give employees an account with which to start betting. That stake is a subsidy of sorts, as is “sucker” money—the betting of the ignorant. As Hanson notes, “the winners are attracted by losers, just as wolves are attracted by sheep.”

The importance of a diverse user pool for the success of a prediction market can hardly be overstated. If everyone has a similar mind-set or is using the exact same information, each person will predict events uniformly, and like the poker game with all the cards face up, the betting will be minimal at best.

Consider a market set up at the University of Iowa in Iowa City to predict outbreaks of influenza. The market was established because while organizations such as the U.S. Centers for Disease Control and Prevention track actual outbreaks of influenza, there was no good way of predicting them.

Betting in the Iowa influenza market is by invitation. If only epidemiologists participated, the market would suffer from similar mind-sets and information. So the researchers also invited doctors, nurses, and pharmacists, giving the market fresh information and a different set of perspectives.

Each week the CDC ranks influenza activity on a scale of 1 to 5. Bettors wager on which of the CDC’s levels will be reported in a given week, up to five weeks in advance. The market runs throughout the flu season, from September to April. According to a study published this year in the journal Clinical Infectious Diseases, during the market’s first full year, 2004–2005, it correctly predicted the exact level 71 percent of the time one week in advance and 50 percent of the time two weeks ahead.

The use of the CDC’s five levels satisfies a key requirement for a successful prediction market: specificity. Markets have to be about measurable outcomes. Bets on a product release need specific time frames, such as the month-by-month market Microsoft set up. Sales figures can be divided into ranges, as the HP printer product markets were.

Last year, a poorly worded contract by Tradesports caused a ruckus when some bettors lost money. Trade­sports had set up a market to predict whether North Korea would test a long-range ­missile—which was defined as sending a missile beyond the country’s airspace. Those who bought that contract, which expired on 31 July 2006, thought they had won when a North Korean missile flew 442 kilometers (275 miles) into the Sea of Japan (East Sea). However, the contract also specified that the source of the confirming information had to be the U.S. Department of Defense, which declined to release any specifics about the test. Despite a White House statement that confirmed the missile “went out about 275 miles,” Tradesports awarded the contract to those who bet against a successful test.

Vague or ambiguous outcomes are an ongoing problem at Inkling, the Chicago start-up, which also runs a Web site that lets anyone create a market. The company has more than 1200 active or completed markets, according to cofounder Adam Siegel. But about 450, or three out of eight, are “collecting dust”—no one is making wagers. Siegel says the most common problem is a bad question. Some are problematic because they ask for opinions instead of predictions. “We get a lot of ‘Will my wife get pregnant?’ How would anyone know enough to say?”