If there is any one aspect of the Web 2.0 era that I find most fascinating, it is the idea that groups or “crowds” can collectively offer more intelligence about current and future events than individuals–even experts.
No one describes the power of a crowd’s decision making ability more eloquently than James Surowiecki in his book The Wisdom of Crowds. As Surowiecki’s examples illustrate, this concept is not new; rather, it is a natural consequence of human behavior.
One way that this concept is being put to use in real-world applications is through prediction markets (sometimes called decision markets). Prediction markets have tremendous potential as a forecasting tool in the enterprise or for any entity that wants to make use of its single greatest source of intelligence—the individuals sitting within its walls.
If you’re familiar with how a futures contract works, that’s a good analogy. A conventional futures contract is commonly used as insurance against unfavorable movements in the price of assets and also for purely speculative investing. Established futures markets exist for a variety of heavily-traded commodities and assets like oil, corn, and stock indices.
Beyond their value to individual participants, futures markets provide interesting insight about the future. Because market participants have a vested economic interest in being correct, they use all (legally obtained) information available when making their bets. For example, the futures price of corn will reflect expectations of future weather patterns, the demand for livestock feed, the value of the US dollar, and other factors that we may not necessarily associate with the tasty, versatile yellow vegetable. As such, futures markets offer some of the highest quality information about the future as seen through the collective eyes of people that have a financial interest in being correct.
In essence, a prediction market is a futures market created for a specific event, not necessarily economic in nature.
For a prediction market to be most effective, however, there should be a group of individuals that each have unique information about the event. As members of the crowd place their individual bets, a consensus view of the future emerges. And as more information becomes available, the forecast evolves–right up to the moment of the event. As with a futures market, participants are rewarded in proportion to the accuracy of their forecasts. The “payoff” can be real money, fake money, or anything that motivates participants to make wise bets.
Dating back to the 1988 US presidential election, the Iowa Electronic Markets (IEM) represent one of the earliest examples of prediction markets. In August of 1995, the Wall Street Journal ran a story on how the IEM was being used to predict the outcome of the 1996 US presidential election. At press time the market was showing increasing confidence that Bill Clinton would ultimately win and was also favoring Bob Dole as the likely Republican nominee:
For the moment, the prices in the IEM presidential market bode well for Mr. Clinton. His stock has enjoyed a 19% rally since May 1, climbing to 45.1 cents last week from the May 1 price of 37.9. If Mr. Clinton wins re-election, holders of his stock will receive $1 a share. If he loses, his backers will get nothing.
Meantime, in the market for speculating on who will get the Republican presidential nomination, Kansas Sen. Dole’s stock is enjoying a sparkling rally. His shares are up nearly 30% since the beginning of May, largely at the expense of Texas Sen. Gramm, whose shares have dropped about the same percentage.
The prediction market described above is elegantly constructed. Since holders of Clinton’s stock would ultimately receive $1 per share, the pre-election value of his shares at any point in time represented the probability of Clinton winning. At press time, the market felt Clinton had a 45.1% chance of beating the future Republican nominee.
If you were trading in that market in late August 1995 and you had reason to believe that Clinton’s chances were better than 45.1%, then 45.1 cents a share would have looked cheap. You would naturally be motivated to buy shares, which in turn would have driven the price of Clinton’s shares higher.
It is this natural pursuit of self interest that gives the IEM an edge over more traditional methods for forecasting election results like polling, where pollsters hope that good morals prevail.
The following is a simulation of what a prediction market might look like over the course of a year. At the end of the year, the outcome is “yes.”
More recently, the IEM has been used to forecast the outcome of non-political events, like movie box office receipts and the future value of economic variables like unemployment rates, the consumer index, and stock prices. A complete list of markets operated by the IEM is available on their website.
Prediction markets seem to be getting even more visibility these days as social technologies make it easier than ever to create prediction markets around everything from sporting events to enterprise projects.
In future posts, I will explore prediction markets in greater detail. If you have used a prediction market for any purpose, I would love to hear from you in the comments or by email.
[Crystal ball photo by Isobel T via Flickr]