similar to the world’s largest prediction market, Ireland-based Intrade.

“The request for comments was actually very well written and it’s clear they understand a lot of the issues,” Pennock says. Even if public prediction markets for substantial sums are not approved in the U.S., the markets offer considerable promise for enterprise planners who want the latest information on questions such as a product’s likely launch date or revenue projections, and public policy forecasters, who can design markets exempt from CFTC oversight.

Growing opportunities in internal pri-vate-sector prediction markets are also revealing divergent philosophies among the markets’ designers. Many of the public markets feature price-adjustment algorithms built around answering discrete multiple-choice outcomes, such as which candidate will win an election or if a product will launch in month x, y, or z. However, Mat Fogarty, CEO of prediction markets startup Xpree, says enterprise clients need to address questions expressed as continuous variables, such as a date range in which a product will launch or how many units will sell, and those markets need to feature an intuitive interface that encourages participation among those without a great interest in financial or mathematic complexities. The front end of these new prediction markets, as designed by Xpree, will feature interfaces inspired by computer game design, while the back end will replace multiple-choice algorithms with automated market makers based on Bayes-ian probability, enabling participants to place bets on a range of options.

David Pennock of Yahoo! at a “Prediction markets: tapping the Wisdom of crowds” conference organized by Yahoo!’s technology Development Group.

forecasting events

The pioneering, modern public-policy prediction market, the University of Iowa’s Iowa Electronic Markets (IEM), is now 21 years old and still offering new events for traders to forecast. First used in the 1988 U.S. presidential election, the IEM has offered markets on congressional elections, federal monetary policy, and inspired university colleagues to run a prediction market on national influenza infection trends. The IEM’s unique design also inspired the latest corporate prediction market, a virtual-money internal market operated by Google.

IEM steering committee member Thomas Rietz, a professor of finance at the university, says the aggregate zero-risk design of the IEM allows the markets to perfectly reflect the aggregate forecast opinions of its participants. By aggregate zero-risk, Rietz explains that when a trader enters a particular bilateral (either/or) market, he or she must buy one share of each choice, called a bundle, for a total cost of $1. If the trader holds the bundle until the market concludes, there is neither profit nor gain. If the trader guesses the outcome successfully, and sells the losing unit of the bundle to another trader while the market is running, he or she picks up the original $1 bet plus whatever price was agreed upon for the losing share that was sold. If the trader chooses to hold onto the loser and sell the eventual winner, however, they will incur the $1 loss at payout time. At any given

the most visible
enterprise use of
prediction markets
is to help companies
improve product and
process development.

time, the number of eventual winning shares and losing shares is equal and held by the traders. So, the university bears no counterparty risk and there is no need to provide hedging margins that irrationally affect outcomes.

“The price you would be willing to buy or sell for today is your expectation of its value in the future—the prices can be directly interpreted as a forecast,” Rietz says. “In ordinary futures markets, there is a long-lasting debate, going back to John Maynard Keynes in the 1930s, over whether prices can legitimately be used as forecasts, and it all hinges on whether or not people demand a return or face a risk in aggregate when they’re investing in these contracts.”

The enterprise markets are offering intriguing design opportunities, as expressed by Xpree’s Fogarty, as well as possible benefits beyond mining collective beliefs of what may make a successful product. The Google prediction market, for example, was examined by Bo Cowgill of Google, Justin Wolfers of the University of Pennsylvania’s Wharton School of Business, and Eric Zitze-witz of Dartmouth College as a vehicle for the way information flows within an organization. Prediction markets, they assert, provide employees with incentives for truthful revelation and can capture changes in opinion at a much higher frequency than surveys, allowing one to track how information moves inside an organization and how it responds

PhotograPh By daVid rout