Research Corner: Gates Building Prediction Market


Prediction markets are popular for aggregating opinions regarding the likelihood of future events by enabling people to buy and sell "shares" that indicate whether or not they think the event will happen. There has been some evidence that the market prices are reasonably accurate predictors of whether those events will actually happen.

For example, the popular prediction market site intrade.com has markets for sports events, such as who Albert Pujols will play for in 2012; scientific events, such as whether the Higgs Boson Particle will be observed before the end of 2012; and political events, such as who will be the Republican presidential nominee. On August 31st, you could have bought a $10 share of "Rick Perry will be the Republican presidential nominee" for a little less than $4. This price indicated that the market believed that there was roughly a 40% chance of the event happening. As of this writing, the price is a little less than a $1. So, had you bought the share in August, you could now sell it back, and take a $3 loss. If the event happens, people holding shares earn $10 per share, while if it does not they earn nothing. Short selling is allowed, so you can bet against events as well as for. If that makes sense to you, you're ready to play! If you're confused, the intrade.com "How it Works" tab fills in all the details.

From September 2008 to August 2009, Abe Othman, a Ph.D. student at Carnegie Mellon University (CMU), ran a prediction market to forecast when CMU's two new computer science buildings would open. This market was, by one measure, the largest prediction market ever run. And it did manage to zero in on the correct date as the opening date approached.

In partnership with Yahoo! Research through their Faculty Research Engagement Program (FREP), and specific collaboration with David Pennock, we are pleased to announce with this article, a new prediction market on the opening date of the UTCS Gates Building, which builds upon the success of the CMU experiment.

Professor Peter Stone and Post-doc Tsz-Chiu Au invite you to add your predictions to the mix, perhaps after assessing the progress through the live webcams available on the department's website. Successful bidders will earn raffle tickets for a prize drawing after the building opens.

Full details are available at http://www.cs.utexas.edu/gbpm.

Bid early, bid often, and have fun!

by Peter Stone, Artificial Intelligence and Data Mining, Machine Learning, and Natural Computation

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