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From Prediction Markets to Decision Markets

Speaker: Yiling Chen
Harvard University

March 29, 2012 - 4:00 p.m. to 5:00 p.m.
Location: Troy 2012
Hosted By: Dr. Sanmay Das (x2782)


Prediction markets are designed to aggregate information on some event of interest, for example, Obama to be re-elected President in 2012. They provide good incentives for myopic traders to honestly reveal their information about the event. However, in many situations, a decision maker is interested in getting information to facilitate decision making. For example, a company collects information on which CEO candidate, if hired, can maximally increase the stock price of the company and then makes the hiring decision based on the information. Decision markets can be used in such settings to obtain information on some event conditioned on a decision action. However, the inter-dependence between information aggregation and decision making in a decision market creates incentives for traders to manipulate market prices. I will discuss some initial possibility and impossibility results on designing decision markets with good incentive properties and suggest some open questions.
(This talk is based on joint work with Ian Kash, Mike Ruberry, and Victor Shnayder.)

Brief Bio:

Yiling Chen is an Assistant Professor of Computer Science at Harvard University. She received her Ph.D. in Information Sciences and Technology from the Pennsylvania State University. Prior to working at Harvard, she spent two years at the Microeconomic and Social Systems group of Yahoo! Research in New York City. Her current research focuses on topics in the intersection of computer science and economics. She is interested in designing and analyzing social computing systems according to both computational and economic objectives. Chen received an ACM EC outstanding paper award and an NSF Career award, and was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch" in 2011.

Last updated: March 20, 2012