Computation and Incentives in Social Computing


Event Speaker: 

Yiling Chen (Harvard University)

Event Location: 


Event Date/Time: 

Tuesday, September 9, 2014 - 4:00pm

Reception to follow.
Social computing is a broad and evolving research area that concerns harnessing human intelligence to solve computational problems. Social computing systems function via complex and dynamic interactions among people and computing technologies. As a result, understanding how to purposefully design social computing systems to obtain high-quality information or contributions from their participants would require advances in at least a few interleaving directions: algorithms and computational theory, theory of incentive alignment, and understanding of human social behavior.

In this talk, I discuss two projects that my group and collaborators have carried out along the first and third directions respectively. The first project introduces a computational framework for designing prediction markets, markets for eliciting and aggregating probabilistic information about uncertain events of interests. Achieving the goal of information elicitation and aggregation requires prediction markets to have appropriate economic properties, which however pose a significant computational challenge on operating these markets. We propose an online convex optimization framework for designing prediction markets with desirable economic properties and for reasoning about the tractability of the designed markets. In the second project, we conduct behavioral experiments to evaluate the performance of a classical peer prediction mechanism in practice. Peer prediction mechanisms have an elegant game-theoretic equilibrium where participants truthfully reveal their information even if there is no verification of the elicited information. This equilibrium behavior is particularly desirable in social computing for harnessing human knowledge. A surprising result of our experiments is that truthful equilibrium is not focal and that participants seem able to coordinate on other, more profitable equilibria in peer prediction.  
Yiling Chen is the John L. Loeb Associate Professor of Natural Sciences and Associate 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, an AAMAS Best Paper Award, and an NSF Career award, and was selected by IEEE Intelligent Systems as one of "AI's 10 to Watch" in 2011.