Efficient Market Making via Convex Optimization
Suppose that you are interested in estimating the probability that the US unemployment rate in October 2013 will be greater than 10%. You might choose to ask an expert, dig through news articles, or run a regression with historical data to come up with a reasonably well- informed guess. But you might be able to obtain an equally good and often better estimate by appealing to the wisdom of crowds and asking the market.
A prediction market is a betting intermediary designed to aggregate information. It offers financial securities whose payoff is tied to outcomes of an event of interest and attracts participants to trade the securities. In this talk, I will discuss a general framework for the design of prediction markets over combinatorial or infinite state spaces. The framework enables the design of computationally efficient markets tailored to an arbitrary, yet relatively small, space of securities. We show that any market satisfying a set of intuitive conditions must price securities via a convex cost function, which is constructed via conjugate duality. Rather than deal with an exponentially large or infinite outcome space directly, our framework only requires optimization over a convex hull. By reducing the problem of automated market making to convex optimization, where many efficient algorithms exist, we arrive at a range of new efficient pricing mechanisms for various problems. We demonstrate the advantages of this framework with the design of some particular markets.
This talk is based on join work with Jacob Abernethy and Jennifer Wortman Vaughan.
Yiling Chen is an 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.