Efficient Market Making via Convex Optimization

We propose a general framework for the design of securities 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 joint work with Jacob Abernethy and Jennifer Wortman Vaughan.

Speaker Details

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.

Series:
Microsoft Research Talks
Date:
Speakers:
Yiling Chen
Affiliation:
Harvard

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Series: Microsoft Research Talks