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An Optimization-Based Framework for Automated Market-Making

Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan

Abstract

We propose a general framework for the design of securi- ties markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove 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 polynomial-time pricing mechanisms for various problems. We demonstrate the advantages of this framework with the design of some particular markets. We also show that by relaxing the convex hull we can gain computational tractability without compromising the market institution’s bounded budget.

Details

Publication typeInproceedings
Published inTwelfth ACM Conference on Electronic Commerce (EC)
URLhttp://www.jennwv.com/papers/complexmarkets.pdf
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