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.