Title: From Myerson to Automated Markets: New Research Directions in Exchange Design

Speaker: Jason Milionis (5th and final year Ph.D. candidate in the Computer Science Department at Columbia University, USA, and Category Labs) https://jasonmili.github.io/

Abstract: In decentralized finance ("DeFi"), automated market makers (AMMs) enable traders to programmatically exchange one asset for another. Such trades are enabled by the assets deposited by liquidity providers (LPs). The goal of this paper is to characterize and interpret the optimal (i.e., profit-maximizing) strategy of a monopolist liquidity provider, as a function of that LP's beliefs about asset prices and trader behavior. We introduce a general framework for reasoning about AMMs based on a Bayesian-like belief inference framework, where LPs maintain an asset price estimate. In this model, the market maker (i.e., LP) chooses a demand curve that specifies the quantity of a risky asset to be held at each dollar price. Traders arrive sequentially and submit a price bid that can be interpreted as their estimate of the risky asset price; the AMM responds to this submitted bid with an allocation of the risky asset to the trader, a payment that the trader must pay, and a revised internal estimate for the true asset price. We define an incentive-compatible (IC) AMM as one in which a trader's optimal strategy is to submit its true estimate of the asset price, and characterize the IC AMMs as those with downward-sloping demand curves and payments defined by a formula familiar from Myerson's optimal auction theory. We generalize Myerson's virtual values, and characterize the profit-maximizing IC AMM. The optimal demand curve generally has a jump that can be interpreted as a "bid-ask spread," which we show is caused by a combination of adverse selection risk (dominant when the degree of information asymmetry is large) and monopoly pricing (dominant when asymmetry is small).

Short Biography: Jason Milionis ( https://jasonmili.github.io/ ) is a Senior Researcher at Category Labs, and final-year Ph.D. candidate in the Computer Science Department at Columbia University, advised by Christos Papadimitriou and Tim Roughgarden. His research interests span the intersection of Algorithmic Game Theory and Economics with emerging areas of computation, specifically blockchains, where he studies their applications (especially decentralized finance) as innovative frameworks that pose unique incentive alignment challenges. Jason has been awarded the prestigious Two Sigma Ph.D. Fellowship, and previously graduated from the National Technical University of Athens (NTUA) in Electrical and Computer Engineering, majoring in Computer Science.


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