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From Myerson to Automated Markets: New Research Directions in Exchange Design - Jason Milionis (Columbia University, USA, and Category Labs)

milionis
Dates
2026-01-28 16:00 - 17:30
Venue
Archimedes Amphitheatre, 1 Artemidos Street, 15125, Marousi, Archimedes, Athena Research Center, Athens, Greece

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.


👉 Teams link: https://teams.microsoft.com/light-meetings/launch?context=%7B%22Tid%22%3A%226ae07702-c5f7-4f38-9b87-acad62a75d93%22%2C%22Oid%22%3A%22700280a8-c91f-41bd-9bf8-38b7959b4a6f%22%7D&anon=true&lightExperience=true&correlationId=fafb6e21-1108-4dab-9909-177003715233&agent=web&version=25100213804&coords=eyJjb252ZXJzYXRpb25JZCI6IjE5Om1lZXRpbmdfTnpFNE1qWmtaR0l0WldFM05TMDBOelE1TFRrME9USXROV016TXpFM05qZGtZalU0QHRocmVhZC52MiIsInRlbmFudElkIjoiNmFlMDc3MDItYzVmNy00ZjM4LTliODctYWNhZDYyYTc1ZDkzIiwib3JnYW5pemVySWQiOiI3MDAyODBhOC1jOTFmLTQxYmQtOWJmOC0zOGI3OTU5YjRhNmYiLCJtZXNzYWdlSWQiOiIwIn0%3D&deeplinkId=4e63ef33-15f3-4c15-97bd-bdbf273fae2e

 

 
 

Vision

To position Greece as a leading player in AI and Data Science

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Mission

To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world

 

Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).

 
 

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The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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