A Theory of Automated Market Makers in DeFi

Massimo Bartoletti, James Hsin yu Chiang*, Alberto Lluch-Lafuente

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Automated market makers (AMMs) are one of the most prominent decentralized finance (DeFi) applications. They allow users to exchange units of different types of crypto-assets, without the need to find a counter-party. There are several implementations and models for AMMs, featuring a variety of sophisticated economic mechanisms. We present a theory of AMMs. The core of our theory is an abstract operational model of the interactions between users and AMMs, which can be concretised by instantiating the economic mechanisms. We exploit our theory to formally prove a set of fundamental properties of AMMs, characterizing both structural and economic aspects. We do this by abstracting from the actual economic mechanisms used in implementations and identifying sufficient conditions which ensure the relevant properties. Notably, we devise a general solution to the arbitrage problem, the main game-theoretic foundation behind the economic mechanisms of AMMs.

Original languageEnglish
Title of host publicationCoordination Models and Languages
EditorsFerruccio Damiani, Ornela Dardha
Publication date2021
ISBN (Print)9783030781415
Publication statusPublished - 2021
Event23rd IFIP WG 6.1 International Conference on Coordination Models and Languagesec 2021 - Virtual, Online
Duration: 14 Jun 202118 Jun 2021


Conference23rd IFIP WG 6.1 International Conference on Coordination Models and Languagesec 2021
CityVirtual, Online
Internet address
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12717 LNCS

Bibliographical note

Funding Information:
Massimo Bartoletti is partially supported by Conv. Fondazione di Sardegna & Atenei Sardi project F74I19000900007 ADAM. James Hsin-yu Chiang is supported by the PhD School of DTU Compute. Alberto Lluch Lafuente is partially supported by the EU H2020-SU-ICT-03-2018 Project No. 830929 CyberSec4Europe (cybersec4europe.eu).

Funding Information:
Acknowledgements. Massimo Bartoletti is partially supported by Conv. Fondazione

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.


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