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Jules Hedges | Compositional Game Theory - Towards Incentives Modelling at Scale
Jules is a mathematician and computer scientist who was a pioneer of the recently developed field of applied category theory. His main scientific interests are in microeconomics and machine learning. He is a co-founder of the Institute for Categorical Cybernetics (cybercat.insti...), a nonprofit organization for research and open source software development, and 20squares (20squares.xyz/), a startup specializing in applied mechanism design and economic modelling in the blockchain space. By day he is a lecturer in computer science at the University of Strathclyde in Glasgow, Scotland.
julesh.com/
Compositional game theory is an approach to modelling interactions of strategic agents which is designed to be inherently scalable, in contrast to traditional normal-form and extensive-form games that are unsuitable for anything larger than toy problems. It supports a model-is-code paradigm and reuse of model components, inspired by software engineering. I won’t have time to talk much about how compositional game theory works, so instead this will be a taster of what it looks like and what it can do: we have used it in settings such as applied mechanism design, smart contract analysis and the theory of institution design. At the end I’ll hint at a much bigger picture: the mathematical foundations of compositional game theory also appear in several different parts of machine learning and other fields, something we call “categorical cybernetics”, and I’ll discuss what the implications of this could be.
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