Bernhard von Stengel, London School of Economics

Abstract

This project proposes a framework, under development, of using machine learning to study larger and more realistic game-theoretic models. In a dynamic pricing game, firms compete by repeatedly setting a price for a product, where higher prices lead to higher short-term but lower long-term profits. The classic subgame-perfect equilibrium is very competitive, whereas strategic experiments show a tendency of agents to collude. We want to employ small neural nets to learn pricing strategies, with the learning environment given by a mixed equilibrium of existing strategies. Successfully learned strategies are added to the pool and a new equilibrium is computed. This is akin to double-oracle learning in zero-sum games, except that the game is not zero-sum and the resulting equilibrium depends on the learning history. The approach is modular rather than a large simulation, which should allow a better study of the relevant features of the underlying game, its learning mechanism, and the employed equilibrium concept.

[Presentation video]

Speaker Bio: Bernhard von Stengel, educated in Germany and the US, is Professor of Mathematics at the London School of Economics. He is a mathematical game theorist and an expert on computational and geometric methods for solving games. He chaired the 2016 World Congress of the Game Theory Society, is co-editor of the International Journal of Game Theory, and was Area Editor for Game Theory for Mathematics of Operations Research. His recent textbook "Game Theory Basics" teaches standard and non-standard topics that every game theorist should know.