April 1, 2021Virtual seminar
Deep learning for Mean Field Games, and applications to finance
Mathieu LaurièreORFE, Princeton University
Abstract
Mean field game (MFG) theory is a framework to study equilibria in populations consisting of a very large number of strategic agents. This paradigm is motivated by a broad range of models, from economics to crowd motion, finance, and risk management. Developing scalable computational methods is a key step towards concrete applications. In this talk, we propose several numerical methods for MFGs based on machine learning tools such as function approximation via neural networks and stochastic optimization. We will present numerical results for models motivated by applications to finance. This is joint work with René Carmona.
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