A probabilistic approach to the convergence problem in mean field games

Ludovic Tangpi, Princeton University
Fine Hall 214

In this talk we will discuss the convergence problem in mean field games. After introducing mean field games and motivating the problem from classical statistical physics considerations, we will review recent advances on the convergence problem. We will focus on the method based on “forward-backward propagation of chaos”. The main results discussed will establish that allowing structural conditions such as dissipativity of the drift or displacement monotonicity of the coupling functions in the game allows to derive very general, quantitative convergence theorems that do not require analysis (or even existence) of the master equation.