Abstract:
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.
Prof. Ludovic Tangpi - Bio
Ludovic Tangpi is Assistant professor of Operations Research and Financial Engineering and Princeton University. His research focuses on optimal stochastic control, stochastic particle systems and game theory. In addition Ludovic is interesting in variety of application of these topics and quantitative finance. Ludovic’s research is notably funded by an NSF CAREER grant.
Prof. Ludovic Tangpi