Group for Research in Decision Analysis

Mean field stochastic games

Tembine Hamidou Supélec, France

We consider a class of stochastic games with finite number of resource states, individual states and actions per states. At each stage, a random set of players interact. The states and the actions of all the interacting players determine together the instantaneous payoffs and the transitions to the next states. We study the convergence of the stochastic game with variable set of interacting players when the total number of possible players grow without bound. We show that the optimal payoffs and the mean field equilibrium payoffs are solutions of a coupled system of backward-forward equations. The limiting games are equivalent to discrete time anonymous sequential population games or to differential population games. Using multidimensional diffusion processes, a general mean field convergence to coupled stochastic differential equation is given. We illustrate both stable and unstable behaviors of the controlled mean field limit in wireless networks.