Finite State Mean Field Games with Wright–Fisher Common Noise as Limits of N-Player Weighted Games

• Published in: Mathematics of operations research Vol. 47; no. 4; pp. 2840 - 2890
• Main Authors: Bayraktar, Erhan, Cecchin, Alekos, Cohen, Asaf, Delarue, François
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.11.2022; Institute for Operations Research and the Management Sciences
• Subjects: convergence problem; diffusion approximation; Empirical analysis; Finite element analysis; Game theory; Games; mean field games; Noise; Operations research; Primary: 91A13, 91A15, 35K65; Probability; Stochastic models; Wright–Fischer common noise
• ISSN: 0364-765X; 1526-5471
• DOI: 10.1287/moor.2021.1230

Forcing finite state mean field games by a relevant form of common noise is a subtle issue, which has been addressed only recently. Among others, one possible way is to subject the simplex valued dynamics of an equilibrium by a so-called Wright–Fisher noise, very much in the spirit of stochastic models in population genetics. A key feature is that such a random forcing preserves the structure of the simplex, which is nothing but, in this setting, the probability space over the state space of the game. The purpose of this article is, hence, to elucidate the finite-player version and, accordingly, prove that N -player equilibria indeed converge toward the solution of such a kind of Wright–Fisher mean field game. Whereas part of the analysis is made easier by the fact that the corresponding master equation has already been proved to be uniquely solvable under the presence of the common noise, it becomes however more subtle than in the standard setting because the mean field interaction between the players now occurs through a weighted empirical measure. In other words, each player carries its own weight, which, hence, may differ from 1 / N and which, most of all, evolves with the common noise.