# Stationary Equilibria of Mean Field Games with Finite State and Action Space

## TL;DR

This paper studies stationary equilibria in finite state and action mean field games with Markovian dynamics, providing existence results, characterization methods, and computational techniques for these equilibria.

## Contribution

It introduces a framework for finite state and action mean field games with Markov dynamics, establishing existence and characterization of stationary equilibria.

## Key findings

- Existence of stationary mean field equilibria under mild conditions.
- Characterization of equilibria as fixed points of a transition-based map.
- Demonstration of techniques on two example models.

## Abstract

Mean field games formalize dynamic games with a continuum of players and explicit interaction where the players can have heterogeneous states. As they additionally yield approximate equilibria of corresponding $N$-player games, they are of great interest for socio-economic applications. However, most techniques used for mean field games rely on assumptions that imply that for each population distribution there is a unique optimizer of the Hamiltonian. For finite action spaces, this will only hold for trivial models. Thus, the techniques used so far are not applicable. We propose a model with finite state and action space, where the dynamics are given by a time-inhomogeneous Markov chain that might depend on the current population distribution. We show existence of stationary mean field equilibria in mixed strategies under mild assumptions and propose techniques to compute all these equilibria. More precisely, our results allow -- given that the generators are irreducible -- to characterize the set of stationary mean field equilibria as the set of all fixed points of a map completely characterized by the transition rates and rewards for deterministic strategies. Additionally, we propose several partial results for the case of non-irreducible generators and we demonstrate the presented techniques on two examples.