Discrete mean field games
Diogo A. Gomes, Joana Mohr, Rafael R. Souza

TL;DR
This paper develops a discrete-time, finite-state mean field game model for large agent populations, establishing existence, uniqueness, and exponential convergence to stationary solutions under initial-terminal conditions.
Contribution
It introduces a discrete mean field game framework, proving existence, uniqueness, and convergence results, extending continuous models to discrete settings.
Findings
Existence and uniqueness of solutions for the discrete model.
Proven exponential convergence to stationary solutions.
Extended Lasry and Lions' approach to discrete time and states.
Abstract
In this paper we study a mean field model for discrete time, finite number of states, dynamic games. These models arise in situations that involve a very large number of agents moving from state to state according to certain optimality criteria. The mean field approach for optimal control and differential games (continuous state and time) was introduced by Lasry and Lions. Here we consider a discrete version of the problem. Our setting is the following: we assume that there is a very large number of identical agents which can be in a finite number of states. Because the number of agents is very large, we assume the mean field hypothesis, that is, that the only relevant information for the global evolution is the fraction of players in each state at time . The agents look for minimizing a running cost, which depends on , plus a terminal cost . In contrast with…
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Taxonomy
TopicsStochastic processes and financial applications · Stochastic processes and statistical mechanics · Economic theories and models
