A numerical scheme for a mean field game in some queueing systems based on Markov chain approximation method

TL;DR

This paper develops a numerical scheme based on Markov chain approximation for solving a mean field game related to queueing systems, demonstrating convergence and applicability to strategic server models.

Contribution

It introduces a new Markov chain approximation method for MFGs with reflecting barriers, specifically tailored for queueing systems with strategic servers.

Findings

The scheme converges over small time intervals.

It effectively approximates solutions for queueing-based MFGs.

The method is validated through convergence analysis.

Abstract

We use the Markov chain approximation method to construct approximations for the solution of the mean field game (MFG) with reflecting barriers studied in Bayraktar, Budhiraja, and Cohen (2017). The MFG is formulated in terms of a controlled reflected diffusion with a cost function that depends on the reflection terms in addition to the standard variables: state, control, and the mean field term. This MFG arises from the asymptotic analysis of an $N$-player game for single server queues with strategic servers. By showing that our scheme is an almost contraction, we establish the convergence of this numerical scheme over a small time interval.