A numerical scheme for a mean field game in some queueing systems based on Markov chain approximation method
This work provides a numerical method for a specific class of mean field games, but the convergence result is limited to small time intervals, making it incremental.
The authors develop a numerical scheme for solving a mean field game with reflecting barriers arising from queueing systems, and prove its convergence over small time intervals by showing it is an almost contraction.
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.