Mean Field Game Problem with Kantorovich Potential Coupling: An Attrition model

Abstract

In this work we address a class of Mean-Field Game (MFG) systems on the flat torus $\mathbb{T}^{d}$ in which individual agent incentives arise from macroscopic, distribution-dependent objectives governed by the Wasserstein-2 ($W_{2}$) distance. Taking the first variation (Lions derivative) of the Wasserstein-based macroscopic functional yields a coupled forward-backward system of Hamilton--Jacobi--Bellman (HJB) and Fokker--Planck (FP) equations featuring a nonlocal coupling driven by the Kantorovich optimal transport potential. As a primary case study, we formulate a large-scale attacker-defender attrition model aimed at protecting High-Value Units (HVUs). By defining population-wise attrition rates via the $W_{2}$ metric and casting the defense strategy as a Pareto-optimal McKean--Vlasov control problem, we derive a concrete HJB--FP system with a Kantorovich potential coupling. We prove the local-in-time existence of classical solutions for this system using Schauder's fixed-point theorem. Finally, we compute the MFG dynamics by combining the Sinkhorn algorithm with ResNet-based Physics-Informed Neural Networks (PINNs), and simulate two-dimensional attacker-defender engagements across diverse attrition parameters, control costs, and multi-HVU configurations.