Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games

Abstract:Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through representative agents and their associated mean-field distributions. However, existing approaches do not explicitly account for uncertainty in the behavior of other populations. To this end, we introduce a new paradigm: risk-averse multi-population mean-field games, where each population optimizes a worst-case expected reward over dynamically feasible ambiguity sets of mean-field flows of a subset of the other populations. Employing an occupation-measure formulation along with tools from set-valued analysis, we establish, under mild assumptions, several theoretical properties of the multi-population game, including the geometric properties of the ambiguity sets and the existence of a novel risk-averse multi-population mean-field equilibrium. Further, we derive contractivity results of the fixed-point operator under entropy regularization and show that it can be utilized to learn the equilibrium. Finally, we propose a risk-averse fictitious-play scheme and show that exploitability decays to zero, despite the additional nonlinearity introduced by the worst-case objective. We report several numerical experiments to illustrate convergence and risk-averse behavior.

Optimization and Control (math.OC); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2610.09244 [math.OC]

(or arXiv:2610.09244v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2610.09244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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