Decision-focused learning for inverse noncooperative games: Generalization bounds and convergence analysis

We study inverse noncooperative games: learning players’ utilities from observed equilibrium actions and predicting future behavior. Rather than estimate-then-predict, we propose a decision-focused learning approach that embeds the game’s equilibrium as a differentiable layer in an end-to-end system. We provide covering-number bounds for solution-function classes arising from parametric variational inequalities and derive generalization guarantees with smooth losses. Experiments highlight improved predictive accuracy over baselines.