Data-Driven Games with Coherent Risk Measures

Abstract

We introduce Coherent Utility Measure Games (CUMGs) in which players' uncertainty about the distribution of payoffs is modeled using coherent utility (risk) measures. Such measures, including mean semideviation risk and conditional value-at-risk, allow for interpretable notions of players' risk aversion while retaining formal equivalence to distributionally robust games. While CUMGs, which are a subclass of distributionally robust games, are continuous games in general, they can be viewed as finite games ``lifted'' to the mixed strategy space, which illustrates computational challenges. Prior results extend to guarantee equilibrium existence in data-driven CUMGs. We show that the computation of approximate equilibria for CUMGs parameterized by several risk measures lies in PPAD. Consequently, we obtain finite multilinear complementarity programs for the computation of equilibrium for these games, which grow with $K$, the number of data samples. Unlike standard games, these programs are not linear in a two-player setting. Next, we establish the existence of approximate equilibria in finite data-driven CUMGs with small supports in the pure actions for the players, together with sparse data subsamples that guide the search for such equilibria. We also develop a stochastic first-order approach for smoothed CUMGs using data mini-batches, with bounds linking first-order error to approximate equilibrium. We include numerical experiments comparing the sparse-support search algorithm with complementarity-program solvers.