Bayesian Distributionally Robust Nash Equilibrium and Its Application

TL;DR

This paper introduces a Bayesian distributionally robust Nash equilibrium model that accounts for uncertainty in probability distributions, updates with sampling, and is applicable to competitive pricing, demonstrating promising numerical results.

Contribution

It develops a novel Bayesian distributionally robust Nash equilibrium framework with convergence analysis and a Gauss-Seidel solution method for KL divergence-based ambiguity sets.

Findings

Existence of a BDRNE under certain conditions.

Asymptotic convergence of the equilibrium as sample size increases.

Numerical tests show good performance of the model and method.

Abstract

Inspired by the recent work by Shapiro et al. [45], we propose a Bayesian distributionally robust Nash equilibrium (BDRNE) model where each player lacks complete information on the true probability distribution of the underlying uncertainty represented by a random variable and subsequently determines the optimal decision by solving a Bayesian distributionally robust optimization (BDRO) problem under the Nash conjecture. Unlike most of the DRO models in the literature, the BDRO model assumes (a) the true unknown distribution of the random variable can be approximated by a randomized parametric family of distributions, (b) the average of the worst-case expected value of the objective function with respect to the posterior distribution of the parameter, instead of the worst-case expected value of the objective function is considered in each player's decision making, and (c) the posterior distribution of the parameter is updated as more and more sampling information of the random variable is gathered. Under some moderate conditions, we demonstrate the existence of a BDRNE and derive asymptotic convergence of the equilibrium as the sample size increases. Moreover, we propose to solve the BDRNE problem by Gauss-Seidel-type iterative method in the case when the ambiguity set of each player is constructed via Kullback-Leibler (KL) divergence. Finally, we apply the BDRNE model to a price competition problem under multinomial logit demand. The preliminary numerical test results show that the proposed model and computational scheme perform well.