Social learning in nonatomic routing games

• 成果类型: Article
• 署名作者: Macault, Emilien; Scarsini, Marco; Tomala, Tristan
• 署名单位: Hautes Etudes Commerciales (HEC) Paris; Luiss Guido Carli University
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2022.01.003
• 发表日期: 2022
• 页码: 221-233
• 关键词: Routing games; incomplete information; social learning; Series-parallel network; Wardrop equilibrium

摘要

We consider a discrete-time nonatomic routing game with variable demand and uncertain costs. Given a routing network with single origin and destination, the cost function of each edge depends on some uncertain persistent state parameter. At every period, a random traffic demand is routed through the network according to a Wardrop equilibrium. The realized costs are publicly observed and the public Bayesian belief about the state parameter is updated. We say that there is strong learning when beliefs converge to the truth and weak learning when the equilibrium flow converges to the complete-information flow. We characterize the networks for which learning occurs. We prove that these networks have a series-parallel structure and provide a counterexample to show that learning may fail in non-series-parallel networks. (C) 2022 Elsevier Inc. All rights reserved.