Social learning in nonatomic routing games

Emilien Macault, Marco Scarsini and Tristan Tomala

Games and Economic Behavior, 2022, vol. 132, issue C, 221-233

Abstract: 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.

Keywords: Routing games; Incomplete information; Social learning; Series-parallel network; Wardrop equilibrium (search for similar items in EconPapers)
JEL-codes: C72 C73 D83 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Working Paper: Social Learning in Nonatomic Routing Games (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:132:y:2022:i:c:p:221-233

DOI: 10.1016/j.geb.2022.01.003

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