Emilien Macault

interaction seminar

Emilien Macault

HEC Paris
Bayesian learning in non-atomic routing games

IBD Salle 21

Îlot Bernard du Bois - Salle 21

5-9 boulevard Maurice Bourdet
13001 Marseille

Thursday, October 1 2020| 12:00pm to 1:00pm

Gaëtan Fournier: gaetan.fournier[at]univ-amu.fr
Yevgeny Tsodikovich: evgeny.tsodikovich[at]univ-amu.fr


We consider a discrete-time nonatomic routing game with variable demand and uncertain costs. Given a routing network with single origin and destination, the costs functions on edges depend on some uncertain persistent state parameter. Every period, a variable traffic demand routes through the network. The experienced costs are publicly observed and the belief about the state parameter is Bayesianly updated. This paper studies the dynamics of equilibrium and beliefs. We say that there is strong learning when beliefs converge to the truth and there is weak learning when equilibrium flows converge to those under complete information. Our main result is a characterization of the networks for which learning occurs for all increasing cost functions, given highly variable demand. We prove that these networks have a series-parallel structure and provide a counterexample to prove that the condition is necessary.