Social Learning in Nonatomic Routing Games

TL;DR

This paper studies how traffic routing networks with uncertain costs learn over time, showing that series-parallel networks facilitate learning while others may not.

Contribution

It characterizes the network structures that enable learning in nonatomic routing games with uncertain costs, highlighting the importance of series-parallel configurations.

Findings

Series-parallel networks support strong learning.

Learning may fail in non-series-parallel networks.

The paper provides a counterexample illustrating failure of learning.

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.