We consider a discrete-time nonatomic routing game with variable demand and uncertain costs. Given a fixed 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.
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 traffc demand is routed through the network according to a Bayes-Wardrop equilibrium. The realized costs are publicly observed and the 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 prove that the condition is necessary.
D
fixed
R
costs functions on edges depend on
cost function of each edge depends on
R
Every
At every
R
variable traffic demand routes
random traffc demand is routed
R
. The experienced
according to a Bayes-Wardrop equilibrium. The realized
A
Bayesian
R
Bayesianly updated. This paper studies the dynamics of equilibrium and beliefs.
updated.
D
there is
R
equilibrium flows converge to those under complete information. Our main result is a characterization of
the equilibrium flow converges to
A
complete-information flow. We characterize the
D
for all increasing cost functions, given highly variable demand