Information design in Bayesian routing games

TL;DR

This paper investigates how to optimally provide private information to users in Bayesian routing games to minimize travel times, revealing that uncertainty can sometimes facilitate achieving optimal network performance.

Contribution

It introduces a framework for private information design in Bayesian routing games and identifies conditions under which optimality can be achieved, highlighting the beneficial role of uncertainty.

Findings

Optimal private signals can lead to minimal travel times.

Uncertainty in network parameters can facilitate optimal routing.

Conditions for achieving optimality with private signals are established.

Abstract

We study optimal information provision in transportation networks when users are strategic and the network state is uncertain. An omniscient planner observes the network state and discloses information to the users with the goal of minimizing the expected travel time at the user equilibrium. Public signal policies, including full-information disclosure, are known to be inefficient in achieving optimality. For this reason, we focus on private signals and restrict without loss of generality the analysis to signals that coincide with path recommendations that satisfy obedience constraints, namely users have no incentive in deviating from the received recommendation according to their posterior belief. We first formulate the general problem and analyze its properties for arbitrary network topologies and delay functions. Then, we consider the case of two parallel links with affine delay functions, and provide sufficient conditions under which optimality can be achieved by information design. Interestingly, we observe that the system benefits from uncertainty, namely it is easier for the planner to achieve optimality when the variance of the uncertain parameters is large. We then provide an example where optimality can be achieved even if the sufficient conditions for optimality are not met.