Optimal dynamic information provision in traffic routing

TL;DR

This paper studies how a central planner can optimally incentivize strategic agents to experiment with a risky road in a dynamic traffic routing game, balancing information gathering and congestion effects.

Contribution

It characterizes the optimal incentive-compatible recommendation system for dynamic information sharing in a strategic routing game, accounting for congestion and experimentation trade-offs.

Findings

Optimal recommendations induce partial information sharing.

Limiting risky road usage encourages experimentation.

Incentive compatibility depends on congestion and road state.

Abstract

We consider a two-road dynamic routing game where the state of one of the roads (the "risky road") is stochastic and may change over time. This generates room for experimentation. A central planner may wish to induce some of the (finite number of atomic) agents to use the risky road even when the expected cost of travel there is high in order to obtain accurate information about the state of the road. Since agents are strategic, we show that in order to generate incentives for experimentation the central planner however needs to limit the number of agents using the risky road when the expected cost of travel on the risky road is low. In particular, because of congestion, too much use of the risky road when the state is favorable would make experimentation no longer incentive compatible. We characterize the optimal incentive compatible recommendation system, first in a two-stage game and then in an infinite-horizon setting. In both cases, this system induces only partial, rather than full, information sharing among the agents (otherwise there would be too much exploitation of the risky road when costs there are low).