Urgency-aware Optimal Routing in Repeated Games through Artificial Currencies

TL;DR

This paper introduces an urgency-aware incentive mechanism using artificial currencies to align selfish routing behavior with system-optimal solutions in repeated games, effectively reducing perceived discomfort by 14-20%.

Contribution

It proposes a novel artificial currency-based incentive framework for repeated routing games that accounts for agents' urgency and achieves system optimality.

Findings

Routing converges to system-optimal solutions with proper pricing.

Reduces agents' perceived discomfort by 14-20%.

Framework accommodates agents' temporal preferences.

Abstract

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish agents in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby agents commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the agents are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the agents to be rational and model their choices through a game where each agent aims at minimizing a combination of today's discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the agents' urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst reducing the agents' perceived discomfort by 14-20% when compared to a centralized optimal but urgency-unaware policy.