Congestion Games with Distance-Based Strict Uncertainty

TL;DR

This paper introduces a new model of congestion games accounting for agents' distance-based uncertainty, analyzing equilibrium existence, convergence, and efficiency under worst-case cost and regret behaviors.

Contribution

It proposes a non-probabilistic uncertainty model in congestion games and studies the impact of worst-case cost and regret strategies on game properties.

Findings

WCC leads to a modified congestion game with improved welfare under moderate uncertainty.

WCR behavior results in non-congestion games but still allows convergence and efficiency bounds.

The model provides insights into strategic behavior under distance-based uncertainty.

Abstract

We put forward a new model of congestion games where agents have uncertainty over the routes used by other agents. We take a non-probabilistic approach, assuming that each agent knows that the number of agents using an edge is within a certain range. Given this uncertainty, we model agents who either minimize their worst-case cost (WCC) or their worst-case regret (WCR), and study implications on equilibrium existence, convergence through adaptive play, and efficiency. Under the WCC behavior the game reduces to a modified congestion game, and welfare improves when agents have moderate uncertainty. Under WCR behavior the game is not, in general, a congestion game, but we show convergence and efficiency bounds for a simple class of games.