When Does Diversity of Agent Preferences Improve Outcomes in Selfish Routing?

When Does Diversity of Agent Preferences Improve Outcomes in Selfish Routing?

Richard Cole, Thanasis Lianeas, Evdokia Nikolova

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 173-179. https://doi.org/10.24963/ijcai.2018/24

We seek to understand when heterogeneity in agent preferences yields improved outcomes in terms of overall cost. That this might be hoped for is based on the common belief that diversity is advantageous in many multi-agent settings. We investigate this in the context of routing. Our main result is a sharp characterization of the network settings in which diversity always helps, versus those in which it is sometimes harmful. Specifically, we consider routing games, where diversity arises in the way that agents trade-off two criteria (such as time and money, or, in the case of stochastic delays, expectation and variance of delay). Our main contributions are: 1) A participant-oriented measure of cost in the presence of agent diversity; 2) A full characterization of those network topologies for which diversity always helps, for all latency functions and demands.
Keywords:
Agent-based and Multi-agent Systems: Multi-agent Planning
Agent-based and Multi-agent Systems: Noncooperative Games
Agent-based and Multi-agent Systems: Algorithmic Game Theory
Agent-based and Multi-agent Systems: Resource Allocation