Inequality in Congestion Games with Learning Agents

Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos

Keywords: Congestion Games, Reinforcement Learning, Multi-Agent Systems, Fairness, Transport Planning

TL;DR: We show that expanding transport networks can improve efficiency but also deepen inequality, since faster-learning commuters adapt quickly to new routes while slower learners are left behind.

Abstract: Who benefits from expanding transport networks? While designed to improve mobility, such interventions can also create inequality. In this paper, we show that disparities arise not only from the structure of the network itself but also from differences in how commuters adapt to it. We model commuters as reinforcement learning agents who adapt their travel choices at different learning rates, reflecting unequal access to resources and information. To capture potential efficiency-fairness tradeoffs, we introduce the Price of Learning (PoL), a measure of inefficiency during learning. We analyze both a stylized network --- inspired in the well-known Braess's paradox, yet with two-source nodes --- and an abstraction of a real-world metro system (Amsterdam). Our simulations show that network expansions can simultaneously increase efficiency and amplify inequality, especially when faster learners disproportionately benefit from new routes before others adapt. These results highlight that transport policies must account not only for equilibrium outcomes but also for the heterogeneous ways commuters adapt, since both shape the balance between efficiency and fairness.

Area: Modelling and Simluation of Societies (SIM)

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Submission Number: 1131