研究交通网络扩张如何因学习速度差异加剧出行不平等
Inequality in Congestion Games with Learning Agents
- 将通勤者建模为学习速率不同的强化学习代理
- 网络扩容提升效率但使快学习者获益更多，不平等加剧
- 提出'学习代价'衡量学习过程中的效率损失，适合政策设计者
谁从交通网络扩展中受益？尽管旨在改善出行，此类干预也可能引发不平等。本文表明，不平等不仅源于网络结构，也来自通勤者适应能力的差异。我们建模通勤者为强化学习代理，其学习速率不同，反映资源与信息获取的不均。为捕捉潜在的效率-公平权衡，引入‘学习代价’（PoL）来度量学习过程中的低效。分析了一个受著名布雷思悖论启发的简化网络（含双源节点）及阿姆斯特丹地铁系统的抽象模型。仿真显示，网络扩展可同时提升效率并放大不平等，尤其当快速学习者率先利用新路线时。结果强调，交通政策必须考虑均衡结果与通勤者异质性适应方式，二者共同决定效率与公平的平衡。
原文摘要 · Abstract (English)
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.
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