An urban planner might design the spatial layout of transportation amenities so as to improve accessibility for underserved communities -- a fairness objective. However, implementing such a design might trigger processes of neighborhood change that change who benefits from these amenities in the long term. If so, has the planner really achieved their fairness objective? Can algorithmic decision-making anticipate second order effects? In this paper, we take a step in this direction by formulating processes of neighborhood change as instances of no-regret dynamics; a collective learning process in which a set of strategic agents rapidly reach a state of approximate equilibrium. We mathematize concepts of neighborhood change to model the incentive structures impacting individual dwelling-site decision-making. Our model accounts for affordability, access to relevant transit amenities, community ties, and site upkeep. We showcase our model with computational experiments that provide semi-quantitative insights on the spatial economics of neighborhood change, particularly on the influence of residential zoning policy and the placement of transit amenities.

翻译：一位城市规划者可能设计交通设施的空间布局，以提高未被充分服务社区的交通可达性——这是一个公平性目标。然而，实施这样的设计可能引发邻里变迁过程，从而在长期改变从这些设施中受益的人群。如果是这样，规划者是否真正实现了其公平性目标？算法决策能否预见二阶效应？在本文中，我们通过将邻里变迁过程表述为无遗憾动态的实例，朝这个方向迈出了一步；这是一种集体学习过程，其中一组策略性主体迅速达到近似均衡状态。我们将邻里变迁概念数学化，以建模影响个体住宅选址决策的激励机制结构。我们的模型考虑了可负担性、相关交通设施的可达性、社区纽带以及住宅维护。我们通过计算实验展示了模型，这些实验提供了关于邻里变迁空间经济学的半定量见解，特别是居住分区政策的影响以及交通设施的布局对邻里的作用。