Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g., loan approvals). While prior research has emphasized robustness to model updates, considerably less attention has been given to the temporal dynamics of recourse--particularly in competitive, resource-constrained settings where recommendations shape future applicant pools. In this work, we present a novel time-aware framework for algorithmic recourse, explicitly modeling how candidate populations adapt in response to recommendations. Additionally, we introduce a novel reinforcement learning (RL)-based recourse algorithm that captures the evolving dynamics of the environment to generate recommendations that are both feasible and valid. We design our recommendations to be durable, supporting validity over a predefined time horizon T. This durability allows individuals to confidently reapply after taking time to implement the suggested changes. Through extensive experiments in complex simulation environments, we show that our approach substantially outperforms existing baselines, offering a superior balance between feasibility and long-term validity. Together, these results underscore the importance of incorporating temporal and behavioral dynamics into the design of practical recourse systems.

翻译：算法追索旨在为个体提供可操作的建议，以增加其在自动化决策系统（如贷款审批）中获得有利结果的机会。尽管先前研究强调了对模型更新的鲁棒性，但对追索的时间动态性——尤其是在竞争性、资源受限的环境中，建议会塑造未来申请者群体——的关注却相对较少。在本研究中，我们提出了一种新颖的、具有时间感知的算法追索框架，明确建模候选群体如何根据建议进行适应性调整。此外，我们引入了一种基于强化学习（RL）的新型追索算法，该算法捕捉环境的动态演变，以生成既可行又有效的建议。我们设计的建议具有持久性，确保在预定义的时间范围 T 内保持有效性。这种持久性使个体在花费时间实施建议的变更后，能够充满信心地重新申请。通过在复杂模拟环境中进行大量实验，我们证明我们的方法显著优于现有基线，在可行性与长期有效性之间实现了更优的平衡。总之，这些结果强调了将时间和行为动态纳入实际追索系统设计的重要性。