竞争环境中的可操作救济：内生选择的动态博弈
Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection
AI总结：
本文研究了在竞争性环境中，当所有个体均可操作救济时，如何通过风险为基础的选择规则进行内生选择，分析了初始选择对绩效差距的放大作用。
摘要：
本文研究了在竞争性环境中，当所有个体均可操作救济时，通过风险为基础的选择规则进行内生选择的可能性。然而，许多此类系统在竞争性设置中运行，如录取或招聘，其中只有少数候选人能够成功。一个基本问题随之产生：当所有个体在竞争性环境中均可获得救济时会发生什么？本文提出一个框架，将救济建模为候选人之间的战略互动，基于风险的选择规则。被拒选手会努力改进可操作特征，以符合决策规则所暗示的方向，而成功标准会随着许多候选人同时调整而内生演变。这产生了内生选择，其中决策规则和选择阈值均由当前特征状态决定。这种互动产生了一个闭环动态系统，将候选人选择与战略救济联系起来。我们证明，最初被选中的候选人决定了成功标准和改进方向，从而放大初始差异，并在群体中产生持续的绩效差距。
英文摘要：
Actionable recourse studies whether individuals can modify feasible features to overturn unfavorable outcomes produced by AI-assisted decision-support systems. However, many such systems operate in competitive settings, such as admission or hiring, where only a fraction of candidates can succeed. A fundamental question arises: what happens when actionable recourse is available to everyone in a competitive environment? This study proposes a framework that models recourse as a strategic interaction among candidates under a risk-based selection rule. Rejected individuals exert effort to improve actionable features along directions implied by the decision rule, while the success benchmark evolves endogenously as many candidates adjust simultaneously. This creates endogenous selection, in which both the decision rule and the selection threshold are determined by the population's current feature state. This interaction generates a closed-loop dynamical system linking candidate selection and strategic recourse. We show that the initially selected candidates determine both the benchmark of success and the direction of improvement, thereby amplifying initial disparities and producing persistent performance gaps across the population.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
