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.

翻译：可操作申诉研究个体能否通过修改可行特征来推翻人工智能辅助决策系统产生的不利结果。然而，许多此类系统运行在竞争环境中（如招生或招聘），仅部分候选人能够成功。一个根本性问题随之产生：当竞争环境中所有人都能使用可操作申诉时会发生什么？本研究提出一个框架，将可操作申诉建模为风险导向选择规则下候选人之间的战略互动。被拒绝的个体沿决策规则暗示的方向努力改进可操作特征，而成功基准则随多人同时调整而内生化。这形成了内生选择机制——决策规则与选择阈值均由群体当前特征状态共同决定。该互动产生了一个连接候选人选择与战略申诉的闭环动态系统。研究表明，初始选中的候选人同时决定了成功基准和改进方向，从而放大初始差异并在群体中产生持续的表现差距。