Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection

2026-03-18

AI Summary

This study addresses how fairness and outcome distributions are affected in competitive decision-making settings—such as admissions or hiring—when individuals can strategically improve their features through actionable recourse. It is the first to frame actionable recourse within a competitive dynamic game, modeling agents’ strategic behavior under risk-based selection rules and introducing a success threshold that endogenously evolves with group characteristics. Through dynamic systems and game-theoretic analysis, the work reveals that early-selected individuals not only establish the benchmark for success but also shape the direction of subsequent improvements. This endogenous threshold mechanism amplifies initial disparities, leading to persistent and widening performance gaps between groups. The findings demonstrate that ostensibly fair recourse mechanisms may, in competitive environments, exacerbate long-term inequities.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• actionable recourse
• competitive environments
• endogenous selection
• strategic interaction
• performance gaps

Innovation

• actionable recourse
• competitive environments
• endogenous selection
• dynamic game
• strategic interaction