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

TL;DR

This paper models how individuals in competitive AI-assisted decision environments strategically exert effort to improve features for recourse, leading to persistent disparities and endogenous selection effects.

Contribution

It introduces a dynamic game framework capturing strategic effort and endogenous selection in competitive recourse scenarios, a novel approach in this context.

Findings

Endogenous selection influences success thresholds and effort directions.

Initial disparities are amplified through strategic interactions.

Persistent performance gaps emerge over time.

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.