Sequential Cohort Selection

2025-08-22

AI Summary

This paper addresses the fair cohort selection problem in university admissions under unknown population distributions, distinguishing between one-shot (static, policy-predefined) and sequential (multi-stage, dynamically adjustable) settings. We propose a sequential cohort selection framework that constructs a population generative model from historical admission data and jointly optimizes for utility and fairness—incorporating both meritocratic and group-equality constraints—yielding transparent, updateable admission policies. Our contributions are threefold: (i) the first systematic modeling and comparative analysis of fairness–utility trade-offs across these two decision paradigms; (ii) a mechanism design that simultaneously ensures policy transparency and dynamic adaptability; and (iii) theoretical characterization of its utilitarian efficiency and group-level equity properties. Empirical simulations demonstrate that our approach significantly outperforms static baselines in both fairness metrics and selection quality.

Technology Category

Reasoning under Uncertainty: Sequential Decision MakingGame Theory and Economic Paradigms: Fair DivisionMachine Learning: Learning Preferences or Rankings

Application Category

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

Abstract

We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous admission cycles. We also study the fairness properties of the resulting policies in the one-shot setting, including meritocracy and group parity.

Problem

• Fair cohort selection from unknown populations
• University admissions with transparent policies
• Optimizing sequential versus one-shot admission strategies

Innovation

• Sequential cohort selection with policy updates
• Population model optimization from historical data
• Fairness analysis including meritocracy and parity