动态调整招生策略，提升公平性与效益。
Sequential Cohort Selection under Uncertainty
- 用概率模型结合策略梯度，实现自适应招生决策。
- 动态策略比静态方法收益更高，尤其在高成本时优势明显。
- 神经网络策略更优且长期保持公平性，适合复杂环境。
我们研究了大学招生中存在不确定性下的公平群体选择问题，申请人结果仅部分可观测。考虑一次性设置和随时间更新策略的序列设置，提出一种融合结果概率建模与策略梯度的方法，支持逻辑回归和神经网络策略。在序列设置中，该方法联合更新策略与底层模型，以适应变化的申请人群。基于真实招生数据的模拟实验表明，自适应策略在期望效用上显著优于静态基线，尤其在较高录取成本下表现突出。神经网络策略始终获得更高效用，并更有效地适应变化，同时长期保持良好的公平性。结果凸显了在不确定性下适应性与模型表达力的重要性。
原文摘要 · Abstract (English)
We study the problem of fair cohort selection under uncertainty, motivated by university admissions where applicant outcomes are only partially observed. We consider both a one-shot setting, where a fixed policy is applied to a population, and a sequential setting, where policies are updated over time using data from previous admission years. We propose a policy optimization framework that combines probabilistic modeling of outcomes with policy gradient methods, supporting both logistic and neural network policies. In the sequential setting, the approach jointly updates the policy and the underlying models to adapt to evolving applicant populations. Experiments on a simulator grounded in real admission data show that adaptive policies substantially outperform static baselines in term of expected utility, especially under higher admission costs. Neural policies consistently achieve higher utility and adapt more effectively than simpler models, while maintaining favorable fairness properties over time. Our results demonstrate the importance of adaptivity and model expressiveness for decision-making under uncertainty.
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