不确定性下的数据驱动平权行动政策
Towards Data-Driven Affirmative Action Policies under Uncertainty
摘要：
在本文中，我们研究了集中系统下的大学招生，该系统使用等级和标准化考试成绩来匹配申请人和大学课程。我们考虑采取平权行动政策，以增加来自代表性不足群体的被录取申请人的人数。由于这一政策必须在申请期开始前公布，因此申请每个项目的学生的分数分布存在不确定性。这对政策制定者来说是一个严峻的挑战。我们探索了使用历史数据训练的预测模型来帮助优化这些政策参数的可能性。
英文摘要：
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups. Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program. This poses a difficult challenge for policy-makers. We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
