Management Science · 2026 · Rémi Castera、Patrick Loiseau、Bary S. R. Pradelski
中文摘要
我们研究匹配市场中相关性的作用,其中多个决策者同时面对来自同一候选池的选择问题。我们提出一个模型,其中同一候选人在不同决策者处的优先得分呈现出不同程度的相关性,且取决于该候选人所处的社会人口群体。这种差异性相关可能出现在学校择校中(源于筛选标准适用程度的不同)、大学招生中(源于标化可选政策),也可能源于算法单一化,即决策者依赖相同的算法和数据集来评估候选人。我们证明,其中一个群体具有更高的相关性通常会使所有群体的结果改善,从而带来更高的效率。然而,某一群体的学生随着其自身相关性水平的提高,更有可能处于未被匹配的状态。这意味着,属于低相关性群体是有利的。最后,我们将打破平局(tie-breaking)的文献拓展至多个优先类别与中间水平的相关性。总体而言,我们的结果指出,差异性相关是一个此前被忽视的、造成学校、大学和就业录取中群体不平等的系统性来源。本文由收益管理与市场分析领域编辑 Itai Ashlagi 接受。资助声明:本研究部分得到 MIAI @ Grenoble Alpes [资助号 ANR-19-P3IA-0003 与 ANR-23-PEIA-0003]、法国国家科研署(ANR)[资助号 ANR-19-CE48-0018 与 ANR-20-CE23-0007]、美国国家科学基金会 [资助号 DMS-1928930] 以及 Alfred P. Sloan 基金会 [资助号 G-2021-16778] 的支持,后者资助了 B. Pradelski 于 2023 年秋季学期在加州伯克利 Simons Laufer 数学科学研究所(原 MSRI)的驻留。补充材料:在线附录与数据文件可于 https://doi.org/10.1287/mnsc.2024.06067 获取。
Abstract
We study the role of correlation in matching markets, where multiple decision makers simultaneously face selection problems from the same pool of candidates. We propose a model in which a candidate’s priority scores across different decision makers exhibit varying levels of correlation dependent on the candidate’s sociodemographic group. Such differential correlation can arise in school choice because of the varying prevalence of selection criteria, in college admissions because of test-optional policies, or because of algorithmic monoculture, that is, when decision makers rely on the same algorithms and data sets to evaluate candidates. We show that higher correlation for one of the groups generally improves the outcome for all groups, leading to higher efficiency. However, students from a given group are more likely to remain unmatched as their own correlation level increases. This implies that it is advantageous to belong to a low-correlation group. Finally, we extend the tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to differential correlation as a previously overlooked systemic source of group inequalities in school, university, and job admissions. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: This work was partially supported by MIAI @ Grenoble Alpes [Grants ANR-19-P3IA-0003 and ANR-23-PEIA-0003], by the French National Research Agency (ANR) [Grants ANR-19-CE48-0018 and ANR-20-CE23-0007], and by the National Science Foundation [Grant DMS-1928930] and by the Alfred P. Sloan Foundation [Grant G-2021-16778], which funded B. Pradelski’s residency at the Simons Laufer Mathematical Sciences Institute (formerly MSRI) in Berkeley, CA, during the Fall 2023 semester. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06067 .