Multi-rank smart reserves: A general framework for selection and matching diversity goals

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haris Aziz, Zhaohong Sun
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引用次数: 0

Abstract

We study a problem where each school has flexible multi-ranked diversity goals, and each student may belong to multiple overlapping types, and consumes only one of the positions reserved for their types. We propose a novel choice function for a school to select students and show that it is the unique rule that satisfies three fundamental properties: maximal diversity, non-wastefulness, and justified envy-freeness. We provide a fast polynomial-time algorithm for our choice function that is based on the Dulmage Mendelsohn Decomposition Theorem as well as new insights into the combinatorial structure of constrained rank maximal matchings. Even for the case of minimum and maximum quotas for types (that capture two ranks), ours is the first known polynomial-time approach to compute an optimally diverse choice outcome. Finally, we prove that the choice function we design for schools, satisfies substitutability and hence can be directly embedded in the generalized deferred acceptance algorithm to achieve strategyproofness and stability. Our algorithms and results have immediate policy implications and directly apply to a variety of scenarios, such as where hiring positions or scarce medical resources need to be allocated while taking into account diversity concerns or ethical principles.
多等级智能储备:选择和匹配多样性目标的总体框架
我们研究了这样一个问题:每所学校都有灵活的多等级多样性目标,每个学生可能属于多个重叠类型,并且只消耗为其类型预留的一个位置。我们为学校选择学生提出了一个新颖的选择函数,并证明它是唯一满足三个基本属性的规则:最大多样性、不浪费和无嫉妒。我们为我们的选择函数提供了一种快速多项式时间算法,该算法基于 Dulmage Mendelsohn 分解定理以及对受限秩最大匹配的组合结构的新见解。即使对于类型的最小和最大配额(捕捉两个等级),我们的方法也是第一个已知的多项式时间计算最优多样化选择结果的方法。最后,我们证明了我们为学校设计的选择函数满足可替代性,因此可以直接嵌入广义递延接受算法,从而实现策略防错和稳定性。我们的算法和结果具有直接的政策意义,可直接应用于各种情况,如需要分配招聘职位或稀缺医疗资源,同时考虑多样性问题或道德原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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