Stable Matchings with Diversity Constraints: Affirmative Action is beyond NP

Stable Matchings with Diversity Constraints: Affirmative Action is beyond NP

Jiehua Chen, Robert Ganian, Thekla Hamm

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 146-152. https://doi.org/10.24963/ijcai.2020/21

We investigate the following many-to-one stable matching problem with diversity constraints (SMTI-DIVERSE): Given a set of students and a set of colleges which have preferences over each other, where the students have overlapping types, and the colleges each have a total capacity as well as quotas for individual types (the diversity constraints), is there a matching satisfying all diversity constraints such that no unmatched student-college pair has an incentive to deviate? SMTI-DIVERSE is known to be NP-hard. However, as opposed to the NP-membership claims in the literature [Aziz et al., AAMAS 2019; Huang,SODA 2010], we prove that it is beyond NP: it is complete for the complexity class Σ^P_2. In addition, we provide a comprehensive analysis of the problem’s complexity from the viewpoint of natural restrictions to inputs and obtain new algorithms for the problem.
Keywords:
Agent-based and Multi-agent Systems: Algorithmic Game Theory
Agent-based and Multi-agent Systems: Computational Social Choice
Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems