{"type":"ScholarlyArticle","title":"Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search","authors":[{"name":"Kenshi Abe","slug":"kenshi-abe","given":"Kenshi","family":"Abe","affiliation":"CyberAgent , Inc."},{"name":"Junpei Komiyama","slug":"junpei-komiyama","given":"Junpei","family":"Komiyama","affiliation":"New York University"},{"name":"Atsushi Iwasaki","slug":"atsushi-iwasaki","given":"Atsushi","family":"Iwasaki","affiliation":"University of Electro-Communications"}],"abstract":"This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical residency match, each hospital accepts a limited number of doctors. Such capacity constraints are typically given in advance. However, such exogenous constraints can compromise the welfare of the doctors; some popular hospitals inevitably dismiss some of their favorite doctors. Meanwhile, it is often the case that the hospitals are also benefited to accept a few extra doctors. To tackle the problem, we propose an anytime method that the upper confidence tree searches the space of capacity expansions, each of which has a resident-optimal stable assignment that the deferred acceptance method finds. Constructing a good search tree representation significantly boosts the performance of the proposed method. Our simulation shows that the proposed method identifies an almost optimal capacity expansion with a significantly smaller computational budget than exact methods based on mixed-integer programming.","doi":"10.24963/ijcai.2022/1","url":"https://ijcai.org/proceedings/2022/1","pdf":"https://ijcai.org/proceedings/2022/0001.pdf","markdown":"https://ijcai.org/proceedings/2022/1.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/1","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":1,"pages":{"first":3,"last":9},"keywords":["Search: Search and Machine Learning","Agent-based and Multi-agent Systems: Resource Allocation","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Mechanism Design"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986785","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984928","length_sec":320,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abdulkadiroglu and Sönmez, 2003] Atila Abdulkadiroglu and Tayfun Sönmez. 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