Strategyproof Allocation Mechanisms with Endowments and M-convex Distributional Constraints

Suzuki, Takamasa; Tamura, Akihisa; Yahiro, Kentaro; Yokoo, Makoto; Zhang, Yuzhe

doi:10.1016/j.artint.2022.103825

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…The daycare matching problem can be seen as a generalization of hospital-doctor matching with couples, where pairs of doctors participate in the market and aim to secure a pair of positions (Kojima, Pathak, and Roth 2013;Biró, Manlove, and McBride 2014;Nguyen and Vohra 2018). While some papers focus on school choice with initial enrollment, they do not take siblings into consideration (Hamada et al 2017;Suzuki, Tamura, and Yokoo 2018;Suzuki et al 2023). On the other hand, two recent papers delve into school choice with siblings, but assuming restrictive preferences of families (Dur, Morrill, and Phan 2022;Correa et al 2022).…”

Section: Related Workmentioning

confidence: 99%

Stable Matchings in Practice: A Constraint Programming Approach

Sun,

Yamada,

Takenami

et al. 2024

AAAI

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We study a practical two-sided matching problem of allocating children to daycare centers, which has significant social implications. We are cooperating with several municipalities in Japan and our goal is to devise a reliable and trustworthy clearing algorithm to deal with the problem. In this paper, we describe the design of our new algorithm that minimizes the number of unmatched children while ensuring stability. We evaluate our algorithm using real-life data sets, and experimental results demonstrate that our algorithm surpasses the commercial software that currently dominates the market in terms of both the number of matched children and the number of blocking coalitions (measuring stability). Our findings have been reported to local governments, and some are considering adopting our proposed algorithm in the near future, instead of the existing solution. Moreover, our model and algorithm have broader applicability to other important matching markets, such as hospital-doctor matching with couples and school choice with siblings.

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Section: Related Workmentioning

confidence: 99%

Stable Matchings in Practice: A Constraint Programming Approach

Sun,

Yamada,

Takenami

et al. 2024

AAAI

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“…Dur, Morrill, and Phan (2022), as well as Correa et al (2022), consider school choice with siblings under the assumption of restrictive families' preferences. Hamada et al (2017) and Suzuki, Tamura, and Yokoo (2018) study school choice with initial enrollment without siblings. The problem of refugee reallocation also deals with families instead of individuals (Delacrétaz, Kominers, and Teytelboym 2016;Aziz et al 2018), but family members are inseparable and they do not submit preferences over combinations of localities.…”

Section: Related Workmentioning

confidence: 99%

Daycare Matching in Japan: Transfers and Siblings

Sun

Yoshihiro

Moriwaki

et al. 2023

AAAI

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In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm, which is going to be deployed in one municipality in the Tokyo metropolis. There are two features that make this market different from the classical hospital-doctor matching problem: i) some children are initially enrolled and prefer to be transferred to other daycare centers; ii) one family may be associated with two or more children and is allowed to submit preferences over combinations of daycare centers. We revisit some well-studied properties including individual rationality, non-wastefulness, as well as stability, and generalize them to this new setting. We design an algorithm based on integer programming (IP) that captures these properties and conduct experiments on five real-life data sets provided by three municipalities. Experimental results show that i) our algorithm performs at least as well as currently used methods in terms of numbers of matched children and blocking coalition; ii) we can find a stable outcome for all instances, although the existence of such an outcome is not guaranteed in theory.

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“…Furthermore, diversity constraints ensure a balanced mix of different student types within a school [8][9][10][11][12]. This topic has been receiving increased attention from AI researchers [13][14][15][16][17]. Throughout this paper, we focus on the context of a school-student allocation problem to enhance reader comprehension, although the results are applicable to general allocation problems.…”

Section: Introductionmentioning

confidence: 99%

Strategyproof and fair matching mechanism for union of symmetric m-convex constraints

Barrot,

Yahiro,

Yokoo

et al. 2024

PLoS ONE

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We identify a new class of distributional constraints defined as a union of symmetric M-convex sets, which can represent a wide range of real-life constraints in two-sided matching settings. Since M-convexity is not closed under union, a union of symmetric M-convex sets does not belong to this well-behaved class of constraints. Consequently, devising a fair and strategyproof mechanism to handle this new class is challenging. We present a novel mechanism for it called Quota Reduction Deferred Acceptance (QRDA), which repeatedly applies the standard Deferred Acceptance mechanism by sequentially reducing artificially introduced maximum quotas. We show that QRDA is fair and strategyproof when handling a union of symmetric M-convex sets, which extends previous results obtained for a subclass of the union of symmetric M-convex sets: ratio constraints. QRDA always yields a weakly better matching for students than a baseline mechanism called Artificial Cap Deferred Acceptance (ACDA). We also experimentally show that QRDA outperforms ACDA in terms of nonwastefulness.

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Strategyproof Allocation Mechanisms with Endowments and M-convex Distributional Constraints

Cited by 6 publications

References 52 publications

Stable Matchings in Practice: A Constraint Programming Approach

Stable Matchings in Practice: A Constraint Programming Approach

Daycare Matching in Japan: Transfers and Siblings

Strategyproof and fair matching mechanism for union of symmetric m-convex constraints

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