New Directions of Study in Matching with Constraints

Kojima, Fuhito

doi:10.1007/978-3-030-18050-8_67

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…The topic of constrained matching problems has been an active area or research and it has been studied in the context of school choice and hospital residents problem apart from healthcare rationing [30,5,27,28,8,21,41]. The setting with two-sided preferences has been considered in [22,29,23,17].…”

Section: Related Workmentioning

confidence: 99%

Fair Healthcare Rationing to Maximize Dynamic Utilities

Ganesh¹,

Nimbhorkar²,

Ghosal³

et al. 2023

Preprint

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Allocation of scarce healthcare resources under limited logistic and infrastructural facilities is a major issue in the modern society. We consider the problem of allocation of healthcare resources like vaccines to people or hospital beds to patients in an online manner. Our model takes into account the arrival of resources on a day-to-day basis, different categories of agents, the possible unavailability of agents on certain days, and the utility associated with each allotment as well as its variation over time. We propose a model where priorities for various categories are modelled in terms of utilities of agents. We give online and offline algorithms to compute an allocation that respects eligibility of agents into different categories, and incentivizes agents not to hide their eligibility for some category. The offline algorithm gives an optimal allocation while the online algorithm gives an approximation to the optimal allocation in terms of total utility. Our algorithms are efficient, and maintain fairness among different categories of agents. Our models have applications in other areas like refugee settlement and visa allocation. We evaluate the performance of our algorithms on real-life and synthetic datasets. The experimental results show that the online algorithm is fast and performs better than the given theoretical bound in terms of total utility. Moreover, the experimental results confirm that our utility-based model correctly captures the priorities of categories.

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Section: Related Workmentioning

confidence: 99%

Fair Healthcare Rationing to Maximize Dynamic Utilities

Ganesh¹,

Nimbhorkar²,

Ghosal³

et al. 2023

Preprint

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“…When additional feasibility constraints are imposed in two-sided matching, several challenges come up (Kojima 2019). A stable matching or even a feasible matching may not exist and even checking the existence of such a matching can be computationally intractable.…”

Section: Generalized Deferred Acceptancementioning

confidence: 99%

“…In the seminal paper on this topic (Gale and Shapley 1962), there was already a practical constraint that was considered: the institutions had upper capacities on how many agents they can accommodate. As the theory of matching market design has developed, a consistent stream of results has focused on general two-sided matching models with various types of constraints (see, e.g., Kojima (2019)). Some of their results are aimed at abstraction, unification and generalization of existing methods for two-sided matching.…”

Section: Introductionmentioning

confidence: 99%

Matching Market Design with Constraints

Aziz

Bíró

Yokoo

2022

AAAI

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Two-sided matching is an important research area that has had a major impact on the design of real-world matching markets. One consistent feature in many of the real-world applications is that they impose new feasibility constraints that lead to research challenges. We survey developments in the field of two-sided matching with various constraints, including those based on regions, diversity, multi-dimensional capacities, and matroids.

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“…The paper is related to an active area of research on matching with distributional constraints (see, e.g., Kojima, 2019). One general class of distributional constraints that have been examined in matching market design pertains to common quotas over institutions such as hospitals Kojima, 2015, 2017;Goto et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Efficient, Fair, and Incentive-Compatible Healthcare Rationing

Aziz¹,

Brandl²

2021

Preprint

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As the COVID-19 pandemic shows no clear signs of subsiding, fair and efficient rationing of healthcare resources has emerged as an important issue that has been discussed by medical experts, policy-makers, and the general public. We consider a healthcare rationing problem where medical units are to be allocated to patients.Each unit is reserved for one of several categories and the patients have different priorities for the categories. We present an allocation rule that respects the priorities, complies with the eligibility requirements, allocates the largest feasible number of units, and does not incentivize agents to hide that they qualify through a category.Moreover, the rule is polynomial-time computable. To the best of our knowledge, it is the first known rule with the aforementioned properties.

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New Directions of Study in Matching with Constraints

Cited by 6 publications

References 16 publications

Fair Healthcare Rationing to Maximize Dynamic Utilities

Fair Healthcare Rationing to Maximize Dynamic Utilities

Matching Market Design with Constraints

Efficient, Fair, and Incentive-Compatible Healthcare Rationing

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