Matching with regional constraints: An equivalence
In two-sided matching market, when the regional constraints are present, the deferred acceptance (DA) algorithm suffers from undesirable inefficiency due to the artificial allocation of the regional caps among hospitals. We show that, given preferences, there exist allocations that guarantee the efficiency of the DA algorithm. Furthermore, it is equivalent to the FDA algorithm developed by Kamada and Kojima (2015), which endows the latter with an interpretation as a tool for endogenous capacity design. Our proof applies the optimality within the matching with contracts (Hatfield and Milgrom 2005) framework, offering a broadly applicable method for establishing equivalence among DA-based mechanisms.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
From Matching with Diversity Constraints to Matching with Regional Quotas
In the past few years, several new matching models have been proposed and studied that take into account complex distributional constraints. Relevant lines of work include (1) school choice with diversity constraints whe…
DiversityStrong Equivalence in Answer Set Programming with Constraints
We investigate the concept of strong equivalence within the extended framework of Answer Set Programming with constraints. Two groups of rules are considered strongly equivalent if, informally speaking, they have the sam…
TranslationEquivalence between individual and group strategy-proofness under stability
This paper studies the (group) strategy-proofness aspect of two-sided matching markets under stability. For a one-to-one matching market, we show an equivalence between individual and group strategy-proofness under stabi…
Efficient Regional Memory Network for Video Object Segmentation
Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. Ho…
ObjectOne-shot visual object segmentationOptical Flow EstimationSemantic Segmentation+3Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
We study the problem of causal structure learning from a combination of observational and interventional data generated by a linear non-Gaussian structural equation model that might contain cycles. Recent results show th…
Stochastic Optimization