Controlled school choice with soft bounds and overlapping types

Ryoji Kurata, Naoto Hamada, Atsushi Iwasaki, Makoto Yokoo

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


School choice programs are implemented to give students/parents an opportunity to choose the public school the students attend. Controlled school choice programs need to provide choices for students/parents while maintaining distributional constraints on the composition of students, typically in terms of socioeconomic status. Previous works show that setting soft-bounds, which exibly change the priorities of students based on their types, is more appropriate than setting hard-bounds, which strictly limit the number of accepted students for each type. We consider a case where soft-bounds are imposed and one student can belong to multiple types, e.g., \financially-distressed" and \minority" types. We first show that when we apply a model that is a straightforward extension of an existing model for disjoint types, there is a chance that no stable matching exists. Thus we propose an alternative model and an alternative stability definition, where a school has reserved seats for each type. We show that a stable matching is guaranteed to exist in this model and develop a mechanism called Deferred Acceptance for Overlapping Types (DA-OT). The DA-OT mechanism is strategy-proof and obtains the student-optimal matching within all stable matchings. Furthermore, we introduce an extended model that can handle both type-specific ceilings and oors and propose a extended mechanism DA-OT∗ to handle the extended model. Computer simulation results illustrate that DA-OT outperforms an artificial cap mechanism where we set a hard-bound for each type in each school. DA-OT∗ can achieve stability in the extended model without sacrificing students' welfare.

Original languageEnglish
Pages (from-to)153-184
Number of pages32
JournalJournal of Artificial Intelligence Research
Publication statusPublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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