Pareto efficient strategy-proof school choice mechanism with minimum quotas and initial endowments

Ryoji Kurata, Naoto Hamada, Chia Ling Hsu, Takamasa Suzuki, Suguru Ueda, Makoto Yokoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper develops a strategy-proof and Pareto efficient mechanism for a school choice program called Top Trading Cycles among Representatives with Supplementary Seats (TTCR-SS). We consider a setting where minimum quotas are imposed for each school, i.e., a school is required to be assigned at least a certain number of students to operate, and the obtained matching must respect initial endowments, i.e., each student must be assigned to a school that is at least as good as her initial endowment school. Although minimum quotas are relevant in school choice programs and strategy-proofness is important to many policymakers, few existing mechanisms achieve both of them simultaneously. Furthermore, existing mechanisms require that all students consider all schools acceptable to obtain a feasible matching that respects minimum quotas and cannot guarantee Pareto efficiency. TTCR-SS is based on Top Trading Cycles (TTC) mechanism, while it is significantly extended to handle the supplementary seats of schools while respecting minimum quotas. Our simulation results show TTCR-SS is significantly better than an existing TTC-based mechanism in terms of students' welfare.

Original languageEnglish
Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages59-67
Number of pages9
ISBN (Electronic)9781450342391
Publication statusPublished - 2016
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: May 9 2016May 13 2016

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
Country/TerritorySingapore
CitySingapore
Period5/9/165/13/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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