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

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
出版社International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
ページ59-67
ページ数9
ISBN(電子版)9781450342391
出版ステータス出版済み - 2016
イベント15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, シンガポール
継続期間: 5月 9 20165月 13 2016

出版物シリーズ

名前Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN(印刷版)1548-8403
ISSN(電子版)1558-2914

その他

その他15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
国/地域シンガポール
CitySingapore
Period5/9/165/13/16

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • ソフトウェア
  • 制御およびシステム工学

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