Strategyproof and fair matching mechanism for ratio constraints

Kentaro Yahiro, Yuzhe Zhang, Nathanaël Barrot, Makoto Yokoo

Research output: Contribution to journalArticle

Abstract

We introduce a new type of distributional constraints called ratio constraints, which explicitly specify the required balance among schools in two-sided matching. Since ratio constraints do not belong to the known well-behaved class of constraints called M-convex set, developing a fair and strategyproof mechanism that can handle them is challenging. We develop a novel mechanism called quota reduction deferred acceptance (QRDA), which repeatedly applies the standard DA by sequentially reducing artificially introduced maximum quotas. As well as being fair and strategyproof, QRDA always yields a weakly better matching for students compared to a baseline mechanism called artificial cap deferred acceptance (ACDA), which uses predetermined artificial maximum quotas. Finally, we experimentally show that, in terms of student welfare and nonwastefulness, QRDA outperforms ACDA and another fair and strategyproof mechanism called Extended Seat Deferred Acceptance (ESDA), in which ratio constraints are transformed into minimum and maximum quotas.

Original languageEnglish
Article number23
JournalAutonomous Agents and Multi-Agent Systems
Volume34
Issue number1
DOIs
Publication statusPublished - Apr 1 2020

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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