濱田 直斗(九州大学 システム情報科学府 情報学専攻)
鈴木 貴晶(九州大学 システム情報科学研究院)
櫻井 祐子(産業技術総合研究所 人工知能研究センター)
横尾 真(九州大学 大学院システム情報科学研究院)
|概要||This paper considers many-to-one matching problems (e.g. matching students to schools) with distributional constraints.|
We study a problem where distributional constraints are imposed in relative terms.
In standard settings, distributional constraints are often imposed in absolute terms, e.g., maximum quota of a school.
However, there are many real-life situations where distributional constraints are expressed with relative terms such as ratio or proportion.
As far as we are aware of, there is very limited literature addressing such kind of constraints.
In this paper, we define the matching problems with relative distributional constraints and introduce a new quota reduction mechanism that works under the setting.
We theoretically show that our mechanism satisfies fairness and strategyproofness, and by comparing with an artificial cap mechanism via simulation
we further illustrate that our mechanism has an advantage in terms of efficiency.