The past few years have seen a surge of work on fairness in allocation problems where items must be fairly divided among agents having individual preferences. In comparison, fairness in settings with preferences on both sides, that is, where agents have to be matched to other agents, has received much less attention. Moreover, two-sided matching literature has largely focused on ordinal preferences. This paper initiates the study of fairness in stable many-to-one matchings under cardinal valuations. Motivated by real-world settings, we study leximin optimality over stable many-to-one matchings. We first investigate matching problems with ranked valuations where all agents on each side have the same preference orders or rankings over the agents on the other side (but not necessarily the same valuations). Here, we provide a complete characterisation of the space of stable matchings. This leads to FaSt, a novel and efficient algorithm to compute a leximin optimal stable matching under ranked isometric valuations (where, for each pair of agents, the valuation of one agent for the other is the same). Building upon FaSt, we present an efficient algorithm, FaSt-Gen, that finds the leximin optimal stable matching for a more general ranked setting. When there are exactly two agents on one side who may be matched to many agents on the other, strict preferences are enough to guarantee an efficient algorithm. We next establish that, in the absence of rankings and under strict preferences (with no restriction on the number of agents on either side), finding a leximin optimal stable matching is NP-Hard. Further, with weak rankings, the problem is strongly NP-Hard, even under isometric valuations. In fact, when additivity and non-negativity are the only assumptions, we show that, unless P=NP, no efficient polynomial factor approximation is possible.
翻译:过去几年来,在分配问题公平性问题上,项目必须由具有个人偏好的代理商公平分配。相比之下,双方偏好环境的公平性,即代理商必须与其他代理商相匹配的公平性得到的关注要少得多。此外,双向匹配文献在很大程度上侧重于正态偏好。本文启动了在基本估值下稳定多对一匹配的公平性研究。受现实世界环境的驱动,我们研究了相对于稳定的多对一比偏好者之间公平分配项目的最佳性。我们首先调查了与排名估值相匹配的问题,在排名中,双方所有代理商都有相同的偏好订单或排序(但不一定与其他代理商相匹配),这里我们提供了稳定的匹配空间的完整特征。这导致FaStal,一种新而有效的算法,一种在排序下最稳的匹配率(对于每对一对一的代理商来说,一个代理商的估值是相同的。对于另一个代理商的比重,在排序中,一个比一个更稳定的代理商更稳定,在比一个更稳定的代理商更稳定的排序中,我们更稳定的一个代理商在排序上,一个更稳定。