arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.07027cs.GT
收藏

几乎无嫉妒、嫉妒等级与Nash社会福利匹配

Almost Envy-freeness, Envy-rank, and Nash Social Welfare Matchings

Alireza Farhadi, MohammadTaghi Hajiaghayi, Mohamad Latifian, Masoud Seddighin, Hadi Yami

摘要:

对于不可分的项目,Envy-free up to one good(EF 1)和envy-free up to any good(EFX)是envy-freeness的两个著名的扩展。它表明,EF 1总是可以保证与subadditive估值代理。与此形成鲜明对比的是,即使对于四个代理人和附加估值,EFX分配是否总是存在也是未知的。此外,EFX的最佳近似保证是Amanietian等人的$(ϕ-1) \simeq 0.61$。为了找到一个中间立场,以弥合这一差距,在本文中,我们提出了另一个公平性标准,即嫉妒自由到一个随机的好或EFR,这是弱于EFX,但强于EF 1。对于这个概念,我们提供了一个多项式时间的$0.73$ -近似分配算法。对于我们的算法,我们引入了纳什社会福利匹配,使纳什社会福利和嫉妒自由之间的连接。我们相信纳什社会福利匹配将在未来的工作中找到它的应用。

英文摘要:

Envy-free up to one good (EF1) and envy-free up to any good (EFX) are two well-known extensions of envy-freeness for the case of indivisible items. It is shown that EF1 can always be guaranteed for agents with subadditive valuations. In sharp contrast, it is unknown whether or not an EFX allocation always exists, even for four agents and additive valuations. In addition, the best approximation guarantee for EFX is $(ϕ-1) \simeq 0.61$ by Amanitidis et al.. In order to find a middle ground to bridge this gap, in this paper we suggest another fairness criterion, namely envy-freeness up to a random good or EFR, which is weaker than EFX, yet stronger than EF1. For this notion, we provide a polynomial-time $0.73$-approximation allocation algorithm. For our algorithm, we introduce Nash Social Welfare Matching which makes a connection between Nash Social Welfare and envy freeness. We believe Nash Social Welfare Matching will find its applications in future work.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑