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Which is the Fairest (Rent Division) of Them All?

机译:哪个是他们最公平的(租姓)?

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What is a fair way to assign rooms to several housemates, and divide the rent between them? This is not just a theoretical question: many people have used the Spliddit website to obtain envy-free solutions to rent division instances. But envy freeness, in and of itself, is insufficient to guarantee outcomes that people view as intuitive and acceptable. We therefore focus on solutions that optimize a criterion of social justice, subject to the envy freeness constraint, in order to pinpoint the "fairest" solutions. We develop a general algorithmic framework that enables the computation of such solutions in polynomial time. We then study the relations between natural optimization objectives, and identify the maximin solution, which maximizes the minimum utility subject to envy freeness, as the most attractive. We demonstrate, in theory and using experiments on real data from Spliddit, that the maximin solution gives rise to significant gains in terms of our optimization objectives. Finally, a user study with Spliddit users as subjects demonstrates that people find the maximin solution to be significantly fairer than arbitrary envy-free solutions; this user study is unprecedented in that it asks people about their real-world rent division instances. Based on these results, the maximin solution has been deployed on Spliddit since April 2015.
机译:将房间分配给几个室友的公平方式是什么,并将租金分配在它们之间?这不仅仅是一个理论问题:很多人都使用了Spliddit网站来获得租赁部门实例的免费解决方案。但羡慕的狂喜,本身就不足以保证人们认为直观和可接受的结果。因此,我们专注于优化社会正义标准的解决方案,而受到嫉妒的狂热约束,以便确定“最公平”的解决方案。我们开发了一般的算法框架,其能够计算多项式时间中的这种解决方案。然后,我们研究了自然优化目标之间的关系,并识别最大化的最大实用程序可能受到嫉妒的速度,最具吸引力。我们在理论上和使用Spliddit的真实数据的实验证明了Maximin解决方案在我们的优化目标方面产生了显着提升。最后,使用诸如受试者的Spliddit用户的用户学习表明,人们发现最大的解决方案明显更公平,而不是任意envy解决方案;这项用户学习是前所未有的,因为它询问人们对其真实租金的司法实例。基于这些结果,自2015年4月以来,Maximin解决方案已在Spliddit上部署。

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