对任何纳什福利高的物品的自由嫉妒:捐赠物品的美德

I. Caragiannis, N. Gravin, Xin Huang
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引用次数: 87

摘要

最近提出了几个公平概念,试图在具有不可分割的商品的环境中近似嫉妒自由。其中,对任何物品的无嫉妒(EFX)的概念可以说是最接近无嫉妒的。不幸的是,除了在少数特殊情况下,EFX拨款并不存在。我们在这方面取得了重大进展。我们证明,对于每一个具有附加估值的实例,存在一个项目子集的EFX分配,其纳什福利至少是原始项目集最大可能纳什福利的一半。也就是说,在捐赠一些物品给慈善机构后,可以公平高效地分配剩下的物品。这个界被证明是最好的可能。我们的证明具有建设性,突出了纳什福利分配最大化的重要性。从这样的分配开始,我们的算法决定捐赠哪些物品,并将初始捆绑包重新分配给代理,最终获得具有声称的效率保证的分配。将我们的算法应用于大型市场,其中每个项目的代理估值相对较小,产生的EFX几乎具有最优纳什福利。我们还表明,我们的算法可以修改为在多项式时间内计算最优纳什福利的EFX分配,使用对输入的ρ近似分配而不是最大纳什福利分配。
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Envy-Freeness Up to Any Item with High Nash Welfare: The Virtue of Donating Items
Several fairness concepts have been proposed recently in attempts to approximate envy-freeness in settings with indivisible goods. Among them, the concept of envy-freeness up to any item (EFX) is arguably the closest to envy-freeness. Unfortunately, EFX allocations are not known to exist except in a few special cases. We make significant progress in this direction. We show that for every instance with additive valuations, there is an EFX allocation of a subset of items with a Nash welfare that is at least half of the maximum possible Nash welfare for the original set of items. That is, after donating some items to a charity, one can distribute the remaining items in a fair way with high efficiency. This bound is proved to be best possible. Our proof is constructive and highlights the importance of maximum Nash welfare allocation. Starting with such an allocation, our algorithm decides which items to donate and redistributes the initial bundles to the agents, eventually obtaining an allocation with the claimed efficiency guarantee. The application of our algorithm to large markets, where the valuations of an agent for every item is relatively small, yields EFX with almost optimal Nash welfare. We also show that our algorithm can be modified to compute, in polynomial-time, EFX allocations that approximate optimal Nash welfare within a factor of at most 2ρ, using a ρ-approximate allocation on input instead of the maximum Nash welfare one.
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