论文标题
在词典偏好下公平有效的分配
Fair and Efficient Allocations under Lexicographic Preferences
论文作者
论文摘要
嫉妒的富裕(EFX)为不可分割的商品分配提供了公平性的强烈而直观的保证。但是,是否始终存在这样的分配,或者是否可以有效地计算它们仍然是一个重要的开放问题。我们研究了EFX的存在和计算以及在词典偏好下的其他各种经济特性 - 人工智能和经济学中的良好偏好模型。与已知的增值估值结果形成鲜明对比,我们不仅证明了EFX和Pareto最佳分配的存在,而且实际上提供了这两种特性的算法表征。我们还表征了此外的机制,这些机制还具有策略性,非底膜和中性的机制。当效率概念被加强到级别 - 最大性时,我们会获得不存在和计算硬度结果,并表明当EFX放松到另一个被称为Maximin份额保证(MMS)的良好的公平概念时,可以恢复易干性。
Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods. But whether such allocations always exist or whether they can be efficiently computed remains an important open question. We study the existence and computation of EFX in conjunction with various other economic properties under lexicographic preferences--a well-studied preference model in artificial intelligence and economics. In sharp contrast to the known results for additive valuations, we not only prove the existence of EFX and Pareto optimal allocations, but in fact provide an algorithmic characterization of these two properties. We also characterize the mechanisms that are, in addition, strategyproof, non-bossy, and neutral. When the efficiency notion is strengthened to rank-maximality, we obtain non-existence and computational hardness results, and show that tractability can be restored when EFX is relaxed to another well-studied fairness notion called maximin share guarantee (MMS).