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arXiv 2302.13342cs.GT
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混合可分与不可分物品的无嫉妒性与最大 Nash 福利

Envy-freeness and maximum Nash welfare for mixed divisible and indivisible goods

Koichi Nishimura, Hanna Sumita

AI总结:

本文研究混合可分与不可分物品的公平分配,证明二值线性等估值下 MNW 分配满足更强的 EFXM,并扩展刻画、证明一般加性情形满足弱于 EFM 的公平性。

摘要:

我们研究将同时包含可分物品和不可分物品的资源公平分配给具有加性估值的智能体。当只存在可分物品或不可分物品时,已知实现最大 Nash 福利(MNW)的分配满足基于嫉妒的经典公平性概念。此外,文献表明,当估值为二值且线性(即可分物品同质)时,MNW 分配具有相应结构与刻画。本文证明,当所有智能体的估值均为二值线性时,混合物品的 MNW 分配满足混合物品的任意物品无嫉妒(EFXM)。该概念强于已有概念混合物品无嫉妒(EFM),并且我们的结果推广了仅存在可分物品或不可分物品情形下的已有结果。当所有智能体对不可分物品具有二值估值、对可分物品具有相同估值(例如可分物品是货币)时,我们将不可分物品 MNW 分配的已知刻画扩展到混合物品,并证明 MNW 分配满足 EFXM。对于一般加性估值,我们还给出正式证明,表明 MNW 分配满足一个弱于 EFM 的概念。

英文摘要:

We study fair allocation of resources consisting of both divisible and indivisible goods to agents with additive valuations. When only divisible or indivisible goods exist, it is known that an allocation that achieves the maximum Nash welfare (MNW) satisfies the classic fairness notions based on envy. Moreover, the literature shows the structures and characterizations of MNW allocations when valuations are binary and linear (i.e., divisible goods are homogeneous). In this paper, we show that when all agents' valuations are binary linear, an MNW allocation for mixed goods satisfies the envy-freeness up to any good for mixed goods (EFXM). This notion is stronger than an existing one called envy-freeness for mixed goods (EFM), and our result generalizes the existing results for the case when only divisible or indivisible goods exist. When all agents' valuations are binary over indivisible goods and identical over divisible goods (e.g., the divisible good is money), we extend the known characterization of an MNW allocation for indivisible goods to mixed goods, and also show that an MNW allocation satisfies EFXM. For the general additive valuations, we also provide a formal proof that an MNW allocation satisfies a weaker notion than EFM.

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。

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