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arXiv 2407.07641cs.GTcs.CC
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不可分割物品公平分配的低通信协议

Low communication protocols for fair allocation of indivisible goods

Uriel Feige

AI总结:

本文研究不可分割物品公平分配的多方随机通信复杂度,针对不同估值函数给出MMS分配的通信量上下界,提出Aprop分配概念并设计低通信随机协议,同时给出MXS的通信下界。

摘要:

我们研究将$m$件不可分割物品分配给$n<m$个同等权利智能体的公平分配计算的多方随机通信复杂度。首先我们研究MMS(最大最小份额)分配,即保证每个智能体至少获得其最大最小份额的分配,这类分配在简单的估值函数类别中被证明必然存在。我们考察所有智能体平均需要传输的期望比特数:对于单位需求估值,该数值仅为$O(1)$(但若追求EF1(无嫉妒至多一件物品)分配而非MMS分配,则为$Θ(\log n)$);对于二元加性估值,该数值为$Θ(\log \frac{m}{n})$;对于二值加性估值,我们给出了$Ω(\frac{m}{n})$的下界。\n对于一般加性估值,MMS分配不一定存在。我们提出“近似比例”(Aprop)分配的概念,它从两个维度近似比例分配:既是Prop1(至多差一件物品满足比例性),也是$\frac{n}{2n-1}$-TPS(至少获得截断比例份额的$\frac{n}{2n-1}$,因此也至少获得MMS的$\frac{n}{2n-1}$)。我们设计了输出Aprop分配的随机协议,其中每个智能体传输的平均期望比特数为$O(\log m)$。对于更强的MXS(最小EFX份额)概念,我们给出了$Ω(\frac{m}{n})$的下界。

英文摘要:

We study the multi-party randomized communication complexity of computing a fair allocation of $m$ indivisible goods to $n < m$ equally entitled agents. We first consider MMS allocations, allocations that give every agent at least her maximin share. Such allocations are guaranteed to exist for simple classes of valuation functions. We consider the expected number of bits that each agent needs to transmit, on average over all agents. For unit demand valuations, we show that this number is only $O(1)$ (but $Θ(\log n)$, if one seeks EF1 allocations instead of MMS allocations), for binary additive valuations we show that it is $Θ(\log \frac{m}{n})$, and for 2-valued additive valuations we show a lower bound of $Ω(\frac{m}{n})$. For general additive valuations, MMS allocations need not exist. We consider a notion of {\em approximately proportional} (Aprop) allocations, that approximates proportional allocations in two different senses, being both Prop1 (proportional up to one item), and $\frac{n}{2n-1}$-TPS (getting at least a $\frac{n}{2n-1}$ fraction of the {\em truncated proportional share}, and hence also at least a $\frac{n}{2n-1}$ fraction of the MMS). We design randomized protocols that output Aprop allocations, in which the expected average number of bits transmitted per agent is $O(\log m)$. For the stronger notion of MXS ({\em minimum EFX share}) we show a lower bound of $Ω(\frac{m}{n})$.

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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