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arXiv 2502.13541cs.GT
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次可加估值的集中性与最大最小公平分配

Concentration and maximin fair allocations for subadditive valuations

Uriel Feige, Shengyu Huang

AI总结:

本文针对次模估值下的不可分割物品公平分配,改进已有算法与集中性分析,将 MMS 保证从含大常数的 $1/(c\log n\log\log n)$ 提升至 $1/(14\log n)$。

摘要:

我们研究在等权利条件下,将 $m$ 个不可分割物品公平分配给 $n$ 个智能体的问题,其中估值函数为次模函数。此前,Seddighin 和 Seddighin(《Artificial Intelligence》,2024)证明了存在一类分配,可为每个智能体提供至少其最大最小份额(MMS)的 $\frac{1}{c \log n \log\log n}$,其中 $c$ 是某个较大的常数(在他们的工作中超过 1000)。我们修改了他们的算法并改进了其分析,将该比例提升至 $\frac{1}{14 \log n}$。我们的部分改进源于对任意次可加估值函数 $v$ 的价值集中性质进行更紧的分析:考虑物品集合 $S' \subseteq S$,其中 $S$ 的每件物品都以独立随机方式被纳入 $S'$(概率可能不同)。特别地,我们证明,在至多相差不到一件物品价值的意义下,$v(S')$ 的中位数价值(记为 $M$)至少为期望价值的三分之二,即 $M \geq \frac{2}{3}\E[v(S')] - \frac{11}{12}\max_{e \in S} v(e)$。

英文摘要:

We consider fair allocation of $m$ indivisible items to $n$ agents of equal entitlements, with submodular valuation functions. Previously, Seddighin and Seddighin [{\em Artificial Intelligence} 2024] proved the existence of allocations that offer each agent at least a $\frac{1}{c \log n \log\log n}$ fraction of her maximin share (MMS), where $c$ is some large constant (over 1000, in their work). We modify their algorithm and improve its analysis, improving the ratio to $\frac{1}{14 \log n}$. Some of our improvement stems from tighter analysis of concentration properties for the value of any subadditive valuation function $v$, when considering a set $S' \subseteq S$ of items, where each item of $S$ is included in $S'$ independently at random (with possibly different probabilities). In particular, we prove that up to less than the value of one item, the median value of $v(S')$, denoted by $M$, is at least two-thirds of the expected value, $M \geq \frac{2}{3}\E[v(S')] - \frac{11}{12}\max_{e \in S} v(e)$.

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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