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arXiv 2602.06361cs.GTcs.ITcs.LGmath.ITstat.ML
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通过噪声查询实现不可分割物品的无嫉妒分配

Envy-Free Allocation of Indivisible Goods via Noisy Queries

Meta;National University of Singapore(国立新加坡大学);Nanyang Technological University(南洋理工大学)

Zihan Li, Yan Hao Ling, Jonathan Scarlett, Warut Suksompong

AI总结:

针对不可直接观测估值、仅能通过噪声查询获取信息的不可分割物品分配问题,在双智能体高斯噪声和有界估值设定下,推导了实现无嫉妒分配所需查询次数的上下界,并证明了当最优分配负嫉妒值Δ不太小时最优查询次数与m^{2.5}/Δ^2成比例。

摘要:

我们引入了一个公平分配不可分割物品(物品)的问题,其中智能体的估值无法直接观测,而只能通过噪声查询访问。在双智能体设定中,考虑高斯噪声和有界估值,我们推导了根据物品数量$m$和最优分配的负嫉妒值$Δ$,找到无嫉妒分配所需查询次数的上下界。特别地,当$Δ$不太小(即$Δ\gg m^{1/4}$)时,我们证明最优查询次数在忽略对数因子下为$ rac{\sqrt m }{(Δ/ m)^2} = rac{m^{2.5}}{Δ^2}$。我们的上界基于非自适应查询和一个简单的基于阈值的分配算法,该算法在多项式时间内运行,而下界即使在自适应查询和任意计算时间下也成立。

英文摘要:

We introduce a problem of fairly allocating indivisible goods (items) in which the agents' valuations cannot be observed directly, but instead can only be accessed via noisy queries. In the two-agent setting with Gaussian noise and bounded valuations, we derive upper and lower bounds on the required number of queries for finding an envy-free allocation in terms of the number of items, $m$, and the negative-envy of the optimal allocation, $Δ$. In particular, when $Δ$ is not too small (namely, $Δ\gg m^{1/4}$), we establish that the optimal number of queries scales as $\frac{\sqrt m }{(Δ/ m)^2} = \frac{m^{2.5}}{Δ^2}$ up to logarithmic factors. Our upper bound is based on non-adaptive queries and a simple thresholding-based allocation algorithm that runs in polynomial time, while our lower bound holds even under adaptive queries and arbitrary computation time.

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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