无嫉妒分配的最优补贴界限

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yasushi Kawase , Kazuhisa Makino , Hanna Sumita , Akihisa Tamura , Makoto Yokoo
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引用次数: 0

摘要

我们研究了n个主体对有补贴的不可分割物品的公平分配问题,其中每个物品的绝对边际价值不超过1。在单调非递减估值(其中每个项目都是好的)下,Brustle等人证明了最大补贴2(n−1)和总补贴2(n−1)2足以保证无嫉妒分配的存在。在本文中,我们改进了这些边界,甚至在更广泛的模型中。也就是说,我们证明,给定一个EF1分配,我们可以在多项式时间内计算出一个无嫉妒分配,每个代理的补贴最多为n−1,总补贴最多为n(n−1)/2。此外,当评估值为单调非递减时,我们提供了一个多项式时间算法,该算法计算每个代理的补贴最多为n−1.5,总补贴最多为(n2−n−1)/2的无嫉妒分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards optimal subsidy bounds for envy-freeable allocations
We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. [9] demonstrated that a maximum subsidy of 2(n1) and a total subsidy of 2(n1)2 are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most n1 per agent and a total subsidy of at most n(n1)/2. Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most n1.5 per agent and a total subsidy of at most (n2n1)/2.
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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