Almost proportional allocations of indivisible chores: Computation, approximation and efficiency

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haris Aziz , Bo Li , Hervé Moulin , Xiaowei Wu , Xinran Zhu
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引用次数: 0

Abstract

Proportionality (PROP) is one of the simplest and most intuitive fairness criteria used for allocating items among agents with additive utilities. However, when the items are indivisible, ensuring PROP becomes unattainable, leading to increased focus on its relaxations. In this paper, we focus on the relaxation of proportionality up to any item (PROPX), where proportionality is satisfied if an arbitrary item is removed from every agent's allocation. We show that PROPX is an appealing fairness notion for the allocation of indivisible chores, which approximately implies some share-based notions, such as maximin share (MMS) and AnyPrice share (APS). We further provide a comprehensive understanding of PROPX allocations, regarding the computation, approximation, and compatibility with efficiency. On top of these, we extend the study to scenarios where agents do not share equal liability towards the chores, and approximate PROPX allocations using partial information about agents' utilities.

几乎按比例分配不可分割的家务:计算、近似和效率
比例性(PROP)是最简单、最直观的公平标准之一,用于在具有加法效用的代理人之间分配物品。然而,当物品不可分割时,确保 PROP 就变得难以实现,这导致人们越来越关注其松弛问题。在本文中,我们将重点放在任意项目的比例性松弛(PROPX)上,在这种情况下,如果从每个代理的分配中移除一个任意项目,比例性就会得到满足。我们证明,PROPX 是不可分割家务分配的一个有吸引力的公平概念,它近似地暗示了一些基于份额的概念,如最大化份额(MMS)和任意价格份额(APS)。我们还进一步全面地了解了 PROPX 分配的计算、近似性和与效率的兼容性。在此基础上,我们将研究扩展到代理人对家务不承担同等责任的情况,并利用代理人效用的部分信息对 PROPX 分配进行近似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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