论具有共同等级属性的享乐博弈

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2023-08-23 DOI:10.1007/s10472-023-09892-9
Bugra Caskurlu, Fatih Erdem Kizilkaya
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引用次数: 0

摘要

对冲博弈是联盟形成的一个重要模型,其中每个代理的效用只取决于她所在的联盟。所有联盟成员都能获得相同的效用,这种以一般伙伴关系的形成为模型的享乐博弈子类(Larson 2018)被称为具有共同排名属性的享乐博弈(HGCRP)。除了其经济动机外,HGCRP 还因为能保证高效找到核心稳定解而备受瞩目(Farrell 和 Scotchmer Q. J. Econ.103(2), 279-297 1988).我们对现有结果进行了改进,证明 HGCRP 的每个实例都有一个帕累托最优解、核心稳定解和个体稳定解。这一结果的经济意义在于,在 HGCRP 中,不能为了稳定而完全牺牲效率。我们证明,即使联盟规模的上界为 3,找到这样一个解也是 NP 难的;但是,如果联盟规模的上界为 2,找到这样一个解则是多项式时间可解的。我们证明,核心稳定解的总效用与社会最优解(OPT)的总效用之间的差距的上界为 n,其中 n 是代理人的数量,并且这个上界是紧密的。我们的研究发现,对于任何固定的 \(\epsilon > 0\) 来说,计算 OPT 都无法在优于 \(O(n^{1-\epsilon })\) 的范围内被逼近,而且这个不可逼近性下限是多项式紧密的。然而,如果联盟的大小以 2 为界,则 OPT 可以在多项式时间内计算。
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On hedonic games with common ranking property

Hedonic games are a prominent model of coalition formation, in which each agent’s utility only depends on the coalition she resides. The subclass of hedonic games that models the formation of general partnerships (Larson 2018), where all affiliates receive the same utility, is referred to as hedonic games with common ranking property (HGCRP). Aside from their economic motivation, HGCRP came into prominence since they are guaranteed to have core stable solutions that can be found efficiently (Farrell and Scotchmer Q. J. Econ. 103(2), 279–297 1988). We improve upon existing results by proving that every instance of HGCRP has a solution that is Pareto optimal, core stable, and individually stable. The economic significance of this result is that efficiency is not to be totally sacrificed for the sake of stability in HGCRP. We establish that finding such a solution is NP-hard even if the sizes of the coalitions are bounded above by 3; however, it is polynomial time solvable if the sizes of the coalitions are bounded above by 2. We show that the gap between the total utility of a core stable solution and that of the socially-optimal solution (OPT) is bounded above by n, where n is the number of agents, and that this bound is tight. Our investigations reveal that computing OPT is inapproximable within better than \(O(n^{1-\epsilon })\) for any fixed \(\epsilon > 0\), and that this inapproximability lower bound is polynomially tight. However, OPT can be computed in polynomial time if the sizes of the coalitions are bounded above by 2.

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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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