Socially conscious stability for tiered coalition formation games

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2023-11-15 DOI:10.1007/s10472-023-09897-4
Nathan Arnold, Sarah Snider, Judy Goldsmith
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Abstract

We investigate Tiered Coalition Formation Games (TCFGs), a cooperative game inspired by the stratification of Pokémon on the fan website, Smogon. It is known that, under match-up oriented preferences, Nash and core stability are equivalent. We previously introduced a notion of socially conscious stability for TCFGs, and introduced a game variant with fixed k-length tier lists. In this work we show that in tier lists under match-up oriented preferences, socially conscious stability is equivalent to Nash stability and to core stability, but in k-tier lists, the three stability notions are distinct. We also give a necessary condition for tier list stability in terms of robustness (the minimum in-tier utility of an agent). We introduce a notion of approximate Nash stability and approximately socially conscious stability, and provide experiments on the empirical run time of our k-tier local search algorithm, and the performance of our algorithms for generating approximately socially consciously stable tier lists.

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分层联盟形成博弈的社会意识稳定性
我们研究了分层联盟形成游戏(TCFGs),这是一种受粉丝网站Smogon上的poksammon分层启发的合作游戏。我们知道,在匹配导向偏好下,纳什与核心稳定性是等价的。我们之前在tcfg中引入了社会意识稳定性的概念,并引入了具有固定k长度层列表的游戏变体。在本研究中,我们证明了在配对导向偏好下的层表中,社会意识稳定等同于纳什稳定和核心稳定,但在k层表中,这三个稳定概念是不同的。我们还根据鲁棒性(代理的最小层内效用)给出了层列表稳定性的必要条件。我们引入了近似纳什稳定性和近似社会意识稳定性的概念,并提供了我们的k层局部搜索算法的经验运行时间实验,以及我们的算法在生成近似社会意识稳定的层列表方面的性能。
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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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