Evolutionary Dynamics of Preguidance Strategies in Population Games

IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS IEEE Transactions on Computational Social Systems Pub Date : 2024-04-30 DOI:10.1109/TCSS.2024.3386501
Linjie Liu;Xiaojie Chen
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Abstract

Promoting cooperation among conflicting entities in human society and intelligent systems is a formidable task. One potential solution could involve the formulation of incentives designed to decrease the benefits of noncooperators and/or increase the rewards for cooperators. We put forth a novel incentive approach, specifically, a guidance strategy where certain cooperators willingly bear a cost to alter the actions of agents who intend to defect prior to the actual commencement of a game. We introduce an innovative game-theoretical framework that sheds light on the dynamics of guidance strategies, encompassing both peer guidance and pool guidance. Under the peer guidance scheme, each guider independently incurs the cost to influence agents intending to defect, whereas in the pool guidance scheme, guiders organically establish an institution to influence agents prone to free riding. Regardless of whether a peer or pool guidance scheme is utilized, the implementation of a guidance strategy has proven to be remarkably effective in reducing the instances of pure cooperation, also known as second-order free riding. Intriguingly, our result suggests that the pool guidance strategy demonstrates a more potent deterrent effect on second-order free-riding behavior than the peer guidance strategy, particularly when the cost of guidance is exceptionally high. These findings underscore the significance of preguidance in fostering cooperation in human and multiagent AI systems and could offer valuable insights for the development of a regulatory mechanism for preemptive guidance and subsequent punishment.
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群体博弈中预引导策略的进化动力学
促进人类社会和智能系统中相互冲突的实体之间的合作是一项艰巨的任务。一种可能的解决方案是制定激励措施,以减少非合作者的利益和/或增加合作者的回报。我们提出了一种新颖的激励方法,特别是一种引导策略,即在博弈实际开始之前,某些合作者自愿承担一定的成本,以改变打算叛变的代理的行动。我们引入了一个创新的博弈论框架,它揭示了指导策略的动态变化,包括同伴指导和集合指导。在同伴指导方案中,每个指导者都要独立承担影响有意叛逃的代理人的成本,而在集合指导方案中,指导者会有机地建立一个机构来影响容易搭便车的代理人。事实证明,无论采用同伴指导方案还是集合指导方案,实施指导策略都能显著减少纯合作(也称二阶搭便车)的情况。耐人寻味的是,我们的结果表明,与同伴指导策略相比,集合指导策略对二阶搭便车行为的威慑力更大,尤其是在指导成本特别高的情况下。这些发现强调了预先指导在促进人类和多机器人人工智能系统合作中的重要作用,并为开发预先指导和后续惩罚的监管机制提供了宝贵的见解。
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来源期刊
IEEE Transactions on Computational Social Systems
IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)
CiteScore
10.00
自引率
20.00%
发文量
316
期刊介绍: IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.
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