让AI模仿人类行为，能有效提升集体合作水平。
Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
- AI通过模仿玩家行为促进合作
- 仅当AI模仿人类时，合作阈值显著降低
- 适合研究社会协作与AI治理的学者
公地悲剧揭示了个体理性行为导致集体非最优结果的根本性社会困境，威胁共享资源的可持续性。现有缓解策略有限。本研究探讨如何利用人工智能（AI）代理增强公共品博弈中的合作，突破传统监管手段，转而将AI作为合作促成者。我们考察三种情景：(1) 强制合作政策，即机构强制要求AI代理始终合作；(2) 玩家控制代理合作政策，即玩家可演化控制AI合作概率；(3) 代理模仿玩家行为，即AI代理复制玩家行为。基于群体代理进行公共品博弈的计算进化模型，发现只有当AI代理模仿玩家行为时，合作的临界协同阈值才会下降，有效解决该困境。这表明，通过设计模仿人类玩家的AI代理，可助力社会困境中的集体福祉提升。
原文摘要 · Abstract (English)
The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regulatory approaches to using AI as facilitators of cooperation. We investigate three scenarios: (1) Mandatory Cooperation Policy for AI Agents, where AI agents are institutionally mandated always to cooperate; (2) Player-Controlled Agent Cooperation Policy, where players evolve control over AI agents' likelihood to cooperate; and (3) Agents Mimic Players, where AI agents copy the behavior of players. Using a computational evolutionary model with a population of agents playing public goods games, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma. This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。