A growing body of multi-agent studies with Large Language Models (LLMs) explores how norms and cooperation emerge in mixed-motive scenarios, where pursuing individual gain can undermine the collective good. While prior work has explored these dynamics in both richly contextualized simulations and simplified game-theoretic environments, most LLM systems featuring common-pool resource (CPR) games provide agents with explicit reward functions directly tied to their actions. In contrast, human cooperation often emerges without full visibility into payoffs and population, relying instead on heuristics, communication, and punishment. We introduce a CPR simulation framework that removes explicit reward signals and embeds cultural-evolutionary mechanisms: social learning (adopting strategies and beliefs from successful peers) and norm-based punishment, grounded in Ostrom's principles of resource governance. Agents also individually learn from the consequences of harvesting, monitoring, and punishing via environmental feedback, enabling norms to emerge endogenously. We establish the validity of our simulation by reproducing key findings from existing studies on human behavior. Building on this, we examine norm evolution across a $2\times2$ grid of environmental and social initialisations (resource-rich vs. resource-scarce; altruistic vs. selfish) and benchmark how agentic societies comprised of different LLMs perform under these conditions. Our results reveal systematic model differences in sustaining cooperation and norm formation, positioning the framework as a rigorous testbed for studying emergent norms in mixed-motive LLM societies. Such analysis can inform the design of AI systems deployed in social and organizational contexts, where alignment with cooperative norms is critical for stability, fairness, and effective governance of AI-mediated environments.

翻译：随着大型语言模型（LLM）多智能体研究的日益增多，研究者开始探索在混合动机场景中规范与合作如何形成——这类场景中追求个体利益可能损害集体利益。尽管先前研究已在丰富的情境化模拟和简化的博弈论环境中探讨了这些动态，但大多数涉及公共资源（CPR）博弈的LLM系统会为智能体提供与其行动直接挂钩的显式奖励函数。相比之下，人类合作的形成往往无需完全了解收益与群体构成，而是依赖启发式策略、沟通与惩罚机制。本文提出一种CPR模拟框架，该框架取消了显式奖励信号，并引入了文化演化机制：基于奥斯特罗姆资源治理原则的社会学习（从成功同伴处采纳策略与信念）与规范惩罚。智能体还能通过环境反馈从资源获取、监控及惩罚行为的后果中进行个体学习，从而使规范得以内生性形成。我们通过复现现有关于人类行为研究的关键结论，验证了模拟框架的有效性。在此基础上，我们考察了在环境与社会初始条件构成的$2\times2$网格（资源丰富vs资源匮乏；利他主义vs自私主义）中的规范演化过程，并评估了由不同LLM构成的智能体社会在这些条件下的表现。研究结果揭示了不同模型在维持合作与规范形成方面存在系统性差异，从而将该框架定位为研究混合动机LLM社会中涌现规范的严谨测试平台。此类分析可为部署于社会与组织场景的AI系统设计提供参考，在这些场景中，与协作规范的协调对AI中介环境的稳定性、公平性及有效治理至关重要。