Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering cooperation between them is a fundamental challenge. In this paper, we develop the framework of a 'Normative Module': an architecture designed to enhance cooperation by enabling agents to recognize and adapt to the normative infrastructure of a given environment. We focus on the equilibrium selection aspect of the cooperation problem and inform our agent design based on the existence of classification institutions that implement correlated equilibrium to provide effective resolution of the equilibrium selection problem. Specifically, the normative module enables agents to learn through peer interactions which of multiple candidate institutions in the environment, does a group treat as authoritative. By enabling normative competence in this sense, agents gain ability to coordinate their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes primary behaviour within a social environment, leading to higher average welfare. We design a new environment that supports institutions and evaluate the proposed framework based on two key criteria derived from agent interactions with peers and institutions: (i) the agent's ability to disregard non-authoritative institutions and (ii) the agent's ability to identify authoritative institutions among several options. We show that these capabilities allow the agent to achieve more stable cooperative outcomes compared to baseline agents without the normative module, paving the way for research in a new avenue of designing environments and agents that account for normative infrastructure.

翻译：生成智能体通过大型语言模型（LLM）解释和评估环境以实施行为，已在众多社会与技术领域中展现出解决复杂任务的能力。然而，当这些智能体在存在既有规范等社会结构的环境中与其他智能体及人类交互时，促进彼此协作成为一个根本性挑战。本文提出“规范性模块”框架：该架构旨在通过使智能体能够识别并适应特定环境中的规范性基础设施来增强协作。我们聚焦于协作问题中的均衡选择层面，并基于分类制度的存在性来设计智能体——这类制度通过实现相关均衡来有效解决均衡选择问题。具体而言，规范性模块使智能体能够通过同伴交互学习环境中多个候选制度里，群体将哪一种视为权威制度。通过赋予智能体这种规范性能力，它们能够协调其制裁行为；协调的制裁行为进而塑造社会环境中的主体行为，从而提升平均福利水平。我们设计了一个支持制度的新环境，并基于智能体与同伴及制度交互的两项关键标准评估所提框架：（i）智能体忽略非权威制度的能力；（ii）智能体在多个选项中识别权威制度的能力。实验表明，相较于未配备规范性模块的基线智能体，这些能力使智能体能够实现更稳定的协作结果，为设计考虑规范性基础设施的环境与智能体的新研究方向开辟了道路。