Abstract
In the rapidly evolving field of artificial intelligence, ensuring safe decision-making of Large Language Models (LLMs) is a significant challenge. This paper introduces Governance of the Commons Simulation (GovSim), a simulation platform designed to study strategic interactions and cooperative decision-making in LLMs. Through this simulation environment, we explore the dynamics of resource sharing among AI agents, highlighting the importance of ethical considerations, strategic planning, and negotiation skills. GovSim is versatile and supports any text-based agent, including LLMs agents. Using the Generative Agent framework, we create a standard agent that facilitates the integration of different LLMs. Our findings reveal that within GovSim, only two out of 15 tested LLMs managed to achieve a sustainable outcome, indicating a significant gap in the ability of models to manage shared resources. Furthermore, we find that by removing the ability of agents to communicate, they overuse the shared resource, highlighting the importance of communication for cooperation. Interestingly, most LLMs lack the ability to make universalized hypotheses, which highlights a significant weakness in their reasoning skills. We open source the full suite of our research results, including the simulation environment, agent prompts, and a comprehensive web interface.
Abstract (translated)
在人工智能领域，确保大型语言模型（LLMs）的安全决策是一个重要的挑战。本文介绍了治理共享资源模拟（GovSim）模拟平台，该平台旨在研究LLMs的战略互动和合作决策。通过这个仿真环境，我们探讨了AI代理之间资源共享的动态，强调了道德考虑、战略规划和谈判技能的重要性。GovSim具有灵活性，支持任何基于文本的代理，包括LLM代理。利用生成代理框架，我们创建了一个标准代理，促进不同LLM的集成。我们的研究结果表明，在GovSim中，只有两个 out of 15 经测试的LLM成功地实现了可持续的结果，表明模型在管理共享资源方面的能力存在显著的差距。此外，我们发现，通过移除代理与进行沟通的能力，它们超出了共享资源的使用，强调了沟通在合作中的重要性。有趣的是，大多数LLM都缺乏普遍化假设的能力，这表明它们在推理能力方面存在显著的弱点。我们开源了我们所有研究的完整套件，包括仿真环境、代理提示和综合网页界面。
URL
https://arxiv.org/abs/2404.16698
