To investigate the role of language in human collective behaviors, we developed the Agent Group Chat simulation to simulate linguistic interactions among multi-agent in different settings. Agents are asked to free chat in this simulation for their own purposes based on their character setting, aiming to see agents exhibit emergent behaviours that are both unforeseen and significant. Four narrative scenarios, Inheritance Disputes, Law Court Debates, Philosophical Discourses, Movie Casting Contention, are integrated into Agent Group Chat to evaluate its support for diverse storylines. By configuring specific environmental settings within Agent Group Chat, we are able to assess whether agents exhibit behaviors that align with human expectations. We evaluate the disorder within the environment by computing the n-gram Shannon entropy of all the content speak by characters. Our findings reveal that under the premise of agents possessing substantial alignment with human expectations, facilitating more extensive information exchange within the simulation ensures greater orderliness amidst diversity, which leads to the emergence of more unexpected and meaningful emergent behaviors. The code is open source in https://github.com/MikeGu721/AgentGroup, and online platform will be open soon.

翻译：为探究语言在人类集体行为中的作用，我们开发了Agent Group Chat模拟系统，用于模拟多智能体在不同场景下的语言交互。在该模拟中，智能体基于其角色设定自由聊天以实现自身目标，旨在观察智能体展现出的既不可预见又具重要意义的涌现行为。该系统整合了遗产纠纷、法庭辩论、哲学思辨、选角争议四种叙事场景，以评估其对多样化故事线的支持能力。通过配置Agent Group Chat中的特定环境设置，我们能够评估智能体展现的行为是否符合人类预期。通过计算所有角色发言内容的n-gram香农熵，我们对环境中的无序程度进行评估。研究结果表明：在智能体与人类预期高度一致的前提下，促进模拟中更广泛的信息交换，可在多样性中维持更高的有序性，进而催生更多意外且富有意义的涌现行为。代码已开源发布于https://github.com/MikeGu721/AgentGroup，在线平台即将开放。