Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM
  Agents
Zengqing Wu Shuyuan Zheng Qianying Liu Xu Han Brian Inhyuk Kwon Makoto Onizuka Shaojie Tang Run Peng Chuan Xiao
Recent advancements have shown that agents powered by large language models
(LLMs) possess capabilities to simulate human behaviors and societal dynamics.
However, the potential for LLM agents to spontaneously establish collaborative
relationships in the absence of explicit instructions has not been studied. To
address this gap, we conduct three case studies, revealing that LLM agents are
capable of spontaneously forming collaborations even within competitive
settings. This finding not only demonstrates the capacity of LLM agents to
mimic competition and cooperation in human societies but also validates a
promising vision of computational social science. Specifically, it suggests
that LLM agents could be utilized to model human social interactions, including
those with spontaneous collaborations, thus offering insights into social
phenomena. The source codes for this study are available at
https://github.com/wuzengqing001225/SABM_ShallWeTalk .