Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents

Author:

Zengqing Wu, Shuyuan Zheng, Qianying Liu, Xu Han, Brian Inhyuk Kwon, Makoto Onizuka, Shaojie Tang, Run Peng, Chuan Xiao

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computation and Language (cs.CL), Computers and Society (cs.CY), Multiagent Systems (cs.MA), General Economics (econ.GN)

journal:

date:

2024-02-19 00:00:00

Abstract

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 .

PDF: Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents.pdf