Go to ACL ARR 2025 February homepageI Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy
Abstract: As LLM-based agents become increasingly autonomous and will more freely interact with each other, studying the interplay among them becomes crucial to anticipate emergent phenomena and potential risks. In this work, we provide an in-depth analysis of the interactions among agents within a simulated hierarchical social environment, drawing inspiration from the Stanford Prison Experiment. Utilizing 2,000 conversations across five LLMs and 200 scenarios, we analyze persuasion and anti-social behavior between a guard and a prisoner agent with differing objectives.
Among models demonstrating successful interaction, we find that goal setting significantly influences persuasiveness but not anti-social behavior. What's more, agent personas, especially the guard's, substantially impact both successful persuasion by the prisoner and the manifestation of anti-social actions. Notably, we observe the emergence of anti-social conduct even in absence of explicit negative personality prompts. These results have important implications for the development of interactive LLM agents and the ongoing discussion of their societal impact.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: LLMs, computational social science, sociology of machines, NLP tools for social analysis
Contribution Types: Model analysis & interpretability
Languages Studied: English
Submission Number: 1833
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