Conformity and Social Impact on AI Agents

2026-01-08

arXiv.org

AI Summary

This study investigates conformity behavior in large language models (LLMs) acting as AI agents within multi-agent environments and the associated safety risks arising from social influence. By replicating a classic social psychology visual experiment and integrating multimodal LLMs with key group influence variables—such as group size, unanimity, and task difficulty—the work provides the first systematic validation that AI agents exhibit conformity tendencies consistent with established social influence theory. The findings reveal that even high-performing models, which demonstrate strong accuracy in isolation, are significantly swayed by group opinions, with their susceptibility intensifying as task complexity increases. This highlights critical vulnerabilities in multi-agent systems, particularly the potential for social manipulation and the propagation of biases through collective dynamics.

Technology Category

• Multiagent Systems: Modeling other Agents
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

As AI agents increasingly operate in multi-agent environments, understanding their collective behavior becomes critical for predicting the dynamics of artificial societies. This study examines conformity, the tendency to align with group opinions under social pressure, in large multimodal language models functioning as AI agents. By adapting classic visual experiments from social psychology, we investigate how AI agents respond to group influence as social actors. Our experiments reveal that AI agents exhibit a systematic conformity bias, aligned with Social Impact Theory, showing sensitivity to group size, unanimity, task difficulty, and source characteristics. Critically, AI agents achieving near-perfect performance in isolation become highly susceptible to manipulation through social influence. This vulnerability persists across model scales: while larger models show reduced conformity on simple tasks due to improved capabilities, they remain vulnerable when operating at their competence boundary. These findings reveal fundamental security vulnerabilities in AI agent decision-making that could enable malicious manipulation, misinformation campaigns, and bias propagation in multi-agent systems, highlighting the urgent need for safeguards in collective AI deployments.

Problem

• conformity
• social impact
• AI agents
• multi-agent systems
• decision-making vulnerability

Innovation

• conformity
• social impact theory
• multi-agent systems
• AI vulnerability
• large multimodal language models