Understanding Compliance and Conversion Dynamics in Multi-Agent Collectives

Multi-agent AI systems are increasingly prevalent across digital environments, yet their social influence dynamics remain underexplored beyond basic compliance. This study investigates how different multi-agent configurations affect human decision-making through compliance and conversion mechanisms. We conducted a controlled experiment with 127 participants interacting with three LLM-powered agents across three conditions: Majority (all agents opposing participant), Minority (one dissenting agent), and Diffusion (gradual spread of minority position). Participants completed normative and informative tasks while reporting stance and confidence at five time points. Results demonstrate distinct influence patterns by condition and task type. In informative tasks, majority consensus drove largest immediate opinion changes, while minority dissent showed potential for delayed but deeper attitude shifts consistent with conversion-like processes. The diffusion condition revealed how temporal dynamics serve as persuasive signals. These findings extend social psychology theories to human-AI interaction, highlighting risks of synthetic consensus manipulation and opportunities for structured dissent to promote critical thinking.