Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions

• URL: http://arxiv.org/abs/2411.04578v1
• Date: Thu, 07 Nov 2024 10:00:46 GMT
• Title: Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions
• Authors: Tianqi Song, Yugin Tan, Zicheng Zhu, Yibin Feng, Yi-Chieh Lee,
• Abstract summary: We investigate whether a group of AI agents can create social pressure on users to agree with them. We found that conversing with multiple agents increased the social pressure felt by participants. Our study shows the potential advantages of multi-agent systems over single-agent platforms in causing opinion change.
• Score: 7.421573539569854
• License:
• Abstract: Multi-agent systems - systems with multiple independent AI agents working together to achieve a common goal - are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate whether a group of AI agents can create social pressure on users to agree with them, potentially changing their stance on a topic. We conducted a study in which participants discussed social issues with either a single or multiple AI agents, and where the agents either agreed or disagreed with the user's stance on the topic. We found that conversing with multiple agents (holding conversation content constant) increased the social pressure felt by participants, and caused a greater shift in opinion towards the agents' stances on each topic. Our study shows the potential advantages of multi-agent systems over single-agent platforms in causing opinion change. We discuss design implications for possible multi-agent systems that promote social good, as well as the potential for malicious actors to use these systems to manipulate public opinion.

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