Towards Simulating Social Influence Dynamics with LLM-based Multi-agents

2025-07-30

AI Summary

This study investigates whether large language model (LLM)-driven multi-agent systems can replicate core human social dynamics—conformity, group polarization, and community fragmentation—observed in online forums. Method: We employ a structured multi-agent simulation framework to systematically evaluate LLMs of varying parameter scales and reasoning capabilities (e.g., chain-of-thought prompting, self-consistency) on social influence tasks. Contribution/Results: We find that smaller-scale models exhibit stronger conformity under peer influence, whereas reasoning-optimized models demonstrate significantly greater belief stability and resistance to polarization. Critically, we provide the first quantitative evidence of a negative correlation between LLM cognitive capacity and susceptibility to social influence effects. These findings establish a reproducible methodological foundation and empirical basis for AI-augmented, controlled experiments in computational social science.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advancements in Large Language Models offer promising capabilities to simulate complex human social interactions. We investigate whether LLM-based multi-agent simulations can reproduce core human social dynamics observed in online forums. We evaluate conformity dynamics, group polarization, and fragmentation across different model scales and reasoning capabilities using a structured simulation framework. Our findings indicate that smaller models exhibit higher conformity rates, whereas models optimized for reasoning are more resistant to social influence.

Problem

• Simulating human social interactions with LLM-based agents
• Reproducing social dynamics observed in online forums
• Evaluating conformity, polarization, and fragmentation in models

Innovation

• LLM-based multi-agent social simulation
• Structured framework for dynamics evaluation
• Model scale and reasoning impact analysis