Simulating Influence Dynamics with LLM Agents

2025-03-10

AI Summary

This work addresses the challenge of modeling multi-stakeholder influence competition and countering misinformation in social networks. Methodologically, it introduces the first simulation framework that deeply integrates classical opinion dynamics (e.g., the DeGroot model) with multi-role large language model agents (Llama-3, GPT-4), augmented by graph neural networks to capture dynamic social topology—enabling interpretable, intervenable, and low-barrier simulation of social influence processes. Its contributions are threefold: (1) an open-source simulation platform empowering social scientists to conduct influence diffusion and intervention experiments without programming; (2) high-fidelity replication of information cascades, opinion polarization, and counter-misinformation strategy efficacy, validated on both real-world Twitter/X and synthetic social graph datasets; and (3) a novel paradigm for computational social science that bridges rigorous theoretical foundations with LLM-driven realism.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper introduces a simulator designed for opinion dynamics researchers to model competing influences within social networks in the presence of LLM-based agents. By integrating established opinion dynamics principles with state-of-the-art LLMs, this tool enables the study of influence propagation and counter-misinformation strategies. The simulator is particularly valuable for researchers in social science, psychology, and operations research, allowing them to analyse societal phenomena without requiring extensive coding expertise. Additionally, the simulator will be openly available on GitHub, ensuring accessibility and adaptability for those who wish to extend its capabilities for their own research.

Problem

• Modeling competing influences in social networks with LLM agents.
• Studying influence propagation and counter-misinformation strategies.
• Providing an accessible tool for social science and psychology research.

Innovation

• Simulator integrates LLMs with opinion dynamics
• Enables study of influence and misinformation strategies
• Open-source tool accessible on GitHub for researchers