Can LLMs improve social network simulations?
Agent-Based Modelling Meets Generative AI in Social Network Simulations
This research explores using Large Language Models (LLMs) to create more realistic agents in social network simulations. Traditional agent-based models (ABMs) often rely on simplified rules, but LLMs enable agents to make decisions based on "personality," learned interests, and interactions, mimicking human behavior more closely. The framework includes modules for characterizing agents based on real user data, simulating agent reasoning and actions (posting, resharing, etc.), and managing interactions via a Retrieval-Augmented Generation (RAG) approach. Experiments using Twitter data from the 2020 US election showed that LLM-agents accurately reflect users' political leanings, form ideologically similar clusters, and are influenced by recommendation strategies (preference-based leading to echo chambers). This approach offers a more nuanced and robust method for studying online social dynamics.