[2604.03898] LLM-Agent-based Social Simulation for Attitude Diffusion

Computer Science > Artificial Intelligence arXiv:2604.03898 (cs) [Submitted on 4 Apr 2026] Title:LLM-Agent-based Social Simulation for Attitude Diffusion Authors:Deepak John Reji View a PDF of the paper titled LLM-Agent-based Social Simulation for Attitude Diffusion, by Deepak John Reji View PDF Abstract:This paper introduces discourse_simulator, an open-source framework that combines LLMs with agent-based modelling. It offers a new way to simulate how public attitudes toward immigration change over time in response to salient events like protests, controversies, or policy debates. Large language models (LLMs) are used to generate social media posts, interpret opinions, and model how ideas spread through social networks. Unlike traditional agent-based models that rely on fixed, rule-based opinion updates and cannot generate natural language or consider current events, this approach integrates multidimensional sociological belief structures and real-world event timelines. This framework is wrapped into an open-source Python package that integrates generative agents into a small-world network topology and a live news retrieval system. discourse_sim is purpose-built as a social science research instrument specifically for studying attitude dynamics, polarisation, and belief evolution following real-world critical events. Unlike other LLM Agent Swarm frameworks, which treat the simulations as a prediction black box, discourse_sim treats it as a theory-testing instrument, which...