LLM-Agent-based Social Simulation for Attitude Diffusion

TL;DR

This paper presents discourse_simulator, an open-source framework combining LLMs and agent-based models to simulate attitude change and polarization in response to real-world events.

Contribution

It introduces a novel sociological simulation tool that integrates language models, real-time data, and belief structures for social science research.

Findings

Simulated attitude shifts in response to events like protests.

Demonstrated the framework with a Dublin anti-immigration march case study.

Provides an open-source package for social attitude dynamics research.

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 is fundamentally a different epistemological stance for studying social science problems. The paper further demonstrates the framework by modelling the Dublin anti-immigration march on April 26, 2025, with N=100 agents over a 15-day simulation. Package link: https://pypi.org/project/discourse-sim/