用大模型模拟移民议题态度演变，可追踪真实事件影响。
LLM-Agent-based Social Simulation for Attitude Diffusion
- 结合大模型与社会代理，动态生成观点并传播
- 100个代理在15天内模拟都柏林反移民游行影响
- 专为社科研究设计，支持理论验证而非黑箱预测
本文提出discourse_simulator，一个开源框架，将大语言模型（LLMs）与基于代理的建模结合，用于模拟公众对移民态度随时间变化的过程。该框架通过LLMs生成社交媒体内容、解读观点，并建模思想在社交网络中的传播机制。相比传统依赖固定规则的代理模型，本方法整合多维度社会信念结构和真实事件时间线，支持自然语言生成与实时新闻数据接入。框架封装为开源Python包，集成生成式代理、小世界网络拓扑与实时新闻检索系统。其定位为社会科学研究工具，专门用于分析重大事件后态度演化、极化现象与信念变迁。不同于其他将仿真视为预测黑箱的LLM代理集群，discourse_sim将其作为理论检验工具，具有根本不同的认识论立场。论文以2025年4月26日都柏林反移民游行为例，用N=100个代理进行15天模拟，验证框架有效性。
原文摘要 · Abstract (English)
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/
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