A fundamental challenge in opinion dynamics research is the scarcity of real-world longitudinal opinion data, which complicates the validation of theoretical models. To address this, we propose a novel simulation framework using large language model (LLM) agents in structured multi-round dialogs. Each agent's dialog history is iteratively updated with its own previously stated opinions and those of others analogous to the classical DeGroot model. Furthermore, by retaining each agent's initial opinion throughout the dialog, we simulate anchoring effects consistent with the Friedkin-Johnsen model of opinion dynamics. Our framework thus bridges classical opinion dynamics models and modern multi-agent LLM systems, providing a scalable tool for simulating and analyzing opinion formation when real-world data is limited or inaccessible.

翻译：观点动力学研究中的一个根本性挑战在于现实世界纵向观点数据的稀缺，这使得理论模型的验证变得复杂。为解决这一问题，我们提出了一种新颖的模拟框架，该框架利用大语言模型智能体进行结构化的多轮对话。每个智能体的对话历史会迭代更新，纳入其自身先前陈述的观点以及其他智能体的观点，这与经典的DeGroot模型类似。此外，通过在对话全程保留每个智能体的初始观点，我们模拟了与Friedkin-Johnsen观点动力学模型一致的锚定效应。因此，我们的框架连接了经典观点动力学模型与现代多智能体LLM系统，为在现实世界数据有限或难以获取时模拟和分析观点形成提供了一个可扩展的工具。