We introduce and test the System Dynamics Bot, a computer program leveraging a large language model to automate the creation of causal loop diagrams from textual data. To evaluate its performance, we ensembled two distinct databases. The first dataset includes 20 causal loop diagrams and associated texts sourced from the system dynamics literature. The second dataset comprises responses from 30 participants to a vignette, along with causal loop diagrams coded by three system dynamics modelers. The bot uses textual data and successfully identifies approximately sixty percent of the links between variables and feedback loops in both datasets. This paper outlines our approach, provides examples, and presents evaluation results. We discuss encountered challenges and implemented solutions in developing the System Dynamics Bot. The bot can facilitate extracting mental models from textual data and improve model building processes. Moreover, the two datasets can serve as a testbed for similar programs.

翻译：我们介绍并测试了系统动力学机器人，这是一个利用大语言模型从文本数据自动创建因果回路图的计算机程序。为评估其性能，我们整合了两个不同的数据库。第一个数据集包含来自系统动力学文献的20个因果回路图及相关文本。第二个数据集包括30名参与者对情景问题的回答，以及由三位系统动力学建模者编码的因果回路图。该机器人利用文本数据，在两组数据集中成功识别了约百分之六十的变量间连接和反馈回路。本文概述了我们的方法，提供了示例，并展示了评估结果。我们讨论了在开发系统动力学机器人过程中遇到的挑战及实施的解决方案。该机器人有助于从文本数据中提取心智模型，并改进建模过程。此外，这两个数据集可作为类似程序的测试平台。