# From Text to Map: A System Dynamics Bot for Constructing Causal Loop Diagrams

arXiv: https://arxiv.org/abs/2402.11400 · HTML: https://mlsanity.com/paper/2402.11400/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities](https://mlsanity.com/paper/2409.15290/) (2024)
- [Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making](https://mlsanity.com/paper/2505.13761/) (2025)
- [Can Large Language Models Infer Causal Relationships from Real-World Text?](https://mlsanity.com/paper/2505.18931/) (2025)
- [Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques](https://mlsanity.com/paper/2503.21798/) (2025)
- [Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis](https://mlsanity.com/paper/2503.11046/) (2025)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [Traceable Multi-Agent System for Knowledge-Based Forecasting](https://mlsanity.com/paper/2608.03339/) (2026)

## Abstract (arXiv)

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.