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From text to map: a system dynamics bot for constructing causal loop diagrams

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Listed:
  • Niyousha Hosseinichimeh
  • Aritra Majumdar
  • Ross Williams
  • Navid Ghaffarzadegan

Abstract

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 60% of the links between variables and feedback loops in both datasets. This article 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 test‐bed for similar programs. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Suggested Citation

  • Niyousha Hosseinichimeh & Aritra Majumdar & Ross Williams & Navid Ghaffarzadegan, 2024. "From text to map: a system dynamics bot for constructing causal loop diagrams," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    • Handle: RePEc:bla:sysdyn:v:40:y:2024:i:3:n:e1782
    DOI: 10.1002/sdr.1782
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    References listed on IDEAS

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    1. Ross Williams & Niyousha Hosseinichimeh & Aritra Majumdar & Navid Ghaffarzadegan, 2023. "Epidemic Modeling with Generative Agents," Papers 2307.04986, arXiv.org.
    2. Argyle, Lisa P. & Busby, Ethan C. & Fulda, Nancy & Gubler, Joshua R. & Rytting, Christopher & Wingate, David, 2023. "Out of One, Many: Using Language Models to Simulate Human Samples," Political Analysis, Cambridge University Press, vol. 31(3), pages 337-351, July.
    3. Sumaiya Haque & Hesam Mahmoudi & Navid Ghaffarzadegan & Konstantinos Triantis, 2023. "Mental models, cognitive maps, and the challenge of quantitative analysis of their network representations," System Dynamics Review, System Dynamics Society, vol. 39(2), pages 152-170, April.
    4. Ali Akhavan & Mohammad S. Jalali, 2024. "Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    5. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    6. Yearworth, Mike & White, Leroy, 2013. "The uses of qualitative data in multimethodology: Developing causal loop diagrams during the coding process," European Journal of Operational Research, Elsevier, vol. 231(1), pages 151-161.
    7. Pablo Newberry & Neil Carhart, 2024. "Constructing causal loop diagrams from large interview data sets," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    8. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
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    Cited by:

    1. Tyler J. Gandee & Sean C. Glaze & Philippe J. Giabbanelli, 2024. "A Visual Analytics Environment for Navigating Large Conceptual Models by Leveraging Generative Artificial Intelligence," Mathematics, MDPI, vol. 12(13), pages 1-23, June.
    2. Fu, Yuan & Barbrook-Johnson, Peter, 2025. "Using NLP to create preliminary causal system maps for use in policy analysis," INET Oxford Working Papers 2025-09, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.

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