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From text to model: Leveraging natural language processing for system dynamics model development

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Listed:
  • Guido A. Veldhuis
  • Dominique Blok
  • Maaike H.T. de Boer
  • Gino J. Kalkman
  • Roos M. Bakker
  • Rob P.M. van Waas

Abstract

Textual data is abundantly available, and natural language processing (NLP) facilitates its analysis. However, system dynamics (SD) modelling relies on the modeller to identify relevant information. We explore the ability of NLP models to support SD modelling by identifying causal sentences in texts. We provide a primer on the notion of causality in SD and on the linguistic properties of causality, followed by an introduction of NLP models suitable for this task. Using three test cases, we evaluate the performance of the NLP models using common evaluation metrics and an SD model completeness metric. We conclude that NLP models can add considerable value to SD modelling, provided that remaining challenges are addressed. One such caveat is the difference we observe between information regarded as causal and information relevant for describing system structure. We discuss how these challenges can be addressed through collaboration between the NLP and SD fields. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Suggested Citation

  • Guido A. Veldhuis & Dominique Blok & Maaike H.T. de Boer & Gino J. Kalkman & Roos M. Bakker & Rob P.M. van Waas, 2024. "From text to model: Leveraging natural language processing for system dynamics model development," System Dynamics Review, System Dynamics Society, vol. 40(3), July.
    • Handle: RePEc:bla:sysdyn:v:40:y:2024:i:3:n:e1780
    DOI: 10.1002/sdr.1780
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    References listed on IDEAS

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