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Feedback density and causal complexity of simulation model structure

Author

Listed:
  • Asmeret Naugle
  • Stephen Verzi
  • Kiran Lakkaraju
  • Laura Swiler
  • Christina Warrender
  • Michael Bernard
  • Vicente Romero
Abstract
Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the causal interactions present in the code. Causal structure diagrams facilitate comparison of simulation models, including those from different paradigms. Next, we develop metrics for evaluating a model’s causal complexity using its causal structure diagram. We discuss cyclomatic complexity as a measure of the intricacy of causal structure and introduce two new metrics that incorporate the concept of feedback, a fundamental component of causal structure. The first new metric introduced here is feedback density, a measure of the cycle-based interconnectedness of causal structure. The second metric combines cyclomatic complexity and feedback density into a comprehensive causal complexity measure. Finally, we demonstrate these complexity metrics on simulation models from multiple paradigms and discuss potential uses and interpretations. These tools enable direct comparison of models across paradigms and provide a mechanism for measuring and discussing complexity based on a model’s fundamental assumptions and design.

Suggested Citation

  • Asmeret Naugle & Stephen Verzi & Kiran Lakkaraju & Laura Swiler & Christina Warrender & Michael Bernard & Vicente Romero, 2023. "Feedback density and causal complexity of simulation model structure," Journal of Simulation, Taylor & Francis Journals, vol. 17(3), pages 229-239, May.
    • Handle: RePEc:taf:tjsmxx:v:17:y:2023:i:3:p:229-239
    DOI: 10.1080/17477778.2021.1982653
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