Diagrams-to-Dynamics (D2D): Exploring causal loop diagram leverage points under uncertainty.

Area of Science:

• Systems Science
• Computational Modeling

Background:

• Causal loop diagrams (CLDs) are prevalent in health and environmental research for depicting complex causal relationships.
• However, their static and qualitative nature limits dynamic analysis and intervention strategy development.

Purpose of the Study:

• To introduce Diagrams-to-Dynamics (D2D), a novel method for transforming CLDs into exploratory system dynamics models.
• To enable dynamic simulation and identification of leverage points from CLDs, even without empirical data.

Main Methods:

• D2D converts CLDs into system dynamics models with minimal user input, labeling variables as stocks, flows, auxiliaries, or constants.
• The method leverages existing CLD structure (connections and polarity) for simulation and intervention analysis.

Main Results:

• D2D effectively distinguishes between high- and low-ranked leverage points.
• The D2D method demonstrated higher consistency with a calibrated system dynamics model compared to static network centrality analysis.
• D2D provides uncertainty estimates and guidance for future data collection.

Conclusions:

• The D2D method is available as a Python package and web application to facilitate its adoption.
• D2D aims to reduce the complexity barrier for researchers using CLDs to perform dynamic modeling.
• Further research is recommended to validate D2D's utility across diverse applications and domains.