Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational...

Area of Science:

• Complexity science and systems thinking applied to biopsychosocial systems.
• Integration of biological, psychological, and socioenvironmental factors.

Background:

• Systems thinking often results in conceptual models like causal loop diagrams (CLDs) but lacks computational interpretation.
• There's a need to bridge conceptual modeling with dynamic simulation for "what if" scenario analysis in complex systems.

Purpose of the Study:

• To propose a method for converting expert mental models into computable system dynamics models (SDMs).
• To introduce an annotated causal loop diagram (aCLD) facilitating this conversion.
• To enhance the simulation and interpretation of biopsychosocial systems.

Main Methods:

• Capturing expert knowledge into a CLD format with specific annotations.
• Developing an algorithm for creating an annotated CLD (aCLD).
• Formulating a system dynamics model (SDM) based on the aCLD.

Main Results:

• The aCLD includes annotations for evidence sources, intermediary variables, causal link functions, and link certainty.
• The proposed algorithm facilitates the systematic conversion from expert knowledge to a computable SDM.
• The process aids in identifying, quantifying, and reducing uncertainty in model simulations.

Conclusions:

• The systematic approach advances the application of computational science methods to biopsychosocial systems.
• Annotated CLDs provide a bridge between conceptual understanding and dynamic simulation.
• This methodology increases confidence in SDM simulation results for complex systems.