Refining the Causal Loop Diagram: A Tutorial for Maximizing the Contribution of Domain Expertise in Computational System Dynamics Modeling

Translational Abstract Systems thinking is essential to study complex problems that arise from many interacting system parts at different levels. An example of a complex problem is depression, related to individual biological and psychological characteristics, but also our society and environment. To schematically describe which system parts are important in explaining a complex problem, it is common to draw a visual representation of the system that produces the problem, i.e., a causal loop diagram (CLD). Even though a CLD can tell us a lot about the origins of a complex problem, it cannot show what the effect would be if a system part were changed. For example, even if the CLD indicates that income inequality is an important system part in explaining depression, we still cannot tell from just this visual representation whether depression rates would change if income inequality were reduced. To answer such questions, we need to develop a computational model reflecting the CLD: a computer program that can calculate what would happen under different conditions. This article describes how to create a CLD that can be converted into a computational system dynamics model. We propose to achieve this conversion by asking experts to communicate their knowledge about individual system parts and storing this information within the CLD. This expert knowledge then helps us calculate what happens when all system parts interact simultaneously. This method can aid in answering questions about the effects of changes in system parts for a broader range of complex problems. Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate what if scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems.

Automated summary

This article describes the process of using causal loop diagrams (CLDs) and computational system dynamics models (SDMs) to study and simulate complex problems. By incorporating expert knowledge and converting it into a computational model, we can better understand the interactions and effects of different parts of a system, as well as the outcomes under different conditions. This approach can be applied to a broader range of complex problems and advances the application of computational science methods to biopsychosocial systems.