TY - JOUR
T1 - Refining the Causal Loop Diagram
T2 - A Tutorial for Maximizing the Contribution of Domain Expertise in Computational System Dynamics Modeling
AU - Crielaard, Loes
AU - Uleman, Jeroen F.
AU - Châtel, Bas D. L.
AU - Epskamp, Sacha
AU - Sloot, Peter M. A.
AU - Quax, Rick
N1 - Funding Information: All authors have nothing to disclose. Sacha Epskamp is an associate editor of the journal Psychological Methods. We acknowledge Nina de Boer, Jonas Dalege, and Julian Burger for their role in revising the final version of this article. We also acknowledge Viktor van der Valk and Julia Anten for their contributions in the early stages of this work. Part of this project was funded by the Netherlands Organization for Health Research and Development (ZonMw) under Project 531003015. Part of this project was granted as GEENA-Q-19-595225 by the American Alzheimer’s Association to M. Olde Rikkert and an unrestricted grant of the research funds from JM v Rossum, Nijmegen, the Netherlands. And part of this research received funding from the Netherlands Organisation for Scientific Research (NWO), Grant 645.003.002, as a part of the Social Health Games project in collaboration with Games for Health and Cooperation Dela Publisher Copyright: © 2022 American Psychological Association
PY - 2022/5/12
Y1 - 2022/5/12
N2 - 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-beabsent 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.
AB - 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-beabsent 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.
KW - Causal loop diagram
KW - Complexity science
KW - Group model building
KW - System dynamics modeling
KW - Systems thinking
UR - http://www.scopus.com/inward/record.url?scp=85130775738&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/35549316/
U2 - https://doi.org/10.1037/met0000484
DO - https://doi.org/10.1037/met0000484
M3 - Article
C2 - 35549316
SN - 1082-989X
JO - Psychological methods
JF - Psychological methods
ER -