Diagrams-to-Dynamics (D2D) - Exploring Causal Loop Diagram Leverage Points under Uncertainty

Oct 26, 2026

Causal loop diagrams (CLDs) are widely used to express hypothesized causal structures in health and environmental research, but their qualitative and static nature limits dynamic analysis and intervention planning. Diagrams-to-Dynamics (D2D) converts CLDs into exploratory system dynamics models without empirical data. Using a protocol to label variables as stocks, flows, auxiliaries, and constants, D2D simulates hypothetical interventions, estimates uncertainty, ranks leverage points, and supports future data collection guidance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Abstract

Background

Results

Conclusions

Background

Complexity science and CLDs

Limitations of CLDs for quantitative testing

CLD-to-SDM conversion challenges

Semi-quantitative and network alternatives

Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty Jeroen F. Uleman1,*, Loes Crielaard2, Leonie K. Elsenburg1,2,3, Guido A. Veldhuis4,5, Naja Hulvej Rod1, Rick Quax6, Vítor V. Vasconcelos3,6,7 1 Copenhagen Health Complexity Center, University of Copenhagen, Copenhagen, Denmark. 2 Department of Public and Occupational Health, Amsterdam UMC University of Amsterdam, Amsterdam, The Netherlands. 3 Center for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands. 4 TNO-The Netherlands Organization for Applied Scientific Research, The Hague, the Netherlands 5 Institute for Management Research, Radboud University, Nijmegen, the Netherlands 6 Computational Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands. 7 POLDER center, Institute for Advanced Study, University of Amsterdam, The Netherlands. *[Corresponding author.](Corresponding author. jeroen.uleman@sund.ku.dk)[ jeroen.uleman@sund.ku.dk](Corresponding author. jeroen.uleman@sund.ku.dk) Abstract Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems. However, as qualitative and static representations, CLDs are limited in their ability to support dynamic analysis and inform intervention strategies. We propose Diagrams-to-Dynamics (D2D), a method for converting CLDs into exploratory system dynamics models in the absence of empirical data. With minimal user input—following a protocol to label variables as stocks, flows or auxiliaries, and constants—D2D utilizes the structural information already encoded in CLDs, namely the existence and polarity of causal connections, to simulate hypothetical interventions and explore potentially influential places to intervene, known as‘leverage points,’under uncertainty. Results: D2D helps distinguish between high-and low-ranked leverage points. We compare D2D to a calibrated system dynamics model constructed from the same CLD and variable labels. D2D showed greater consistency with the calibrated model than did static network centrality analysis, while also providing uncertainty estimates and guidance for future data collection. Conclusions: The D2D method is implemented in an open-source Python package and a webbased application to support further testing and to lower the barrier to dynamic modeling for researchers working with CLDs. Future studies could help establish the approach’s utility across a broad range of cases and domains. Keywords: causal loop diagram, system dynamics, analysis, network, quantification, package, Python Background Complex problems arise from interacting components that form feedback loops operating across multiple scales [1, 2] . Addressing them requires moving beyond reductionist approaches and capturing the dynamic interplay between biological, psychological, and social factors [3–5] . Accordingly, complexity science has gained traction in fields like public health [3–7], psychology [8–12], and environmental sciences [13–15], offering novel ways to analyze these interconnected systems. As a key tool in complexity science, causal loop diagrams (CLDs) are increasingly used to explore and communicate the feedback mechanisms underlying complex problems. In health sciences, areas of application have included chronic stress [16, 17], workplace well-being [18], depression [19, 20], and concussion [21] . CLDs map causal connections between key system variables to gain insight into such problems and identify ‘leverage points,’ i.e., intervention targets hypothesized to significantly influence system behavior [22] . The analysis of leverage points often relies on visual inspection [23] or structured qualitative approaches like Meadows’12 Places to Intervene in a System [22], the Intervention Level Framework [24], orthe Action Scales Model [25] . CLDs have also been shown to improve system

FAQs

What problem does Diagrams-to-Dynamics (D2D) address?

D2D addresses the gap between qualitative, static causal loop diagrams and the need for dynamic simulation and formally exploring interventions when empirical data are limited.

How does D2D convert a causal loop diagram into a model?

D2D follows a protocol where users label variables as stocks, flows, auxiliaries, or constants, then uses causal connections and their polarity from the CLD to construct exploratory system dynamics models.

How does D2D evaluate leverage points compared with alternatives?

D2D distinguishes high- and low-ranked leverage points and shows greater consistency with a calibrated system dynamics model than static network centrality analysis, while also providing uncertainty estimates and guidance for data collection.

Oct 26, 2026

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