System Dynamics Modelling
Applying System Dynamics Modelling (SDM) involves using causal loop diagramming for qualitative models to visualize and understand the feedback loops and relationships within a system. For several purposes, this causal loop diagramming is sufficient. For quantitative models, these causal loop diagrams serve as a foundation for developing data-driven models that can simulate system behavior over time. By integrating both qualitative and quantitative approaches, SDM provides a comprehensive tool for analyzing complex systems and predicting the impact of different variables.
The process of SD (in theory and practice) involves distinct phases. Depending on the size of the project, I generally use a phased approach to make projects and processes more tangible:
| Phase 1 | Defining the concept/framework/stakeholder sessions: Making Causal loop diagrams |
| Phase 2 | Group sessions, model validation, processing data: Making a data model |
| Phase 3 + | Simulation, policy analyis, visual interface, monitor use: Making a platform |
Qualitative modelling
In qualitative expert modelling, the modeler establishes a Causal Loop Diagram on the basis of literature, stakeholder input, and expert validation (phase 1 to 3). This three-month plan for qualitative system dynamics modeling guides you from understanding core underlying principles to analyzing behaviour via feedback-thinking and causal pathways, providing a structured approach to grasping the complex problem. As I use a phased approach, Most activities are carried out in phase 1.
The end product is a detailed report featuring well-validated causal loop diagrams that highlight key feedback loops and system behaviors. This report provides actionable insights and recommendations for addressing the identified system dynamics, helping decision-makers in strategic planning.
Roughly, qualitative modeling is carried out up to step 3, whereas quantitative modeling extends through to step 5.
Quantitative modelling
This process for quantitative system dynamics modeling enables precise simulation and analysis of complex systems (phase 1 to 5). Through systematic data collection, model building, and scenario testing, it uncovers key variables and leverage points, offering valuable insights for strategic decision-making and system optimization.
The end product is an interactive visual interface displaying the quantitative model, including data sources, simulation results, and key insights. This interface provides users with actionable recommendations for optimizing system performance and supporting strategic decision-making.