Abstract for: Structural Analysis of System Dynamics Models

There is a significant leap from qualitative system dynamics, i.e., causal problem mapping, to quantitative system dynamics, where model relationships need to be mathematically formalized and parameter values must be estimated. While the former is quite intuitive, accessible, and manageable with limited resources, the latter in contrast demands sophisticated analytical and modeling skills and requires a relatively large budget of resources. Consequently, policy as well as decision-makers, often poorly trained in mathematical modeling and having restricted resources, refrain from using quantitative system dynamics. Instead, they prefer applying qualitative system dynamics to their complex strategic issues. However, modeling complex strategic issues by means of qualitative system dynamics only and exclusively, bears the risk of generating weakly validated and potentially misleading results. Therefore, we propose an intermediate phase in the system dynamics modeling process to close the gap between qualitative and quantitative system dynamics. In that intermediate phase, concept models emerging from qualitative system dynamics, are comprehensively analyzed based exclusively on their structure. We introduce a suite of tools for the algorithmic detection of high-leverage points, intended and unintended consequences thereof, and system archetypes in system dynamics models. We illustrate the benefits and restrictions of these tools by analyzing the PLUM Model and the World2 Model. Beyond system dynamics, these tools might also be applied to cognitive maps or other causal models.