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AbstractThis article is based on my talk at the 2019 International System Dynamics Conference on the occasion of receiving the Jay. W. Forrester Award for the article, "Structural dominance analysis of large and stochastic models" (System Dynamics Review 2016, 32(1): 26–51). I summarize here the history of the research project that led to the award‐winning article. I present the evolution of the ideas in a non‐technical way that develops the intuition for how eigenvalue elasticity analysis works and highlights the power of its explanations. I discuss what I believe to be the main benefits of structural dominance analysis and address the major criticisms that have been raised against it, and I close by reflecting on why I believe the capability to formally establish links between structure and behavior will become more salient in a context that pushes for larger models and demands higher standards of evidence.© 2020 System Dynamics Society

AbstractThe argument of this article is that it is possible to focus on the structural complexity of system dynamics models to design a partition strategy that maximizes the test points between the model and the real world, and a calibration sequence that permits an incremental development of model confidence. It further argues that graph theory could be used as a basis for making sense of the structural complexity of system dynamics models, and that this structure could be used as a basis for more formal analysis of dynamic complexity. After reviewing the graph representation of system structure, the article presents the rationale and algorithms for model partitions based on data availability and structural characteristics. Special attention is given to the decomposition of cycle partitions that contain all the model's feedback loops, and a unique and granular representation of feedback complexity is derived. The article concludes by identifying future research avenues in this arena. Copyright © 2004 John Wiley & Sons, Ltd.

AbstractThe purpose of this article is to report on improvements on the interpretation and insights emerging from dynamic decomposition weight analysis (DDWA). These improvements emerged from efforts to further automate and expand the eigenvalue elasticity analysis methods and resolve inconsistencies in assumptions made in published reports of DDWA usage. In addition to making available to the broad system dynamics community an improved toolset to perform eigenvalue elasticity analysis, in this paper we clarify the set of assumptions needed to obtain reliable results and develop a new framework to assess the impact of model parameters on the projections of behavior modes on stock behavior. We illustrate the use of these developments by updating a previously published model analysis. The paper concludes by summarizing our findings and their implications for the further development of structural dominance analysis.© 2018 System Dynamics Society

AbstractGeoff Coyle has recently posed the question as to whether or not there may be situations in which computer simulation adds no value beyond that gained from qualitative causal‐loop mapping. We argue that simulation nearly always adds value, even in the face of significant uncertainties about data and the formulation of soft variables. This value derives from the fact that simulation models are formally testable, making it possible to draw behavioral and policy inferences reliably through simulation in a way that is rarely possible with maps alone. Even in those cases in which the uncertainties are too great to reach firm conclusions from a model, simulation can provide value by indicating which pieces of information would be required in order to make firm conclusions possible. Though qualitative mapping is useful for describing a problem situation and its possible causes and solutions, the added value of simulation modeling suggests that it should be used for dynamic analysis whenever the stakes are significant and time and budget permit. Copyright © 2001 John Wiley & Sons, Ltd.