Abstract for: Structural dominance in large and stochastic models
The last decade and a half has seen a significant effort to develop and automate methods for identifying structural dominance in system dynamics models. To date, however, the interpretation and testing of these methods has been with small (less than 5 stocks), deterministic models that show smooth behavioral transitions. While the analysis of simple and stable models is an obvious first step to provide proof of concept, the methods have become stable enough to be tested in a wider range of models. In this paper we report the findings from expanding the domain of application these methods in two significant dimensions: increasing model size and incorporating stochastic variance in some of the model variables. Exploring the effectiveness of these methods in these two dimensions will increase their applicability into more realistic model analysis situations.While we only show results of the analysis of one large and stochastic model, the results are promising. We find that the methods work as predicted with large stochastic models, that they generate insights that are consistent with the existing explanations for the behavior of the tested model, and that they do so in a very efficient way.