System dynamics (SD) is an established discipline to model and simulate complex dynamic systems. The primary goal of SD is to evaluate and design new policies that can impact the system under study in a desired way. Policy design, that is, identifying effective model levers, however, is a challenge and in many cases trial-and-error driven. In this article, we introduce a new and coherent framework for model analysis, called structural analysis methods (SAM), to facilitate the policy design

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... ss in complex SD models. SAM provides a resource-efficient and effective means for the detection of candidate policy parameters. It enables to identify intended and unintended effects of activating these policy parameters, and to discover candidate structural changes such as introducing new variables and links in SD models. The main innovation of SAM is that it translates the structure of SD models into weighted digraphs allowing algorithmic tools from the realms of graph theory and network science to be applied to SD. SAM is validated on the basis of two well-known simulation models of increasing complexity: the third-order Phosphorus Loops in Soil and Sediment (PLUM) model and the fifth-order World2 model. The validation shows that SAM seems to be most valuable for the analysis of more complex simulation models (World2) and is less suited for the analysis of low complexity models (PLUM).