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 process 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).

系统动力学（SD）是建立模型并模拟复杂动态系统的公认学科。SD的主要目标是评估和设计可以以所需方式影响正在研究的系统的新策略。但是，政策设计（即确定有效的模型杠杆）是一项挑战，并且在许多情况下是反复试验的结果。在本文中，我们介绍了一种新的，一致的模型分析框架，称为结构分析方法（SAM），以促进复杂SD模型中的策略设计过程。SAM提供了一种资源高效且有效的手段来检测候选策略参数。它可以识别激活这些策略参数的预期和非预期效果，并发现候选结构更改，例如在SD模型中引入新变量和链接。SAM的主要创新之处在于，它将SD模型的结构转换为加权有向图，从而允许将图论和网络科学领域的算法工具应用于SD。SAM是基于两个复杂性不断提高的著名模拟模型进行验证的：土壤和泥沙中的三阶磷环（PLUM）模型和五阶World2模型。验证表明，SAM对于分析更复杂的仿真模型（World2）似乎最有价值，而对于低复杂度模型（PLUM）的分析则不太适合。土壤和沉积物中的三阶磷环（PLUM）模型和五阶World2模型。验证表明，SAM对于分析更复杂的仿真模型（World2）似乎最有价值，而对于低复杂度模型（PLUM）的分析则不太适合。土壤和沉积物中的三阶磷环（PLUM）模型和五阶World2模型。验证表明，SAM对于分析更复杂的仿真模型（World2）似乎最有价值，而对于低复杂度模型（PLUM）的分析则不太适合。