论文标题
寻找重要的循环
Finding the Loops that Matter
论文作者
论文摘要
重要方法的循环（Schoenberg et al，2019）用于理解模型行为提供指标，以显示模型中反馈回路对每个时间点行为的贡献。为了提供这些指标，有必要找到来计算它们的循环集。我们在本文中显示了在模拟中不同点重要的循环。这些重要的循环可能并非彼此独立，不能从模型结构的静态分析中确定。然后，我们描述了一种算法，该算法可用于发现模型中最重要的循环，这些循环太多，无法富含反馈，无法详尽地发现。我们证明了该算法的使用，以找到最大的解释性循环的能力以及对大型模型的计算性能。通过使用这种方法，可以将重要方法的循环应用于任何大小或复杂性的模型。
The Loops that Matter method (Schoenberg et. al, 2019) for understanding model behavior provides metrics showing the contribution of the feedback loops in a model to behavior at each point in time. To provide these metrics, it is necessary find the set of loops on which to compute them. We show in this paper the necessity of including loops that are important at different points in the simulation. These important loops may not be independent of one another and cannot be determined from static analysis of the model structure. We then describe an algorithm that can be used to discover the most important loops in models that are too feedback rich for exhaustive loop discovery. We demonstrate the use of this algorithm in terms of its ability to find the most explanatory loops, and its computational performance for large models. By using this approach, the Loops that Matter method can be applied to models of any size or complexity.
