将重要的循环无缝集成到模型开发和分析中
Seamlessly Integrating Loops That Matter into Model Development and Analysis
摘要：
理解模型为什么会这样做，对于向它们学习以及向广大受众传达它们提供的见解至关重要。重要循环方法自动显示在每个时间点哪些循环是主导行为，并从用户可调整的一组重要循环中生成简化的因果循环图。本文介绍了将这些工具实现到一个功能齐全的模型开发环境中的挑战以及开发的解决方案。工具的承诺，如果有的话，被这个实现的结果放大了，我们给出了几个使用工具的例子。对于教学模型，Loops that Matter可以在加速和深化学习的同时简化沟通。对于复杂的模型，这些工具允许以动画简化因果循环图的形式提取对行为的现实解释。对于具有离散和不连续元素的模型，更大的反馈图像仍然是容易可分解的。虽然毫无疑问，交付的工具会有改进和增强，但它们代表了我们从概念化到交付理解模型的能力向前迈出了一大步。
英文摘要：
Understanding why models behave the way they do is critical to learning from them, and to conveying the insights they offer to a broad audience. The Loops that Matter methodology automatically shows which loops are dominating behavior at each point in time and generates simplified causal loop diagrams from a user adjustable set of important loops. This paper describes the challenges of implementing these tools into a fully functioning model development environment along with the solutions developed. The promise of the tools has, if anything, been amplified by the results of this implementation, and we give several examples of using the tools. For pedagogical models Loops that Matter can ease communication while speeding and deepening learning. For complex models the tools allow the extraction of realistic explanations of behavior in the form of animated simplified causal loop diagrams. For models with discrete and discontinuous elements, the bigger feedback picture is still easily discoverable. While there will doubtless be refinements and enhancement to the delivered tools, they represent a large step forward in our ability to understand models from conceptualization through delivery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
