论文标题
自动图生成以建立理解和可用性
Automated Diagram Generation to Build Understanding and Usability
论文作者
论文摘要
因果环，库存和流程图广泛用于系统动力学，因为它们有助于组织关系并传达意义。本文使用Schoenberg（2019）的分析工作选择在压缩模型中包括的内容，展示了如何在自动生成的因果环图中清楚地介绍该信息。这些图是使用图理论中工作人员开发的工具生成的，生成的图表清晰且美观。也可以建立这种方法来产生库存和流程图。自动库存和流程图生成为代表仅使用方程式开发的模型打开了大门，无论如何或起源，以清晰易理解的方式开发。由于模型可能很大，因此再次为图理论开发的分组技术的应用可以帮助以最可用的形式构造所得图。本文介绍了为自动图生成开发的算法，并显示了它们在大型模型中使用的许多示例。这些技术将这些技术应用于现有但无法访问的基于方程式的模型可以帮助扩大系统动力学建模的知识库。这些技术还可以用于改善具有图形信息的所有现有模型的布局。
Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. Using the analytical work of Schoenberg (2019) to select what to include in a compressed model, this paper demonstrates how that information can be clearly presented in an automatically generated causal loop diagram. The diagrams are generated using tools developed by people working in graph theory and the generated diagrams are clear and aesthetically pleasing. This approach can also be built upon to generate stock and flow diagrams. Automated stock and flow diagram generation opens the door to representing models developed using only equations, regardless or origin, in a clear and easy to understand way. Because models can be large, the application of grouping techniques, again developed for graph theory, can help structure the resulting diagrams in the most usable form. This paper describes the algorithms developed for automated diagram generation and shows a number of examples of their uses in large models. The application of these techniques to existing, but inaccessible, equation-based models can help broaden the knowledge base for System Dynamics modeling. The techniques can also be used to improve layout in all, or part, of existing models with diagrammatic informtion.
