Automated Diagram Generation to Build Understanding and Usability

TL;DR

This paper presents algorithms for automatically generating clear, aesthetically pleasing causal loop and stock and flow diagrams from models, enhancing understanding and usability in System Dynamics.

Contribution

It introduces novel algorithms for automated diagram generation from models, including grouping techniques for large models, based on graph theory.

Findings

Generated diagrams are clear and aesthetically pleasing.

Algorithms successfully applied to large, complex models.

Automated diagrams help make equation-based models more accessible.

Abstract

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.