Automated Diagram Generation to Build Understanding and Usability
  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.
