A perspective on generative artificial intelligence for the enhancement of methods in system dynamics

System Dynamics (SD) offers a set of indispensable tools for systems engineering, systems thinking, and cybernetics. To date, the development of such valuable models — crucial for ensuring causality and reproducibility in studies addressing socio-technical and social sustainability issues — has been predominantly manual. Furthermore, the criteria and processes involved in creating models such as Causal Loop Diagrams (CLD) and Stock and Flow Diagrams (SFD) are, in many cases, neither explainable nor reproducible. Recently, some studies have expressed some potential for generative artificial intelligence to be considered as a valuable tool for the automated construction of CLD models. Although promising, still many problems affect this technique but, most of all, there is a clear gap in the construction of SFD and in the transformation of a CLD into an equivalent SFD. SFDs are indeed essential when a dynamic has to be encoded into a mathematical system, in order to allow simulations, predictions and, most of all, synthesis of some control system. In this work, we would like to provide a perspective on the envisioned methodologies that have the potential to provide breakthroughs in this area, with a special focus on generative artificial intelligence, which is currently the best available technology for these kinds of transformations.