5 Reads
·
4 Citations
Quantitative models based on systems thinking and system science are routinely used to explore and anticipate the likely behavior of broad and highly complex issues and problems. While such models can provide valuable insights, they are invariably simplistic and frequently face controversy in both structure and quantitative details. The end result is that, while they may prove valuable in understanding the dynamics of the system, their value in understanding the evolutionary and behavioral tendencies of the system may be quite limited. A qualitative approach based upon structural perspectives can suggest tendencies beyond the scope of quantitative models. This paper presents eight interrelated perspectives for examining a complex issue or problem and for inferring potential evolutionary tendencies or behavior based upon the structural characteristics of the system under study. Experience suggests these perspectives may be useful not only in dealing with qualitative system models, but also in validating and troubleshooting quantitative models. Good system dynamic models contribute to deep understanding of an issue or topic and offer insight to those striving to understand systems or remediate problems. The literature of system dynamics work predominantly focuses on the current situation and near-term forecasts, where quantitative system dynamic models can be particularly accurate and useful. As the time-horizon for understanding the system is extended, the validity of models (system dynamics or otherwise) invariably decreases due to omitted information and mechanisms. In order to build models that are more useful, John Sterman suggests "…modelers must also take care to search for and include in their models the feedback loops, and structures that have not been important in generating dynamics to date but that may become active as the system evolves" (Sterman 2000).