I have developed lots of System Dynamics models over the years for both private and public organizations. My models have all been built to answer strategic questions for which there was no single obvious answer, due to the presence of dynamic complexities including accumulations, delays, nonlinearities, and feedback loops. These complexities cause the impacts of interventions to look different in the long term than in the short term, and different than a static or purely linear approach would suggest. This is what sets SD models apart as my clients see it.
While my models all include these complexities, they may not always adhere to a traditional view of what an SD model should and should not contain. The party line is that a model’s boundary should be broad enough so that the system’s main observed behaviors—such as S-shaped growth, oscillation, or overshoot and decline—are fully explained by the model’s endogenous structure. One should avoid the use of exogenous time series drivers, because they undermine the ability of the model to explain and to anticipate change.
I mostly agree with this view but want to offer a friendly amendment here. In my experience with real-world clients, I have often encountered situations in which it makes sense to employ exogenous time series for the sake of completeness and realism.
For example, one model, done for the Gates Foundation, addressed policies to accelerate the decline of childhood malnutrition and stunting in the developing world. This decline is primarily the result of rising GDP per capita, which I represented (mostly) exogenously. The contribution of the model was not to explain the fundamental reason for the decline of childhood stunting (the general GDP effect was already known) but rather to identify its detailed mechanisms, show their natural limits over time due to nonlinearities, and quantify the further impacts across the aging chain as childhood stunting turns into adult infirmity and disability.
A second recent example involves a model of the U.S. opioid epidemic done for a client interested in quantifying attributable cause and financial liability. This model starts from prescriptions being written and wends its way through the dynamics of illicit use, addiction, and overdose. Some of the characteristic growth behavior of the model is driven by exogenous time series inputs for prescription frequency and dose, as well as time series for the influx of deadly fentanyl illegally manufactured outside the country. Yet, in this case, too, the use of the exogenous time series did not take away from the model’s ability to do the dynamic analysis that most mattered to the client.
My experience suggests that we should be less doctrinaire about the endogenous perspective and understand that “endogenous” is a relative thing. No model can be all-encompassing and explain all observed behavior patterns. That’s why we define a model relative to some subset of behaviors also known as the dynamic problem. As long as the model adequately addresses the dynamic problem, it shouldn’t really matter if the model has some exogenous time series included to improve the model’s realism.
I would like academics to listen more closely to the experiences of practitioners as they think about how SD can make a contribution in the world. They need not fear that modifying their traditional view of the fully-endogenous model is an invitation to poor quality. It is possible for a model to be somewhat less comprehensively endogenous than that view might dictate, but still high quality and capable of addressing the dynamic problem.