Educational programs leading to degrees in system dynamics are rare and thus of critical importance to the future of the field of system dynamics. However, to a large extent such programs have not yet been made transparent to the system dynamics community as a whole. The present article describes the design and rationale for undergraduate and graduate programs at Worcester Polytechnic Institute (WPI). The goal of the article is to invite feedback from the system dynamics community about our specific programs as well as to facilitate wider discussion about the appropriate content, design, and pedagogy of degree programs and courses in system dynamics.

This paper revisits the use of trend forecasting in driving ordering policies in supply chains by comparing it with derivative control in classical control theory. While both processes involve use of trend to determine policies for achieving reliable performance, the former often worsens instability, while the latter can improve stability with certainty. The similarities and differences between the two processes are discussed and a framework suggested for improving the efficacy of trend forecasting in ordering policies. Copyright © 2009 John Wiley & Sons, Ltd.

While there is a consensus among system dynamics scholars that the dichotomous term validity must be replaced by the term confidence for system dynamics models, it is unclear what qualifies as a system dynamics model – a computational instrument for forecasting, or an experimental tool to inform the policy process? And what exactly needs to be done to build confidence in a model? Confidence building process is described in the system dynamics writings at a rather philosophical level that can be used to justify almost any model. The confidence building procedures provided in the text books are sketchy, do not distinguish between forecasting and policy models and do not adequately describe the iterative process subsumed in the various steps of model construction that might yield confidence. Confidence in forecasting models is an article of faith no matter how detailed they might be and how diligent is their calibration. Forecasting models are albeit irrelevant to system dynamics practice, which must focus on policy. This paper revisits the problem of confidence in system dynamics models addressing policy and attempts to carefully describe their qualification and the process that practitioners must follow to arrive at them.