Abstract for: SD Meets OR: A New Synergy to Address Policy Problems

We reflect on our past six years of collaboration to develop systems models that address various challenges regarding the US policies for higher education and scientific workforce development. We offer a central methodological hypothesis that many traditional OR models can be modified with SD feedback loops driven by forces outside of the traditional OR modelerís universe. We argue that such models, even if simple and approximate, will be powerful, insightful, easy to communicate, and effective. We argue that such models can emerge from collaboration between SD and OR. We present three examples of our recent modeling works. The goal here is to communicate policy and decision insights. We elaborate on common characteristics of these small models. A major point is that these modeling examples are not following conventional system dynamics (SD) or operations research (OR) modeling, but each model benefits from both schools of modeling, and addresses a major policy problem. We think the fields of SD and OR can significantly benefit from communication and collaboration. To move forward, and as an example, we discuss the first steps towards integration of queueing models and feedback loop models: endogenous queueing models.