Traditional system dynamics studies rely heavily upon heuristics and experi
ence. Nevertheless, mathematical exploration techniques have been introduce
d as important elements for a successful study. We argue that the role of o
ptimization in system dynamics studies is not to replace experience-based k
nowledge, but instead to augment, facilitate, and expand the heuristic expl
oration of a model. Accordingly, our approach involves narrowing the design
space (using response surfaces) and the subsequent direct investigation of
the simulation model (using heuristics), Response surfaces have received c
onsiderable attention in optimization because of their capability to replac
e complex models with analytic equations, thereby increasing computational
efficiency. However, doubts exist as to the usefulness of a response-surfac
e approximation of an approximation of reality (i.e., a system dynamics mod
el). We demonstrate the usefulness of response surfaces in system dynamics
studies with a case study involving a high-level model of an industrial eco
system; our intent in using response surfaces is not to replace the simulat
ion models with analytic equations, but instead to direct attention to regi
ons within the design space of the original simulation with the most desira
ble performance. Recommended changes to a system are based directly on the
simulation model, not on response surfaces, avoiding the added level of app
roximation inherent in response surfaces. The primary focus of the article
is on the concept exploration approach, which is presented first. The case
study towards the end is offered as supporting evidence. Copyright (C) 2000
John Wiley & Sons, Ltd.