An approach to system dynamics modeling is advocated that adheres to t
he scientific method, and that may be applied regardless of model scop
e or size. Scientific modeling is distinguished from other approaches
largely by the quality of evaluatin and revision performed and by an i
nsistence upon empirical evidence to support hypotheses and formulatio
ns. Three case studies drawn from the author's experience are presente
d. Practical lessons for scientific modeling are given to help guide e
xpectations and maximize effectiveness of the approach. Modelers and c
lients should clearly understand the level of rigor they wish to pursu
e and what this means for the degree of confidence that may be placed
in model results and insights.