Screening for Dispersion Effects by Sequential Bifurcation

The mean of the output of interest obtained from a run of a computer simulation model of a system or process often depends on many factors; many times, however, only a few of these factors are important. Sequential bifurcation is a method that has been considered by several authors for identifying these important factors using as few runs of the simulation model as possible. In this article, we propose a new sequential bifurcation procedure whose steps use a key stopping rule that can be calculated explicitly, something not available in the best methods previously considered. Moreover, we show how this stopping rule can also be easily modified to efficiently identify those factors that are important in influencing the variability rather than the mean of the output. In empirical studies, the new method performs better than previously published fully sequential bifurcation methods in terms of achieving the prescribed Type I error. It also achieves higher power for detecting moderately large effects using fewer replications than earlier methods. To achieve this control for midrange effects, the new method sometimes requires more replications than other methods in the case where there are many very large effects.