Validating the Assumptions of Sequential Bifurcation in Factor Screening

Sequential bifurcation (SB) is a very efficient and effective method for identifying the important factors (inputs) of simulation models with very many factors, provided the SB assumptions are valid. A variant of SB called multiresponse SB (MSB) can be applied to simulation models with multiple types of responses (outputs). The specific SB and MSB assumptions are: (i) a second-order
polynomial per output is an adequate approximation (valid metamodel) of the implicit input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation per output is monotonic); (iii) heredity applies; i.e., if an input has no important first-order effect, then this input has no important second-order effects. To validate these three assumptions, we develop new methods. We compare these methods through Monte Carlo experiments and a case study.