Searching for important factors in simulation models with many factors: Sequential bifurcation

This paper deals with the problem of 'screening'; that is, how to find the important factors in simulation models that have many (for example, 300) 'factors' (also called simulation parameters or input variables). Screening assumes that only a few factors are really important (parsimony principle). This paper solves the screening problem by a novel technique called 'sequential bifurcation'. This technique is both effective and efficient; that is, it does find all important factors, yet it requires relatively few simulation runs. The technique is demonstrated through a realistic case study, concerning a complicated simulation model, called 'IMAGE'. This simulation models the greenhouse phenomenon (the worldwide increase of temperatures). This case study gives surprising results: the technique identifies some factors as being important that the ecological experts initially thought to be unimportant. Sequential bifurcation assumes that the input/output behavior of the simulation model may be approximated by a first-order polynomial (main effects), possibly augmented with interactions between factors. The technique is sequential; that is, it specifies and analyzes simulation runs, one after the other.