We refer to metamodels constructed in this manner as baseline metamodels.
Unfortunately, baseline metamodels assume that critical thresholds within a single input or relative relationships among multiples inputs of the ABM do not exist.
Our evaluation also compares the effectiveness of our augmented metamodels to alternative metamodels constructed by applying machine learning methodologies.
This paper distinguishes between fitting and validating a metamodel.
Several validation criteria, measures, and estimators are discussed.
Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.
Due to this assumption the baseline metamodel can be misleading because it gives impression that one can compensate for a component of the system by improving some other component even if such a substitution is inadequate or invalid (Friedman & Pressman 1988; dos Santos & Porta 1999; Kleijnen & Sargent 2000; Meckesheimer 2001; Bigelow & Davis 2002).
We propose to address the aforementioned deficiency by augmenting widely used first-order regression analysis with Boolean conditions that highlight when tradeoffs and substitutions among variables are valid.