Motivated Metamodels

A metamodel is a relatively small, simple model that approximate the “behavior” of a large, complex model. A common way to develop a metamodel is to generate “data” from a number of largemodel runs and to then use off-the-shelf statistical methods without attempting to understand the model’s internal workings. It is much preferable, in some problems, to improve the quality of such metamodels by using various types of phenomenological knowledge. The benefits are sometimes mathematically subtle, but strategically important, as when one is dealing with a system that could fail if any of several critical components fail. Naïve metamodels may fail to reflect the individual criticality of such components and may therefore be misleading if used for policy analysis. By inserting an appropriate dose of theory, however, such problems can be greatly mitigated. Our work is intended to be a contribution to the emerging understanding of multiresolution, multiperspective modeling.