Using Parameter Sensitivity and Interdependence to Predict Model Scope and Falsifiability

One important criterion for a model's utility is its scope, the ability to predict a wide range of results. Scope is often difficult to ascertain without extensive data fitting. For example, J. E. Cutting, N. Bruno, N. P. Brady, and C. Moore (1992) compared 2 models of perceived visual depth by fitting many data sets that were arbitrarily generated from underlying functions. They then defined scope as the number of functions a model could account for. We present an alternative technique for scope evaluation that is based on analysis of the behavior of a model's parameters and does not require extensive data fitting. The technique examines the ratio between the overall interdependence among model parameters and their sensitivity, which we show to be inversely related to a model's scope.