Evaluative models are concerned with ascribing meaning to information; that is, they interpret information. To contrast evaluative models and statistical models, consider the following example. We might use a statistical model to describe how much woody debris we would expect in a mile of stream given the stream gradient and channel type, but, having predicted the quantity of woody debris, we might wish to say something about whether this predicted value is good or bad for salmon spawning habitat. In order to make an assertion about spawning habitat, we need a model that interprets the predicted value for woody debris. Formal logic-based models use membership functions (a type of interpretive model) to make such interpretations. Decision software also often uses utility functions (another form of interpretive model) to rate the attributes of alternatives with respect to satisfying some overall goal.
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