MODEL VALIDATION BASED ON VALUE-OF-INFORMATION THEORY

The modeling and simulation community has devoted considerable attention to the question of model validity. Their work has focused on the concept of "accuracy," loosely defined as the difference between a model-computed result and a real-world result. This paper makes use of an example case that results in a paradox to illustrate weaknesses in an accuracy-focused approach, and proposes in its stead a value-focused approach based on classical decision theory. Instead of advocating the use of a model based on its accuracy, this work advocates using a model if it adds value to the overall application thus relating validation directly to system performance. The approach fills significant gaps in the current theory, notably providing a clearly defined validity metric and a fundamental rationale for the use of this metric.