A Paradigm of Model Validation and Validated Models for Best-Estimate-Plus-Uncertainty Predictions in Systems Engineering
What constitutes a validated model? What are the criteria that allow one to defensibly make the claim that they are using a validated model in an analysis? These questions get to the heart of what model validation really implies (conceptually, operationally, interpretationally, etc.), and these details are currently the subject of substantial debate in the V&V community. This is perhaps because many contemporary paradigms of model validation have a limited modeling scope in mind, so the validation paradigms do not span different modeling regimes and purposes that are important in engineering. This paper discusses the different modeling regimes and purposes that it is important for a validation theory to span, and then proposes a validation paradigm that appears to span them. The author's criterion for validated models proceeds from a desire to meet an end objective of “best estimate plus uncertainty” (BEPU) in model predictions. Starting from this end, the author works back to the implications on the model validation process (conceptually, operationally, interpretationally, etc.). Ultimately a shift is required in the conceptualization and articulation of model validation, away from contemporary paradigms. Thus, this paper points out weaknesses in contemporary model validation perspectives and proposes a conception of model validation and validated models that seems to reconcile many of the issues.