Validation Risk in Hierarchical Models

Abstract

Using a model as an input data source for integration into another model carries with it risks to the validity of the model composition. This paper presents research into the inherent risks of model integration. The research decomposes models into sets of semantic concepts allowing for a calculation of structural alignment. Measurable changes in a model’s output due to the integration of another model provide an impact assessment. Risks to decisions arise from incompatible assumptions and constructions of models. We present a risk assessment as a tuple containing differences in models’ alignment across three axes and in changes in a model’s output metrics. Risks to decisions arise from incompatible assumptions and constructions of models.