Uncertainty quantification in simulation models: a proposed framework and application through case study

Abstract

Despite the great advances in modeling and simulation techniques, modelers and researchers acknowledge that models are simplified representations of reality and, hence, are subject to uncertainty and errors. Although models are inevitably uncertain, they can still be a valuable decision-support tool if the users are informed about the uncertainty in the results. The importance of model uncertainty identification and quantification becomes clear in this context, but there are numerous challenges that remain. In this work, an uncertainty analysis framework is proposed for simulation models. This framework comprises of the steps that must be performed to analyze the uncertainty in simulation models. Next, an application of the framework is discussed where entropy is used as a possible measure of input-uncertainty. By using this framework, stakeholders can be better advised regarding the applicability and uncertainty of the simulation model, which will lead to an appropriate adjustment of expectations on the model results.