In a comment published in 2020 in the journal Nature 22 scholars take COVID-19 as the occasion for suggesting five ways to make models serve society better. the guidelines for impactвЂ…assessment), the White House OfficeвЂ…ofвЂ…ManagementвЂ…andвЂ…Budget, the IntergovernmentalвЂ…PanelвЂ…onвЂ…ClimateвЂ…Change and USвЂ…EnvironmentalвЂ…ProtectionвЂ…Agency's modeling guidelines. Examples are the EuropeanвЂ…Commission (see e.g. National and international agencies involved in impactвЂ…assessment studies have included sections devoted to sensitivity analysis in their guidelines. In models involving many input variables, sensitivity analysis is an essential ingredient of model building and quality assurance. Sensitivity analysis addresses the second of these issues (although uncertainty analysis is usually a necessary precursor), performing the role of ordering by importance the strength and relevance of the inputs in determining the variation in the output. This requires, first, a quantification of the uncertainty in any model results ( uncertaintyвЂ…analysis) and second, an evaluation of how much each input is contributing to the output uncertainty. Good modeling practice requires that the modeler provide an evaluation of the confidence in the model. Further, models may have to cope with the natural intrinsic variability of the system (aleatory), such as the occurrence of stochastic events. This uncertainty imposes a limit on our confidence in the response or output of the model. Quite often, some or all of the model inputs are subject to sources of uncertainty, including errorsвЂ…ofвЂ…measurement, absence of information and poor or partial understanding of the driving forces and mechanisms. the output is an 'opaque' function of its inputs.

In such cases, the model can be viewed as a blackвЂ…box, i.e. ДЄ mathematicalвЂ…model (for example in biology, climate change, economics or engineering) can be highly complex, and as a result, its relationships between inputs and outputs may be poorly understood. To seek to identify important connections between observations, model inputs, and predictions or forecasts, leading to the development of better models.

Not knowing the sensitivity of parameters can result in time being uselessly spent on non-sensitive ones. In case of calibrating models with large number of parameters, a primary sensitivity test can ease the calibration stage by focusing on the sensitive parameters.Finding regions in the space of input factors for which the model output is either maximum or minimum or meets some optimum criterion (see optimization and Monte Carlo filtering).by making recommendations more credible, understandable, compelling or persuasive). Enhancing communication from modelers to decision makers (e.g.Model simplification вЂ“ fixing model input that has no effect on the output, or identifying and removing redundant parts of the model structure.Searching for errors in the model (by encountering unexpected relationships between inputs and outputs).Uncertainty reduction, through the identification of model input that cause significant uncertainty in the output and should therefore be the focus of attention in order to increase robustness (perhaps by further research).Increased understanding of the relationships between input and output variables in a system or model.Testing the robustness of the results of a model or system in the presence of uncertainty.The process of recalculating outcomes under alternative assumptions to determine the impact of a variable under sensitivity analysis can be useful for a range of purposes, including: A related practice is uncertaintyвЂ…analysis, which has a greater focus on uncertaintyвЂ…quantification and propagationвЂ…ofвЂ…uncertainty ideally, uncertainty and sensitivity analysis should be run in tandem. Sensitivity analysis is the study of how the uncertainty in the output of a mathematicalвЂ…model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs.

Study of uncertainty in the output of a mathematical model or system

Apsim global sensitivity analysis