A formalized procedure to identify the impact of changes in various model components on model output. Sensitivity analysis is an integral part of simulation experimentation and may influence model formulations. It is commonly used to examine model behaviour. The general procedure is to define a model output variable that represents an important aspect of model behaviour. The values of various inputs of the model are then varied and the resultant change in the output variable is monitored. Large changes in the output variable imply that the particular input varied is important in controlling model behaviour. Within this general definition, sensitivity analysis has been applied to a variety of model inputs including state variables, environmental variables and initial conditions.
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem. 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:
* Testing the robustness of the results of a model or system in the presence of uncertainty.
* Increased understanding of the relationships between input and output variables in a system or model.
* 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).
* Searching for errors in the model (by encountering unexpected relationships between inputs and outputs).
* Model simplification – fixing model input that has no effect on the output, or identifying and removing redundant parts of the model structure.
* Enhancing communication from modelers to decision makers (e.g. by making recommendations more credible, understandable, compelling or persuasive).
* 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).
* 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. Not knowing the sensitivity of parameters can result in time being uselessly spent on non-sensitive ones.
* To seek to identify important connections between observations, model inputs, and predictions or forecasts, leading to the development of better models.
