Sensitivitydetermine

Sensitivitydetermine is a framework used to assess how changes in input variables influence the outputs of a model or system. It encompasses practices for quantifying and interpreting the sensitivity of results to input variations, and is applied in fields such as engineering, environmental science, economics, and pharmacology. The aim is to identify which inputs most influence outcomes, to prioritize data collection, calibration, and model refinement, and to support uncertainty analysis and decision making.

Definition and scope: It covers both local sensitivity analysis and global sensitivity analysis, and may integrate uncertainty quantification and scenario analysis. Local analyses examine changes in outputs due to small perturbations around a baseline; global analyses explore input effects over their entire plausible ranges.

Methods: Local sensitivity uses derivatives or finite differences. Global methods include Morris screening, Sobol indices, FAST, and variance-based approaches; surrogate models and emulation can reduce computational cost. Monte Carlo sampling is common; correlation-based methods exist but may be limited by nonlinearities and input dependencies.

Process: Start with problem framing, define outputs of interest, specify input ranges and distributions, select sensitivity metrics, perform computations, and interpret results. Results guide model simplification, improved measurements, and risk assessment.

Limitations: Results depend on model structure, assumptions about input independence, potential nonlinear interactions, and computational demands. Care is needed in interpreting sensitivity results, especially when inputs are correlated or when models are used outside their validated domains.

See also: sensitivity analysis, uncertainty quantification, model calibration, risk assessment.