parametersensitive

Parameter sensitive describes systems, models, or algorithms whose behavior or performance changes in response to the values of one or more parameters. The term is used across disciplines to emphasize that outputs are not invariant under parameter variation, and that parameter choices can qualitatively alter results.

In mathematics and computer science, parameter sensitivity is studied to understand how small changes in parameters affect stability, convergence, or accuracy. Examples include numerical solvers whose error depends on step size or tolerance, and statistical models whose predictions depend on prior or likelihood parameters.

Assessing parameter sensitivity typically involves sensitivity analysis. Local methods examine partial derivatives of outputs with respect to parameters, while global methods explore the impact across the full parameter space. Common techniques include Morris screening, Sobol indices, and variance-based approaches. Properly scaling and identifying identifiable parameters are important, as non-identifiable or ill-conditioned parameters can distort sensitivity estimates.

Applications span engineering, physics, economics, and data science. In optimization, algorithms can be parameter-sensitive with respect to hyperparameters such as learning rate or regularization strength; in dynamic systems, model behavior can hinge on parameters governing damping, stiffness, or feedback gains. Awareness of parameter sensitivity supports model calibration, robustness analysis, and experimental design.

See also: sensitivity analysis, parameter estimation, identifiability, robustness, model calibration.