Compara mètodes

	Anàlisi de Sensibilitat Robusta	Simulació Monte Carlo
Camp	Simulació	Presa de decisions
Família	Process / pipeline	MCDM
Any d'origen	1990s–2000s	1949
Autor original	Saltelli, A. and colleagues	Metropolis, N., Ulam, S.
Tipus	Simulation-based robustness assessment pipeline	Robustness wrapper — Monte Carlo uncertainty propagation
Font seminal	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Àlies	RSA, Robust SA, Sensitivity Analysis under Uncertainty, Uncertainty-robust sensitivity analysis	—
Relacionats	3	0
Resum	Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.