Σύγκριση μεθόδων

	Ανάλυση Παγκόσμιας Ευαισθησίας	Προσομοίωση Monte Carlo
Πεδίο	Προσομοίωση	Λήψη Αποφάσεων
Οικογένεια	Process / pipeline	MCDM
Έτος προέλευσης	1973–2001	1949
Δημιουργός	I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)	Metropolis, N., Ulam, S.
Τύπος	Variance-based sensitivity decomposition	Robustness wrapper — Monte Carlo uncertainty propagation
Θεμελιώδης πηγή	Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Εναλλακτικές ονομασίες	variance decomposition, Sobol indices, Morris screening, FAST method	—
Συναφείς	4	0
Σύνοψη	Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed.	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.