השוואת שיטות

	ניתוח רגישות גלובלי	סימולציית מונטה קרלו
תחום	סימולציה	קבלת החלטות
משפחה	Process / pipeline	MCDM
שנת המקור	1973–2001	1949
הוגה השיטה	I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)	Metropolis, N., Ulam, S.
סוג	Variance-based sensitivity decomposition	Robustness wrapper — Monte Carlo uncertainty propagation
מקור מכונן	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
כינויים	variance decomposition, Sobol indices, Morris screening, FAST method	—
קשורות	4	0
תקציר	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.