เปรียบเทียบวิธี

	การวิเคราะห์ความไวทั่วโลก	การจำลองแบบมอนติคาร์โล
สาขาวิชา	การจำลอง	การตัดสินใจ
ตระกูล	Process / pipeline	MCDM
ปีกำเนิด	1973–2001	1949
ผู้ริเริ่ม	I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)	Metropolis, N., Ulam, S.
ประเภท	Variance-based sensitivity decomposition	Robustness wrapper — Monte Carlo uncertainty propagation
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	variance decomposition, Sobol indices, Morris screening, FAST method	—
ที่เกี่ยวข้อง	4	0
สรุป	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.