So sánh phương pháp

	Phân tích Kịch bản và Mô phỏng Giả định	Phân tích độ nhạy toàn cục
Lĩnh vực	Mô phỏng	Mô phỏng
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1950s (origins); widely adopted in management since 1970s	1973–2001
Người khởi xướng	Peter Schwartz (scenario planning formalization), Herman Kahn (RAND Corporation, 1950s–60s)	I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)
Loại	Structured analytical approach / simulation	Variance-based sensitivity decomposition
Công trình gốc	Goodwin, P. & Wright, G. (2014). Decision Analysis for Management Judgment (5th ed.). Wiley. ISBN: 978-1118173671	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.
Tên gọi khác	what-if analysis, what-if simulation, stress testing, scenario planning	variance decomposition, Sobol indices, Morris screening, FAST method
Liên quan	3	4
Tóm tắt	Scenario analysis is a structured analytical approach that systematically compares system outputs across different combinations of uncertain input values. When paired with a quantitative model, it becomes a simulation — capable of stress-testing assumptions and projecting the range of plausible outcomes. Formalised in strategic planning by Peter Schwartz and Herman Kahn from the 1950s onward, the method is widely used in policy evaluation, business forecasting, financial risk assessment, and scientific model exploration.	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.