Usporedite metode

	Stohastička analiza scenarija	Simulacija Monte Carlo
Područje	Simulacija	Donošenje odluka
Obitelj	Process / pipeline	MCDM
Godina nastanka	1955–1980s	1949
Tvorac	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition	Metropolis, N., Ulam, S.
Vrsta	Probabilistic scenario enumeration and evaluation	Robustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvor	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Drugi nazivi	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis	—
Srodne	4	0
Sažetak	Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.	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.