Сравнение методов

	Детерминистический анализ сценариев	Стохастический сценарный анализ
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1967	1955–1980s
Автор метода	Kahn, H., Wiener, A. J. (RAND Corporation / Hudson Institute)	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition
Тип	Exploratory planning and decision-support framework	Probabilistic scenario enumeration and evaluation
Основополагающий источник	Kahn, H., Wiener, A. J. (1967). The Year 2000: A Framework for Speculation on the Next Thirty-Three Years. Macmillan, New York. ISBN: 9780025604407	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374
Другие названия	DSA, Fixed-Input Scenario Analysis, Classical Scenario Analysis, Deterministic What-If Analysis	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis
Связанные	5	4
Сводка	Deterministic Scenario Analysis (DSA) is a structured planning method in which analysts construct a finite set of internally consistent future scenarios, each defined by fixed, precisely specified parameter values rather than probability distributions. By running a model or calculation under each scenario's fixed inputs, decision-makers can map how outcomes diverge across plausible futures and stress-test strategies without requiring full probabilistic characterization of uncertainty.	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.