Сравнение методов

	Байесовский сценарный анализ	Робастный сценарный анализ
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s	1950 (foundations); 2003 (modern RDM formulation)
Автор метода	Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s)	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
Тип	Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis	Scenario-based robustness evaluation
Основополагающий источник	Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231.	Wald, A. (1950). Statistical Decision Functions. Wiley, New York.
Другие названия	BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
Связанные	5	5
Сводка	Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures.	Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty.