Methoden vergleichen

	Mehrziel-Szenarioanalyse	Stochastische Szenarioanalyse
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2013 (integrated framework); scenario analysis roots: 1967	1955–1980s
Urheber	Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967)	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition
Typ	Structured qualitative-quantitative hybrid	Probabilistic scenario enumeration and evaluation
Wegweisende Quelle	Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688.	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374
Aliasnamen	MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis
Verwandt	4	4
Zusammenfassung	Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy.	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.