Comparer des méthodes

	Dynamique stochastique des systèmes	Analyse de sensibilité
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1980s–2000s	2004
Auteur d'origine	Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Type	Continuous stochastic simulation	Robustness wrapper — parameter / weight perturbation sensitivity indices
Source fondatrice	Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester
Alias	SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics	—
Apparentées	5	0
Résumé	Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios.	SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.