Comparer des méthodes

	Simulation stochastique à événements discrets	Dynamique stochastique des systèmes
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s–1970s	1980s–2000s
Auteur d'origine	Banks, Carson, Nelson, Nicol; Law, A. M.	Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers
Type	Stochastic simulation model	Continuous stochastic simulation
Source fondatrice	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127	Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159
Alias	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES	SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics
Apparentées	6	5
Résumé	Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.	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.