Comparer des méthodes

	Microsimulation stochastique	Simulation stochastique à événements discrets
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1957	1960s–1970s
Auteur d'origine	Guy H. Orcutt	Banks, Carson, Nelson, Nicol; Law, A. M.
Type	Stochastic individual-level simulation	Stochastic simulation model
Source fondatrice	Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127
Alias	Probabilistic Microsimulation, Monte Carlo Microsimulation, Stochastic Micro-simulation, SMSM	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES
Apparentées	6	6
Résumé	Stochastic Microsimulation tracks a large population of individual units — people, households, or firms — through time by applying random draws from empirically estimated probability distributions at each transition event. Unlike deterministic counterparts, every state change is decided by chance, preserving realistic heterogeneity and allowing rigorous uncertainty quantification across multiple simulation runs.	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.