Comparer des méthodes

	Microsimulation stochastique	Modèle de Markov stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1957	1993
Auteur d'origine	Guy H. Orcutt	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Type	Stochastic individual-level simulation	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Source fondatrice	Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Alias	Probabilistic Microsimulation, Monte Carlo Microsimulation, Stochastic Micro-simulation, SMSM	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
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.	A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.