Comparer des méthodes

	Automates Cellulaires Stochastiques	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1940s–1980s	1906
Auteur d'origine	von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.	Andrei Markov
Type	Grid-based stochastic simulation	Probabilistic state-transition model
Source fondatrice	Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	SCA, Probabilistic Cellular Automata, PCA, Stochastic CA	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	5	5
Résumé	Stochastic Cellular Automata (SCA) extend classical cellular automata by replacing deterministic transition rules with probabilistic ones, allowing each cell on a grid to change state according to a probability distribution conditioned on its neighborhood. This makes SCA a powerful tool for simulating real-world spatial processes where randomness, noise, and uncertainty govern local interactions — from epidemic spread and forest fires to traffic flow and material diffusion.	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.