Comparer des méthodes

	Automates Cellulaires Déterministes	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1940s–1950s	1906
Auteur d'origine	John von Neumann and Stanislaw Ulam	Andrei Markov
Type	Discrete deterministic grid simulation	Probabilistic state-transition model
Source fondatrice	von Neumann, J. (1966). Theory of Self-Reproducing Automata. University of Illinois Press, Urbana, IL. (Edited and completed by A. W. Burks.)	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	Deterministic CA, Classical Cellular Automata, Rule-based CA, Finite Automata Grid Model	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	6	5
Résumé	Deterministic Cellular Automata (DCA) is a simulation method that models the evolution of complex systems through a regular grid of cells, each holding a discrete state, updated synchronously at each time step according to a fixed, deterministic rule applied to the cell and its neighbors. The outcome is fully reproducible given the same initial conditions and rule set.	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.