Comparer des méthodes

	Automates Cellulaires Déterministes	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1940s–1950s	1949
Auteur d'origine	John von Neumann and Stanislaw Ulam	Metropolis, N., Ulam, S.
Type	Discrete deterministic grid simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	von Neumann, J. (1966). Theory of Self-Reproducing Automata. University of Illinois Press, Urbana, IL. (Edited and completed by A. W. Burks.)	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	Deterministic CA, Classical Cellular Automata, Rule-based CA, Finite Automata Grid Model	—
Apparentées	6	0
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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.