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Stokastiske Celleautomater×Monte Carlo-simulering×
FagområdeSimuleringBeslutningstagning
FamilieProcess / pipelineMCDM
Oprindelsesår1940s–1980s1949
Ophavspersonvon Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.Metropolis, N., Ulam, S.
TypeGrid-based stochastic simulationRobustness wrapper — Monte Carlo uncertainty propagation
Oprindelig kildeWolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
AliasserSCA, Probabilistic Cellular Automata, PCA, Stochastic CA
Relaterede50
ResuméStochastic 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.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.
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ScholarGateSammenlign metoder: Stochastic Cellular Automata · MONTE-CARLO-SIMULATION. Hentet 2026-06-18 fra https://scholargate.app/da/compare