השוואת שיטות

	אוטומטים תאיים סטוכסטיים	סימולציית מונטה קרלו
תחום	סימולציה	קבלת החלטות
משפחה	Process / pipeline	MCDM
שנת המקור	1940s–1980s	1949
הוגה השיטה	von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.	Metropolis, N., Ulam, S.
סוג	Grid-based stochastic simulation	Robustness wrapper — Monte Carlo uncertainty propagation
מקור מכונן	Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
כינויים	SCA, Probabilistic Cellular Automata, PCA, Stochastic CA	—
קשורות	5	0
תקציר	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.	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.