Comparer des méthodes

	Automates Cellulaires Stochastiques	Modélisation Basée sur les Agents (MBA)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1940s–1980s	1970s–1990s (formalized as a field)
Auteur d'origine	von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Type	Grid-based stochastic simulation	Computational simulation method
Source fondatrice	Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	SCA, Probabilistic Cellular Automata, PCA, Stochastic CA	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
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.	Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.