Comparer des méthodes

	Modèle de Markov à base d'agents	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	1906
Auteur d'origine	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Andrei Markov
Type	Hybrid simulation — agent-based modeling with Markov state transitions	Probabilistic state-transition model
Source fondatrice	Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Alias	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	5	5
Résumé	The Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently samples its next state from a probability transition matrix, enabling the model to capture both micro-level heterogeneity across agents and the tractable probabilistic structure of Markov chains. The approach is widely used in health economics, epidemiology, social science, and operations research.	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.