Comparer des méthodes

	Modèle de Markov à base d'agents	Simulation à événements discrets basée sur des agents
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	2000s
Auteur d'origine	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Hybridization formalized by multiple authors; Siebers & Aickelin, Lagergren & Buckley among key contributors
Type	Hybrid simulation — agent-based modeling with Markov state transitions	Hybrid simulation paradigm
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.	Lagergren, J. H., & Buckley, E. (2010). A hybrid approach to simulation: Combining agent-based and discrete event simulation. Proceedings of the 2010 Winter Simulation Conference, pp. 170–181. IEEE.
Alias	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	AB-DES, Hybrid ABM-DES, Agent-DES, Hybrid Agent-Based Discrete-Event Simulation
Apparentées	5	4
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.	Agent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-making heterogeneity of individual entities while maintaining the precise, time-stamped flow control of discrete-event simulation, making it suitable for complex systems where both individual agency and process sequencing matter.