Comparer des méthodes

	Modèle de Markov à base d'agents	Modélisation Basée sur les Agents (MBA)
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	1970s–1990s (formalized as a field)
Auteur d'origine	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Type	Hybrid simulation — agent-based modeling with Markov state transitions	Computational simulation method
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.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
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.	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.