Compară metode

	Model Markov cu Agenți	Model Markovian Stocastic
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s	1993
Autorul original	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Tip	Hybrid simulation — agent-based modeling with Markov state transitions	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Sursa seminală	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.	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
Denumiri alternative	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Înrudite	5	6
Rezumat	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 Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.