Porovnat metody

	Model agentně založený na Markovových modelech	Diskrétní simulace událostí (DES)
Obor	Simulace	Simulace
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2000s	1960s (formalized); modern computational form from 1970s onward
Tvůrce	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
Typ	Hybrid simulation — agent-based modeling with Markov state transitions	Stochastic process simulation
Původní zdroj	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.	Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127
Další názvy	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Příbuzné	5	4
Shrnutí	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.	Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.