方法对比

	Agent-Based Markov Model	离散事件仿真 (DES)
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	2000s	1960s (formalized); modern computational form from 1970s onward
提出者	Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
类型	Hybrid simulation — agent-based modeling with Markov state transitions	Stochastic process simulation
开创性文献	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
别名	ABMM, Agent-Based Markov Chain Model, ABM-Markov hybrid, Agent Markov simulation	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
相关	5	4
摘要	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.