Process / pipelineSimulation / optimization
Agent-Based Markov Model — Hybrid Simulation with Autonomous Agents and Markov State Transitions
Agent-Based Markov Model (ABMM) 是一种混合仿真框架，它将马尔可夫链的状态转移逻辑嵌入到各个自主的智能体中。每个智能体独立地从概率转移矩阵中抽取其下一个状态，从而使模型能够同时捕捉到智能体之间的微观层面异质性以及马尔可夫链的可处理概率结构。该方法广泛应用于健康经济学、流行病学、社会科学和运筹学。
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来源
- 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. DOI: 10.1073/pnas.082080899 ↗
- Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge, UK. ISBN: 9780521633963
如何引用本页
ScholarGate. (2026, June 3). Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions. ScholarGate. https://scholargate.app/zh/simulation/agent-based-markov-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Agent-Based Discrete-Event Simulation仿真↔ compare
- 基于主体的建模（ABM）仿真↔ compare
- 离散事件仿真 (DES)仿真↔ compare
- 马尔可夫模型仿真↔ compare
- 随机马尔可夫模型仿真↔ compare