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Agent-based Markov model

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.

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Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions

Taksonomisk metoderegistrering · process-pipeline / simulation

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

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Taxonomic bucketAgent-based Discrete-Event Simulationmachine-suggested · Relational suggestion, not evidence.Same method familyAgent-Based Modelingmachine-suggested · Relational suggestion, not evidence.Same method familyDiscrete-Event Simulationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMarkov Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketStochastic Markov Modelmachine-suggested · Relational suggestion, not evidence.