Produktbild: Markov Chain Aggregation for Agent-Based Models

Markov Chain Aggregation for Agent-Based Models

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Understanding Complex Systems

ISBN: 3319796917
ISBN 13: 9783319796918
Autor: Banisch, Sven
Verlag: Springer Verlag GmbH
Umfang: xiv, 195 S., 65 s/w Illustr., 18 farbige Illustr., 195 p. 83 illus., 18 illus. in color.
Erscheinungsdatum: 30.03.2018
Auflage: 1/2016
Produktform: Kartoniert
Einband: Kartoniert

This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting „micro-chain“ including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the updating rule and governs the dynamics at a Markovian level, plays a crucial part in the analysis of „voter-like“ models used in population genetics, evolutionary game theory and social dynamics. The book demonstrates that the problem of aggregation in ABMs – and the lumpability conditions in particular – can be embedded into a more general framework that employs information theory in order to identify different levels and relevant scales in complex dynamical systems

Artikelnummer: 5456287 Kategorie:

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E-Mail: juergen.hartmann@springer.com

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