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Markov Chain Aggregation for Agent-Based Models by Sven Banisch at Abbey's Bookshop,
Markov Chain Aggregation for Agent-Based Models
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Sven Banisch

Markov Chain Aggregation for Agent-Based Models

Sven Banisch

$260.95   $208.75

Paperback

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English
Springer International Publishing AG
30 March 2018
Probability & statistics; Applied mathematics; Chaos theory; Mathematical physics; Technology: general issues
Series: Understanding Complex Systems
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
By:  
Imprint:   Springer International Publishing AG
Country of Publication:   Switzerland
Edition:   Softcover reprint of the original 1st ed. 2016
Dimensions:   Height: 235mm,  Width: 155mm,  Spine: 11mm
Weight:   454g
ISBN:   9783319796918
ISBN 10:   3319796917
Series:   Understanding Complex Systems
Pages:   195
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

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