Markov Chain Aggregation for Agent-Based Models by Sven Banisch (English) Paperb

Description: Markov Chain Aggregation for Agent-Based Models by Sven Banisch 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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 Back Cover 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 Table of Contents Introduction.- Background and Concepts.- Agent-based Models as Markov Chains.- The Voter Model with Homogeneous Mixing.- From Network Symmetries to Markov Projections.- Application to the Contrarian Voter Model.- Information-Theoretic Measures for the Non-Markovian Case.- Overlapping Versus Non-Overlapping Generations.- Aggretion and Emergence: A Synthesis.- Conclusion. Feature Introduces and describes a new approach for modelling certain types of complex dynamical systems Self-contained presentation and introductory level Useful as advanced text and as self-study guide Details ISBN3319796917 Author Sven Banisch ISBN-10 3319796917 ISBN-13 9783319796918 Format Paperback Series Understanding Complex Systems Year 2018 DEWEY 519 Pages 195 Imprint Springer International Publishing AG Place of Publication Cham Country of Publication Switzerland Publication Date 2018-03-30 Language English UK Release Date 2018-03-30 Illustrations 18 Illustrations, color; 65 Illustrations, black and white; XIV, 195 p. 83 illus., 18 illus. in color. Edited by Voyner Ravena-Canete Birth 1974 Affiliation European University Viadrina, Germany Position journalist Qualifications Ph.D. Publisher Springer International Publishing AG Edition Description Softcover reprint of the original 1st ed. 2016 Alternative 9783319248752 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:128955446;

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