- 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
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- About this book
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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 (10 chapters)
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Introduction
Pages 1-10
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Background and Concepts
Pages 11-33
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Agent-Based Models as Markov Chains
Pages 35-55
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The Voter Model with Homogeneous Mixing
Pages 57-82
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From Network Symmetries to Markov Projections
Pages 83-107
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Table of contents (10 chapters)
- Download Preface 1 PDF (65.5 KB)
- Download Sample pages 2 PDF (290.9 KB)
- Download Table of contents PDF (96 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Markov Chain Aggregation for Agent-Based Models
- Authors
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- Sven Banisch
- Series Title
- Understanding Complex Systems
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-24877-6
- DOI
- 10.1007/978-3-319-24877-6
- Hardcover ISBN
- 978-3-319-24875-2
- Softcover ISBN
- 978-3-319-79691-8
- Series ISSN
- 1860-0832
- Edition Number
- 1
- Number of Pages
- XIV, 195
- Number of Illustrations
- 65 b/w illustrations, 18 illustrations in colour
- Topics
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