| Document Type
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BL
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| Record Number
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649969
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| Doc. No
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dltt
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| Main Entry
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Banisch, Sven
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| Title & Author
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Markov chain aggregation for agent-based models /\ Sven Banisch
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| Series Statement
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Understanding complex systems,
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Springer complexity
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| Page. NO
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1 online resource (xiv, 195 pages) :: illustrations (some color).
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| ISBN
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9783319248776
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: 3319248774
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9783319248752
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: 3319248758 (print)
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: 9783319248752 (print)
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| Bibliographies/Indexes
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Includes bibliographical references
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| Contents
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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
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| Abstract
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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 ĺlmicro-chainĺl 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 ĺlvoter-likeĺl 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
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| Subject
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Markov processes.
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| Subject
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Multiagent systems.
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| Dewey Classification
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519.2/33
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| LC Classification
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QA274.7
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| Added Entry
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Ohio Library and Information Network.
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