Overview
- Provides a generic, unified approach to Network-Oriented Modeling for adaptive individual and social human processes
- Addresses the adaptivity of any order by means of (multilevel) reified temporal-causal networks
- Makes it easy to incorporate theories and findings from cognitive, affective, and social neuroscience into modeling
- Provides means of addressing the complexity of adaptive dynamical processes from the Network-Oriented Modeling perspective
Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 251)
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Table of contents (16 chapters)
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Front Matter
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Introduction
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Front Matter
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Modeling Adaptive Networks by Network Reification
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Front Matter
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Applications of Higher-Order Adaptive Network Models
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Front Matter
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A Modeling Environment for Reified Networks
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Front Matter
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Mathematical Analysis of How Emerging Network Behaviour Relates to Base Network Structure
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Front Matter
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Mathematical Analysis of How Emerging Network Behaviour of Adaptive Networks Relates to Reified Network Structure
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Front Matter
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Keywords
About this book
This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level.
This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-OrientedModeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications.
Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes.
The book is suitable for multidisciplinary Master’s and Ph.D. students withoutassuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.Authors and Affiliations
Bibliographic Information
Book Title: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models
Authors: Jan Treur
Series Title: Studies in Systems, Decision and Control
DOI: https://doi.org/10.1007/978-3-030-31445-3
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-31444-6Published: 13 November 2019
Softcover ISBN: 978-3-030-31447-7Published: 13 November 2020
eBook ISBN: 978-3-030-31445-3Published: 01 November 2019
Series ISSN: 2198-4182
Series E-ISSN: 2198-4190
Edition Number: 1
Number of Pages: XVII, 412
Topics: Complexity, Data Engineering, Applications of Graph Theory and Complex Networks, Computational Intelligence, Information Systems Applications (incl. Internet)