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Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models

  • Jan Treur
Book

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 251)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Modeling Adaptive Networks by Network Reification

  4. Applications of Higher-Order Adaptive Network Models

  5. A Modeling Environment for Reified Networks

  6. Mathematical Analysis of How Emerging Network Behaviour Relates to Base Network Structure

  7. Mathematical Analysis of How Emerging Network Behaviour of Adaptive Networks Relates to Reified Network Structure

  8. Finalising

About this book

Introduction

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-Oriented Modeling 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 without assuming 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.

Keywords

Adaptive Networks Network-Oriented Modeling Temporal-Causal Network Models Adaptation Principles Higher Order Adaptation Network Reification Relation between Network Structure and Emerging Network Adaptive Mental Networks Adaptive Social Networks Plasticity and Metaplasticity of Networks

Authors and affiliations

  1. 1.Social AI Group, Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-31445-3
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-31444-6
  • Online ISBN 978-3-030-31445-3
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site