Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models (Studies in Systems, Decision and Control, 251, Band 251) - Hardcover

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Prof. Jan Treur, Faculty of Sciences, the Vrije Universiteit (VU) in Amsterdam, is the head of the Behavioural Informatics Group. This multidisciplinary group investigates methods and techniques for modelling and analysis of complex human and social processes. Such models are applied to design human- or socially aware applications that support humans or groups of humans in their daily functioning, for example, in the form of smartphone apps or specialized social media. Application areas include support for a healthy lifestyle, active lifestyle, ambient-assisted living, crowd behavior, virtual patients, chronic diseases, and mental disorders such as depression, post-traumatic stress disorders, or autism spectrum disorders. Among Prof. Treur's research interests are Complex Adaptive Systems, Computational Modelling, Human-Aware and Socially Aware Computing, Ambient Intelligence, Social Networks, Socio-Technical Systems, Cognitive, Affective and Social Neuroscience, Biological or Biologically Inspired Modelling, Social and Organization Modelling, and Philosophy of Mind. He has taken part in various National and EU Projects, was and is involved in many conferences as Program Committee Member, Chair or Keynote Speaker, and has more than 600 papers to his name. He has designed successful multidisciplinary curricula for Bachelor and Master programs in Lifestyle Informatics and Socially Aware Computing at his university. He has been and is teaching on modelling topics within these Bachelor and Master curricula and up to now has supervised well over 30 PhD theses within these areas. For further information we refer to the Author's website.

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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 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.