Adding one and one makes two, usually. But sometimes things add up to more than the sum of their parts. This observation, now frequently expressed in the maxim “more is different”, is one of the characteristic features of complex systems and, in particular, complex networks. Along with their ubiquity in real world systems, the ability of networks to exhibit emergent dynamics, once they reach a certain size, has rendered them highly attractive targets for research. The resulting network hype has made the word “network” one of the most in uential buzzwords seen in almost every corner of science, from physics and biology to economy and social sciences. The theme of “more is different” appears in a different way in the present v- ume, from the viewpoint of what we call “adaptive networks.” Adaptive networks uniquely combine dynamics on a network with dynamical adaptive changes of the underlying network topology, and thus they link classes of mechanisms that were previously studied in isolation. Here adding one and one certainly does not make two, but gives rise to a number of new phenomena, including highly robust se- organization of topology and dynamics and other remarkably rich dynamical beh- iors.

Public and private managers who are looking for sustainable development have to implement innovative solutions in a complex field of action. Joint action is needed, but the existing power networks within and between public and private domains tend to frustrate joint innovations. This book analyzes how public and private managers deal with energy transitions by creating innovative networks capable of co-ordinated action. A case study shows how separated power networks in the field of mobility, energy and environment, which are spread over the public and private world as well as civil society, are becoming more interconnected

Adding one and one makes two, usually. But sometimes things add up to more than the sum of their parts. This observation, now frequently expressed in the maxim “more is different”, is one of the characteristic features of complex systems and, in particular, complex networks. Along with their ubiquity in real world systems, the ability of networks to exhibit emergent dynamics, once they reach a certain size, has rendered them highly attractive targets for research. The resulting network hype has made the word “network” one of the most in uential buzzwords seen in almost every corner of science, from physics and biology to economy and social sciences. The theme of “more is different” appears in a different way in the present v- ume, from the viewpoint of what we call “adaptive networks.” Adaptive networks uniquely combine dynamics on a network with dynamical adaptive changes of the underlying network topology, and thus they link classes of mechanisms that were previously studied in isolation. Here adding one and one certainly does not make two, but gives rise to a number of new phenomena, including highly robust se- organization of topology and dynamics and other remarkably rich dynamical beh- iors.

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.

In the last years, adaptive networks have been discovered simultaneously in different fields as a universal framework for the study of self-organization phenomena. Understanding the mechanisms behind these phenomena is hoped to bring forward not only empirical disciplines such as biology, sociology, ecology, and economy, but also engineering disciplines seeking to employ controlled emergence in future technologies. This volume presents new analytical approaches, which combine tools from dynamical systems theory and statistical physics with tools from graph theory to address the principles behind adaptive self-organization. It is the first class of approaches that is applicable to continuous networks. The volume discusses the mechanisms behind three emergent phenomena that are prominently discussed in the context of biological and social sciences:• synchronization,• spontaneous diversification, and• self-organized criticality.Self-organization in continuous adaptive networks contains extended research papers. It can serve as both, a review of recent results on adaptive self-organization as well as a tutorial of new analytical methodsSelf-organization in continuous adaptive networks is ideal for academic staff and master/research students in complexity and network sciences, in engineering, physics and maths.

A coherent introduction to the basic concepts of pattern recognition, incorporating recent advances from AI, neurobiology, engineering, and other disciplines. Treats specifically the implementation of adaptive pattern recognition to neural networks. Annotation copyright Book News, Inc. Portland, Or.

Cognitive networks can be crucial for the evolution of future communication systems; however, current trends have indicated major movement in other relevant fields towards the integration of different techniques for the realization of self-aware and self-adaptive communication systems. Evolution of Cognitive Networks and Self-Adaptive Communication Systems overviews innovative technologies combined for the formation of self-aware, self-adaptive, and self-organizing networks. By aiming to inform the research community and the related industry of solutions for cognitive networks, this book is essential for researchers, instructors, and professionals interested in clarifying the latest trends resulting in a unified realization for cognitive networking and communication systems.