This book uses rigorous mathematical analysis to advance opinion dynamics models for social networks in three major directions. First, a novel model is proposed to capture how a discrepancy between an individual’s private and expressed opinions can develop due to social pressures that arise in group situations or through extremists deliberately shaping public opinion. Detailed theoretical analysis of the final opinion distribution is followed by use of the model to study Asch’s seminal experiments on conformity, and the phenomenon of pluralistic ignorance. Second, the DeGroot-Friedkin model for evolution of an individual’s social power (self-confidence) is developed in a number of directions. The key result establishes that an individual’s initial social power is forgotten exponentially fast, even when the network changes over time; eventually, an individual’s social power depends only on the (changing) network structure. Last, a model for the simultaneous discussion of multiple logically interdependent topics is proposed. To ensure that a consensus across the opinions of all individuals is achieved, it turns out that the interpersonal interactions must be weaker than an individual’s introspective cognitive process for establishing logical consistency among the topics. Otherwise, the individual may experience cognitive overload and the opinion system becomes unstable. Conclusions of interest to control engineers, social scientists, and researchers from other relevant disciplines are discussed throughout the thesis with support from both social science and control literature.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

This book, which resulted from an intensive discourse between experts from several disciplines – complexity theorists, cognitive scientists, philosophers, urban planners and urban designers, as well as a zoologist and a physiologist – addresses various issues regarding cities. It is a first step in responding to the challenge of generating just such a discourse, based on a dilemma identified in the CTC (Complexity Theories of Cities) domain. The latter has demonstrated that cities exhibit the properties of natural, organic complex systems: they are open, complex and bottom-up, have fractal structures and are often chaotic. CTC have further shown that many of the mathematical formalisms and models developed to study material and organic complex systems also apply to cities. The dilemma in the current state of CTC is that cities differ from natural complex systems in that they are hybrid complex systems composed, on the one hand, of artifacts such as buildings, roads and bridges, and of natural human agents on the other. This raises a plethora of new questions on the difference between the natural and the artificial, the cognitive origin of human action and behavior, and the role of planning and designing cities. The answers to these questions cannot come from a single discipline; they must instead emerge from a discourse between experts from several disciplines engaged in CTC.

This book aims to gather the insight of leading experts on corruption and anti-corruption studies working at the scientific frontier of this phenomenon using the multidisciplinary tools of data and network science, in order to present current theoretical, empirical, and operational efforts being performed in order to curb this problem. The research results strengthen the importance of evidence-based approaches in the fight against corruption in all its forms, and foster the discussion about the best ways to convert the obtained knowledge into public policy. The contributed chapters provide comprehensive and multidisciplinary approaches to handle the non-trivial structural and dynamical aspects that characterize the modern social, economic, political and technological systems where corruption takes place. This book will serve a broad multi-disciplinary audience from natural to social scientists, applied mathematicians, including law and policymakers.

The chapters in this volume, and the volume itself, celebrate the life and research of Roberto Tempo, a leader in the study of complex networked systems, their analysis and control under uncertainty, and robust designs. Contributors include authorities on uncertainty in systems, robustness, networked and network systems, social networks, distributed and randomized algorithms, and multi-agent systems—all fields that Roberto Tempo made vital contributions to. Additionally, at least one author of each chapter was a research collaborator of Roberto Tempo’s. This volume is structured in three parts. The first covers robustness and includes topics like time-invariant uncertainties, robust static output feedback design, and the uncertainty quartet. The second part is focused on randomization and probabilistic methods, which covers topics such as compressive sensing, and stochastic optimization. Finally, the third part deals with distributed systems and algorithms, and explores matters involving mathematical sociology, fault diagnoses, and PageRank computation. Each chapter presents exposition, provides new results, and identifies fruitful future directions in research. This book will serve as a valuable reference volume to researchers interested in uncertainty, complexity, robustness, optimization, algorithms, and networked systems.

This book constitutes the refereed proceedings of the 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, held in Frankfurt/Main, Germany, in August 2018. The 21 papers presented were carefully reviewed and selected from 39 submissions. They cover the main areas in animat research, including the animat approach and methodology, perception and motor control, action selection and navigation, learning and adaptation, and collective and social behavior.

This book presents recent research in intelligent and fuzzy techniques on digital transformation and the new normal, the state to which economies, societies, etc. settle following a crisis bringing us to a new environment. Digital transformation and the new normal-appearing in many areas such as digital economy, digital finance, digital government, digital health, and digital education are the main scope of this book. The readers can benefit from this book for preparing for a digital “new normal” and maintaining a leadership position among competitors in both manufacturing and service companies. Digitizing an industrial company is a challenging process, which involves rethinking established structures, processes, and steering mechanisms presented in this book. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc., and Ph.D. students studying digital transformation and new normal. The book covers fuzzy logic theory and applications, heuristics, and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers.

Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.

This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Applied Mathematics, and co-Director of MACSI, at the University of Limerick, Ireland.