SpringerBriefs in Complexity – serie

This book presents a broad, multidisciplinary review of the factors that have been shown to or might influence sharing information on social media, regardless of its veracity.

This book is about improving human decision making and performance in complex, dynamic tasks.

This book deals with the science of science by applying network science methods to citation networks and uniquely presents a physics-inspired model of citation dynamics.

This concise monograph introduces and examines social influence from the perspective of the so-called target, rather than from the source, thus providing for the first time a bidirectional account of this pervasive social phenomenon, further bridging  simple micro-level dyadic interaction rules with macro-level properties of the (social) system. This integrative approach allows for advanced models of influence to be developed in both the social and natural sciences (e.g. social animals). In particular, when used to investigate emergent properties of social change, this approach shows that social transitions occur as “bubbles of new” in the “sea of old.”  While in the traditional view influence is synonymous with achieving power and control over others, the present approach to social influence puts the emphasis on the target’s motives and strategies. Here, the target may actively seek out influence to help forge opinions and achieve guidance regarding courses of action. In this process, the target observes others, models their thought and behavior, and asks for information and opinions. In this broadened perspective, the processes of social influence enables those being influenced (the targets) to use the knowledge and processing capacity of influence sources to maximize their access to information, minimize their processing effort, while optimizing their own functioning and that of the social system in which they evolve. This short text addresses above all scientists interested in social influence in the fields of psychology, sociology, economy, marketing, and biology. However, also researchers interested in modeling social processes, especially opinion dynamics and social change, such as computer scientists, physicists and applied mathematicians will benefit from the insights provided.

In the face of growing challenges, we need modes of thinking that allow us to not only grasp complexity but also perform it. The author presents a new framework for operationalising complex thinking in a set of dimensions and properties through which it may be enacted.

This book is a collection of reflections by thought leaders at first-mover organizations in the exploding field of "Data Science for Social Good", meant as the application of knowledge from computer science, complex systems and computational social science to challenges such as humanitarian response, public health, sustainable development. The book provides both an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies that are pushing forward this new sector. This book will appeal to students and researchers in the rapidly growing field of data science for social impact, to data scientists at companies whose data could be used to generate more public value, and to decision makers at nonprofits, foundations, and agencies that are designing their own agenda around data.

The authors introduce “New Knowledge” by presenting a methodology, 'The Decalogue’, that portrays a genuinely systemic approach for managing complexity in organizations and Value Chains through focusing on the management of a leverage point called constraint (Theory of Constraints) and the understanding of variation (Theory of Profound Knowledge).

This book presents the iterative beauty of fractals and fractal functions graphically with the aid of MATLAB programming. As it covers basic fractals like Sierpinski triangle to advanced fractal functions with explicit MATLAB code, the presented fractal illustrations hopefully benefit even the non-field readers.

This book delves into the current state and future prospects of systems thinking and sustainability education within business schools. It meticulously examines the trends and drivers shaping the demand and supply of such education, along with the implications and challenges it presents for various stakeholders and society at large. Strategic recommendations and suggestions are provided to elevate and propel systems thinking and sustainability education in business schools, outlining a visionary roadmap for the future.Furthermore, the book explores the intersectionality of sustainability and diversity in business education, offering examples and cases of visionary and innovative initiatives and projects in the field.Distinguished by special features such as illustrations, the book offers a comprehensive and integrative overview of the current landscape and future trajectories of systems thinking and sustainability education in business schools.The primary benefit for readers lies in gaining a deeper and broader understanding of systems thinking and sustainability education in business schools. It equips them with the knowledge to apply systems thinking and sustainability principles and tools to tackle the complex and wicked problems of the twenty-first century. Additionally, the book aims to inspire and inform business schools and their stakeholders to embrace and enhance systems thinking and sustainability education in their curricula and pedagogy, contributing to the advancement of sustainability and systems thinking in both business and society.

Understanding the processes behind the evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which involves finding the model from a set of candidates that best explains a given network. This book is a systematic review of methods for this purpose. Each method is outlined in three parts: its core principle (used to organize methods into four categories), other relevant details including my own observations, and software availability. The book provides a comprehensive overview of the state-of-the-art in network model selection and concludes by exploring future directions. A unified, optimal method could identify the mechanisms that shape real-world networks more precisely than any current approach. This work represents the first step toward developing such an optimal method. It will be a valuable resource for students and researchers in network science.

This monograph demonstrates the interplay between Shannon information and semantic information in cognition. It shows that Shannon’s information acts as driving force for the formation of semantic information; and vice versa, namely, that semantic information participates in the formation of Shannonian information. The authors show that in cognition, Shannonian and semantic information are interrelated as two aspects of a cognitive process termed as information adaptation. In the latter the mind/brain adapts to the environment by the deflating and/or inflating of the information conveyed by the environment. In the process of information adaptation, quantitative variations in Shannon’s information entail different meanings while different meanings affect the quantity of information. The book illustrates the above conceptually and mathematically by reference to three cognitive processes: pattern recognition, face learning and the recognition of a moving object.

Using a system dynamics approach, this book illustrates the physics of fundamental accumulation processes (stocks and flows) across the demand and supply sectors of energy systems. Examples of system dynamics simulation models are presented where these accumulation processes are driving the behavior of the system. Based on these modeling efforts, two cases (the socio-economic and environmental implications of the energy policy of Pakistan and the dynamics of green power in Ontario, Canada) are analyzed and discussed. By studying the dynamics of the fundamental structures of an energy system, the reader gains an enhanced understanding of the stocks and flows of complex systems as well as their role in energy policy. This book is of use to managers and practitioners, teachers, researchers, and students of design and assessment of policy making for complex, dynamic energy systems.

This book covers the modelling of human behaviour in the education and labour markets, which due to their interdependency are viewed as one system. Important factors influencing the decision-making of individuals and firms in this system are discussed. The role of social environment and networks is stressed. The approach of agent-based modelling is presented and compared with standard economic modelling and other simulation techniques in the context of modelling complex adaptive systems. Practical questions in building agent-based models of labour–education market system with social networks are discussed. These questions include modelling the structure of education system and agent behaviour there; modelling and calibrating the labour market without and with firms; generating the social network, defining its behaviour and calibrating it; and embedding the resulting system into a larger model.

This brief presents readers with a summary of classic, modern, and state-of-the-art methods for discovering the roles of entities in networks (including social networks) that range from small to large-scale. It classifies methods by their mathematical underpinning, whether they are driven by implications about entity behaviors in system, or if they are purely data driven. The brief also discusses when and how each method should be applied, and discusses some outstanding challenges toward the development of future role mining methods of each type.

This Brief revisits and extends Epstein’s classical agent-based model of civil violence by considering important mechanisms suggested by social conflict theories. Among them are: relative deprivation as generator of hardship, generalized vanishing of the risk perception (‘massive fear loss’) when the uprisings surpass a certain threshold, endogenous legitimacy feedback, and network influence effects represented by the mechanism of dispositional contagion. The model is explored in a set of computer experiments designed to provide insight on how mechanisms lead to increased complexity of the solutions. The results of the simulations are compared with statistical analyses of estimated size, duration and recurrence of large demonstrations and riots for eight African countries affected by the “Arab Spring,” based on the Social Conflict Analysis Database.It is shown that the extensions to Epstein’s model proposed herein lead to increased “generative capacity”of the agent-based model (i.e. a richer set of meaningful qualitative behaviors) as well the identification of key mechanisms and associated parameters with tipping points. The use of quantitative information (international indicators and statistical analyses of conflict events) allows the assessment of the plausibility of input parameter values and simulated results, and thus a better understanding of the model’s strengths and limitations.The contributions of the present work for understanding how mechanisms of large scale conflict lead to complexbehavior include a new form of the estimated arrest probability, a simple representation of political vs economic deprivation with a parameter which controls the `sensitivity' to value, endogenous legitimacy feedback, and the effect of network influences (due to small groups and “activists”). In addition, the analysis of the Social Conflict Analysis Database provided a quantitative description of the impact of the “Arab Spring” in several countries focused on complexity issues such as peaceful vs violent, spontaneous vs organized, and patterns of size, duration and recurrence of conflict events in this recent and important large-scale conflict process. This book will appeal to students and researchers working in these computational social science subfields.

This book introduces and discusses both the fundamental aspects and the measurability of applications of time-symmetric kinetic quantities, outlining the features that constitute the non-dissipative branch of non-equilibrium physics.These specific features of non-equilibrium dynamics have largely been ignored in standard statistical mechanics texts. This introductory-level book offers novel material that does not take the traditional line of extending standard thermodynamics to the irreversible domain.It shows that although stationary dissipation is essentially equivalent with steady non-equilibrium and ubiquitous in complex phenomena, non-equilibrium is not determined solely by the time-antisymmetric sector of energy-entropy considerations. While this should not be very surprising, this book provides timely, simple reminders of the role of time-symmetric and kinetic aspects in the construction of non-equilibrium statistical mechanics.

This book provides a comprehensive overview on emergent bursty patterns in the dynamics of human behaviour. It presents common and alternative understanding of the investigated phenomena, and points out open questions worthy of further investigations.The book is structured as follows. In the introduction the authors discuss the motivation of the field, describe bursty phenomena in case of human behaviour, and relate it to other disciplines. The second chapter addresses the measures commonly used to characterise heterogeneous signals, bursty human dynamics, temporal paths, and correlated behaviour. These definitions are first introduced to set the basis for the discussion of the third chapter about the observations of bursty human patterns in the dynamics of individuals, dyadic interactions, and collective behaviour. The subsequent fourth chapter discusses the models of bursty human dynamics. Various mechanisms have been proposed about the source of the heterogeneities in human dynamics, which leads to the introduction of conceptually different modelling approaches. The authors address all of these perspectives objectively, highlight their strengths and shortcomings, and mention possible extensions to them. The fifth chapter addresses the effect of individual heterogeneous behaviour on collective dynamics. This question in particular has been investigated in various systems including spreading phenomena, random walks, and opinion formation dynamics. Here the main issues are whether burstiness speeds up or slows down the co-evolving processes, and how burstiness modifies time-dependent paths in the system that determine the spreading patterns of any kind of information or influence. Finally in the sixth chapter the authors end the review with a discussion and future perspectives.It is an ideal book for researchers and students who wish to enter the field of bursty human dynamics or want to expand their knowledge on such phenomena.

This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties. After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem of network reconstruction and introduces various advanced techniques to reliably infer the topology of a network from partial local information. Chapter 5 is devoted to the reformulation of certain “hard” combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a “softened” maximum-entropy framework. A final chapter offers various overarching remarks and take-home messages.By requiring no prior knowledge of network theory, the book targets a broad audience ranging from PhD students approaching these topics for the first time to senior researchers interested in the application of advanced network techniques to their field.

This book presents an introduction to Evolutionary Game Theory (EGT) which is an emerging field in the area of complex systems attracting the attention of researchers from disparate scientific communities. EGT allows one to represent and study several complex phenomena, such as the emergence of cooperation in social systems, the role of conformity in shaping the equilibrium of a population, and the dynamics in biological and ecological systems.Since EGT models belong to the area of complex systems, statistical physics constitutes a fundamental ingredient for investigating their behavior. At the same time, the complexity of some EGT models, such as those realized by means of agent-based methods, often require the implementation of numerical simulations. Therefore, beyond providing an introduction to EGT, this book gives a brief overview of the main statistical physics tools (such as phase transitions and the Ising model) and computational strategies for simulating evolutionary games (such as Monte Carlo algorithms on lattices).  This book will appeal to students and researchers in this burgeoning field of complex systems.

This book systematically interprets and documents new, unifying principles and basic laws describing the most relevant aspects of hierarchy. To do so, it discusses recent experiments and models that are simple and realistic enough to reproduce the observations, and develops concepts for a better understanding of the complexity of systems consisting of many organisms. The book covers systems ranging from flocks of birds to groups of people. Although it focuses on hierarchical collective behavior in general, two aspects pop up in the majority of cases: collective motion and dynamically changing, partially directed networks (and the natural relation between the two). In addition, it offers a brief description of the most relevant definitions and concepts involved in the context of hierarchies, presenting both a review of the current literature and a number of new experimental and computational results in more detail. It is a valuable resource for students and scholars pursuing research on the structure of interactions within the collectives of animals and humans.

This book deals with artificial intelligence (AI) and its several applications. It is not an organic text that should be read from the first page onwards, but rather a collection of articles that can be read at will (or at need). The idea of this work is indeed to provide some food for thoughts on how AI is impacting few verticals (insurance and financial services), affecting horizontal and technical applications (speech recognition and blockchain), and changing organizational structures (introducing new figures or dealing with ethical issues).The structure of the chapter is very similar, so I hope the reader won’t find difficulties in establishing comparisons or understanding the differences between specific problems AI is being used for. The first chapter of the book is indeed showing the potential and the achievements of new AI techniques in the speech recognition domain, touching upon the topics of bots and conversational interfaces. The second and thirds chapter tackle instead verticals that are historically data-intensive but not data-driven, i.e., the financial sector and the insurance one. The following part of the book is the more technical one (and probably the most innovative), because looks at AI and its intersection with another exponential technology, namely the blockchain. Finally, the last chapters are instead more operative, because they concern new figures to be hired regardless of the organization or the sector, and ethical and moral issues related to the creation and implementation of new type of algorithms.

This book provides the basis of a formal language and explores its possibilities in the characterization of multiplex networks. Armed with the formalism developed, the authors define structural metrics for multiplex networks. A methodology to generalize monoplex structural metrics to multiplex networks is also presented so that the reader will be able to generalize other metrics of interest in a systematic way. Therefore, this book will serve as a guide for the theoretical development of new multiplex metrics. Furthermore, this Brief describes the spectral properties of these networks in relation to concepts from algebraic graph theory and the theory of matrix polynomials. The text is rounded off by analyzing the different structural transitions present in multiplex systems as well as by a brief overview of some representative dynamical processes. Multiplex Networks will appeal to students, researchers, and professionals within the fields of network science, graph theory, and data science.

This book introduces and discusses the analysis of interacting many-body complex systems exhibiting spontaneous synchronization from the perspective of nonequilibrium statistical physics. While such systems have been mostly studied using dynamical system theory, the book underlines the usefulness of the statistical physics approach to obtain insightful results in a number of representative dynamical settings. Although it is intractable to follow the dynamics of a particular initial condition, statistical physics allows to derive exact analytical results in the limit of an infinite number of interacting units. Chapter one discusses dynamical characterization of individual units of synchronizing systems as well as of their interaction and summarizes the relevant tools of statistical physics. The latter are then used in chapters two and three to discuss respectively synchronizing systems with either a first- or a second-order evolution in time. This book provides a timely introduction to the subject and is meant for the uninitiated as well as for experienced researchers working in areas of nonlinear dynamics and chaos, statistical physics, and complex systems.

This Brief is an essay at the interface of philosophy and complexity research, trying to inspire the reader with new ideas and new conceptual developments of cellular automata. Going beyond the numerical experiments of Steven Wolfram, it is argued that cellular automata must be considered complex dynamical systems in their own right, requiring appropriate analytical models in order to find precise answers and predictions in the universe of cellular automata.Indeed, eventually we have to ask whether cellular automata can be considered models of the real world and, conversely, whether there are limits to our modern approach of attributing the world, and the universe for that matter, essentially a digital reality.