This invaluable book captures the proceedings of a workshop that brought together a group of distinguished scientists from a variety of disciplines to discuss how networking influences decision making. The individual lectures interconnect psychological testing, the modeling of neuron networks and brain dynamics to the transport of information within and between complex networks. Of particular importance was the introduction of a new principle that governs how complex networks talk to one another — the Principle of Complexity Management (PCM). PCM establishes that the transfer of information from a stimulating complex network to a responding complex network is determined by how the complexity indices of the two networks are related. The response runs the gamut from being independent of the perturbation to being completely dominated by it, depending on the complexity mismatch.

This invaluable book captures the proceedings of a workshop that brought together a group of distinguished scientists from a variety of disciplines to discuss how networking influences decision making. The individual lectures interconnect psychological testing, the modeling of neuron networks and brain dynamics to the transport of information within and between complex networks. Of particular importance was the introduction of a new principle that governs how complex networks talk to one another ? the Principle of Complexity Management (PCM). PCM establishes that the transfer of information from a stimulating complex network to a responding complex network is determined by how the complexity indices of the two networks are related. The response runs the gamut from being independent of the perturbation to being completely dominated by it, depending on the complexity mismatch.

Networks of Echoes: Imitation, Innovation and Invisible Leaders is a mathematically rigorous and data rich book on a fascinating area of the science and engineering of social webs. There are hundreds of complex network phenomena whose statistical properties are described by inverse power laws. The phenomena of interest are not arcane events that we encounter only fleetingly, but are events that dominate our lives. We examine how this intermittent statistical behavior intertwines itself with what appears to be the organized activity of social groups. The book is structured as answers to a sequence of questions such as: How are decisions reached in elections and boardrooms? How is the stability of a society undermined by zealots and committed minorities and how is that stability re-established? Can we learn to answer such questions about human behavior by studying the way flocks of birds retain their formation when eluding a predator? These questions and others are answered using a generic model of a complex dynamic network—one whose global behavior is determined by a symmetric interaction among individuals based on social imitation. The complexity of the network is manifest in time series resulting from self-organized critical dynamics that have divergent first and second moments, are non-stationary, non-ergodic and non-Poisson. How phase transitions in the network dynamics influence such activity as decision making is a fascinating story and provides a context for introducing many of the mathematical ideas necessary for understanding complex networks in general. The decision making model (DMM) is selected to emphasize that there are features of complex webs that supersede specific mechanisms and need to be understood from a general perspective. This insightful overview of recent tools and their uses may serve as an introduction and curriculum guide in related courses.

A nonsimple (complex) system indicates a mix of crucial and non-crucial events, with very different statistical properties. It is the crucial events that determine the efficiency of information exchange between complex networks. For a large class of nonsimple systems, crucial events determine catastrophic failures - from heart attacks to stock market crashes.This interesting book outlines a data processing technique that separates the effects of the crucial from those of the non-crucial events in nonsimple time series extracted from physical, social and living systems. Adopting an informal conversational style, without sacrificing the clarity necessary to explain, the contents will lead the reader through concepts such as fractals, complexity and randomness, self-organized criticality, fractional-order differential equations of motion, and crucial events, always with an eye to helping to interpret what mathematics usually does in the development of new scientific knowledge.Both researchers and novitiate will find Crucial Events useful in learning more about the science of nonsimplicity.

To many scientists the gap between the nineteenth century views of consciousness proposed by the psychologist William James and that developed by the inventor of psychophysics Gustav Fechner has never seemed wider. However the twentieth century concept of collective/cooperative behavior within the brain has partially reconciled these diverging perspectives suggesting the notion of consciousness as a physical phenomenon. A kernel of twenty-first century investigators bases their investigations on physiological fluctuations experiments. These fluctuations, although apparently erratic, when analyzed with advanced methods of fractal statistical analysis reveal the emergence of complex behavior, intermediate between complete order and total randomness, a property usually referred to as temporal complexity. Others, with the help of modern technologies, such MRI, establish a more direct analysis of brain dynamics, and focus on the brain’s topological complexity. Consequently the two groups adopt different approaches, the former being based on phenomenological and macroscopic considerations, and the latter resting on the crucial role of neuron interactions. The neurophysiology research work has an increasing overlap with the emerging field of complex networks, whereas the behavior psychology experiments have until recently ignored the complex cooperative dynamics that are proved by increasing experimental evidence to characterize the brain function. It is crucial to examine both the experimental and theoretical studies that support and those that challenge the view that it is an emergent collective property that allows the healthy brain to function. What needs to be discussed are new ways to understand the transport of information through complex networks sharing the same dynamical properties as the brain. In addition we need to understand information transfer between complex networks, say between the brain and a controlled experimental stimulus. Experiments suggest that brain excitation is described by inverse power-law distributions and recent studies in network dynamics indicate that this distribution is the result of phase transitions due to neuron network dynamics. It is important to stress that the development of dynamic networking establishes a connection between topological and temporal complexity, establishing that a scale-free distribution of links is generated by the dynamic correlation between dynamic elements located at very large Euclidean distances from one another. Dynamic networking and dynamics networks suggest a new way to transfer information: the long-distance communication through local cooperative interaction. It is anticipated that the contributed discussions will clarify how the global intelligence of a complex network emerges from the local cooperation of units and the role played by critical phase transitions in the observed persistence of this cooperation.

This exceptional book is concerned with the application of fractals and chaos, as well as other concepts from nonlinear dynamics to biomedical phenomena. Herein we seek to communicate the excitement being experienced by scientists upon making application of these concepts within the life sciences. Mathematical concepts are introduced using biomedical data sets and the phenomena being explained take precedence over the mathematics.In this new edition what has withstood the test of time has been updated and modernized; speculations that were not borne out have been expunged and the breakthroughs that have occurred in the intervening years are emphasized. The book provides a comprehensive overview of a nascent theory of medicine, including a new chapter on the theory of complex networks as they pertain to medicine.

The Routledge Handbook of Classics and Cognitive Theory is an interdisciplinary volume that examines the application of cognitive theory to the study of the classical world, across several interrelated areas including linguistics, literary theory, social practices, performance, artificial intelligence and archaeology. With contributions from a diverse group of international scholars working in this exciting new area, the volume explores the processes of the mind drawing from research in psychology, philosophy, neuroscience, and anthropology, and interrogates the implications of these new approaches for the study of the ancient world. Topics covered in this wide-ranging collection include: cognitive linguistics applied to Homeric and early Greek texts, Roman cultural semantics, linguistic embodiment in Latin literature, group identities in Greek lyric, cognitive dissonance in historiography, kinesthetic empathy in Sappho, artificial intelligence in Hesiod and Greek drama, the enactivism of Roman statues and memory and art in the Roman Empire. This ground-breaking work is the first to organize the field, allowing both scholars and students access to the methodologies, bibliographies and techniques of the cognitive sciences and how they have been applied to classics.