This book introduces a refreshing approach to twenty-first-century scientific approach in an age, which is also known as the Century of Complexity. It deals with the deep problem of complexity, being operative from the bottom-up. The current lack of understanding of complexity has led scholars into the so-called embarrassment of complexity. A long overdue paradigm shift is necessary to address complexity as generative complexity and brings readers to the edge of a scientific revolution: that is, a generative revolution in the Century of Complexity. The book offers a radical shift of paradigm from the paradigm of simplifying into the new generative paradigm of complexifying about processes that develop from the bottom-up. The book links complex generative reality with a corresponding radical new generative nature of order and explores new fronts in science. This book explores innovative concepts of interaction, of causality, of the unit of study, and of reality itself and enables readers to see complexity as generative, emergent complexity as being operative from the bottom-up. The book discusses and suggests solutions for the problem of complexity in this Century of Complexity. The author provides a new understanding of complexity based on a generative flux of forces and relations. The book aims to bring about a fundamental and foundational change in how we view and ‘do’ science for an interdisciplinary audience of academics ranging from social science and humanities to economy and biology.

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

The underlying idea and motive for the book is that the notion of complexity may humanize the social sciences, may conceive the complex human being as more human, and turn reality as assumed in our doing social science into a more complex, that is a richer reality for all. The main focus of this book is on new thinking in complexity, with complexity to be taken as derived from the Latin word complexus: ‘that which is interwoven.’ The trans-disciplinary approach advocated here will be trans-disciplinary in two ways: firstly, by going beyond the separate disciplines within the fields of both natural sciences and social sciences, and, secondly, by going beyond the separate cultures of the natural sciences and of the social sciences and humanities.

This book capitalizes on the developments in dynamical systems and education by presenting some of the most recent advances in this area in seventeen non-overlapping chapters. The first half of the book discusses the conceptual framework of complex dynamical systems and its applicability to educational processes. The second half presents a set of empirical studies that that illustrate the use of various research methodologies to investigate complex dynamical processes in education, and help the reader appreciate what we learn about dynamical processes in education from using these approaches.

With the advent of electronic databases, information technologies, and the Internet, organizations now more than ever have easy access to all the knowledge they need to conduct their affairs. Identifying the useful information in all that data, however, can pose a challenge. Knowledge Discovery, Transfer, and Management in the Information Age brings together the latest empirical research in knowledge management practices and information retrieval strategies to assist organizations in effectively and efficiently utilizing the data at their disposal. Academics, managers, researchers, and professionals within the field of knowledge management will make use of this book to increase their understanding of best practices in the manipulation of information resources.

Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.

Generative Emergence provides insight into the non-linear dynamics that lead to organizational emergence through the use of complexity sciences. The book explores how the model of Generative Emergence could be applied to enact emergence within and across organizations.

Predictive coding (PC) is a neurocognitive concept, according to which the brain does not process the whole qualia of external information, but only residual mismatches occurring between incoming information and an individual, inner model of the world. At the time of issue initiation, I expected an essential focus on mismatch signals in the brain, especially those captured by neurophysiologic oscillations. This was because one most plausible approach to the PC concept is to identify and validate mismatch signals in the brain. Announcing the topic revealed a much deeper consideration of intelligible minds of researchers. It turned out that what was of fundamental interest was which brain mechanisms support the formation, maintenance and consolidation of the inner model determining PC. Is PC a dynamic construct continuously modulated by external environmental or internal mental information? The reader will be delighted to get acquainted with the current views and understanding of eminent scholars in the field. It will be challenging to discover the realm of sleep where both physiological, energy preserving and mental qualia principles build on the inner models to shape and transform the self. And where neurophysiologic oscillations may both transmit external information and translate inner models from state to state to preserve the self-continuity and compactness.