“If you liked Chaos, you’ll love Complexity. Waldrop creates the most exciting intellectual adventure story of the year” (The Washington Post). In a rarified world of scientific research, a revolution has been brewing. Its activists are not anarchists, but rather Nobel Laureates in physics and economics and pony-tailed graduates, mathematicians, and computer scientists from all over the world. They have formed an iconoclastic think-tank and their radical idea is to create a new science: complexity. They want to know how a primordial soup of simple molecules managed to turn itself into the first living cell—and what the origin of life some four billion years ago can tell us about the process of technological innovation today. This book is their story—the story of how they have tried to forge what they like to call the science of the twenty-first century. “Lucidly shows physicists, biologists, computer scientists and economists swapping metaphors and reveling in the sense that epochal discoveries are just around the corner . . . [Waldrop] has a special talent for relaying the exhilaration of moments of intellectual insight.” —The New York Times Book Review “Where I enjoyed the book was when it dove into the actual question of complexity, talking about complex systems in economics, biology, genetics, computer modeling, and so on. Snippets of rare beauty here and there almost took your breath away.” —Medium “[Waldrop] provides a good grounding of what may indeed be the first flowering of a new science.” —Publishers Weekly

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for stochastic systems with traditional and emerging engineering-oriented complexities. The book begins with an overview of the relevant background, motivation, and research problems, and then: Discusses the robust stability and stabilization problems for a class of stochastic time-delay interval systems with nonlinear disturbances Investigates the robust stabilization and H∞ control problems for a class of stochastic time-delay uncertain systems with Markovian switching and nonlinear disturbances Explores the H∞ state estimator and H∞ output feedback controller design issues for stochastic time-delay systems with nonlinear disturbances, sensor nonlinearities, and Markovian jumping parameters Analyzes the H∞ performance for a general class of nonlinear stochastic systems with time delays, where the addressed systems are described by general stochastic functional differential equations Studies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing measurement and stochastic disturbances Uses gain-scheduling techniques to tackle the probability-dependent control and filtering problems for time-varying nonlinear systems with incomplete information Evaluates the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with multiple random communication delays and random packet losses Examines the filtering problem for a class of nonlinear genetic regulatory networks with state-dependent stochastic disturbances and state delays Considers the H∞ state estimation problem for a class of discrete-time complex networks with probabilistic missing measurements and randomly occurring coupling delays Addresses the H∞ synchronization control problem for a class of dynamical networks with randomly varying nonlinearities Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities describes novel methodologies that can be applied extensively in lab simulations, field experiments, and real-world engineering practices. Thus, this text provides a valuable reference for researchers and professionals in the signal processing and control engineering communities.

Although much recent social science and humanities work has been a revolt against simplification, this volume explores the contrast between simplicity and complexity to reveal that this dichotomy, itself, is too simplistic. John Law and Annemarie Mol have gathered a distinguished panel of contributors to offer—particularly within the field of science studies—approaches to a theory of complexity, and at the same time a theoretical introduction to the topic. Indeed, they examine not only ways of relating to complexity but complexity in practice. Individual essays study complexity from a variety of perspectives, addressing market behavior, medical interventions, aeronautical design, the governing of supranational states, ecology, roadbuilding, meteorology, the science of complexity itself, and the psychology of childhood trauma. Other topics include complex wholes (holism) in the sciences, moral complexity in seemingly amoral endeavors, and issues relating to the protection of African elephants. With a focus on such concepts as multiplicity, partial connections, and ebbs and flows, the collection includes narratives from Kenya, Great Britain, Papua New Guinea, the Netherlands, France, and the meetings of the European Commission, written by anthropologists, economists, philosophers, psychologists, sociologists, and scholars of science, technology, and society. Contributors. Andrew Barry, Steven D. Brown, Michel Callon, Chunglin Kwa, John Law, Nick Lee, Annemarie Mol, Marilyn Strathern, Laurent Thévenot, Charis Thompson

Here at last is the definitive book on how to make a pop-up. Every aspect of the creation of a pop-up, known as paper engineering, is clearly and thoroughly covered. All types of parallel folds, angle folds, wheels, and pull tabs are accurately detailed verbally and visually, flat and in dimension. Also included is a history of pop-ups and a step-by-step photographic essay on how a pop-up is made from start to finish. This guided tour is perfect for aspiring pop-up creators, paper engineers, students, and appreciators of this unique art form.

Awareness of complexity in science and technology dates back to the 1970s. However, all social systems tend to develop structures that become more complex over time, be it within families, tribes, cities, states, or societal and economic organizations. Complexities 2 covers a broad array of fields, from justice and linguistics to education and organizational management. The aim of this book is to show, without aiming to provide a comprehensive overview, the diversity of approaches and behaviors towards the obstacle of complexity in understanding and achieving human actions. When we see complexity as the incompleteness of knowledge and the uncertainty of the future, we realize that simplifying is not an adequate approach to complexity, even in the humanities and social sciences. This book explores the relationship between order and disorder in this field of knowledge.

Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).

Managing Complexity is the first book that clearly defines the concept of Complexity, explains how Complexity can be measured and tuned, and describes the seven key features of Complex Systems: ConnectivityAutonomyEmergencyNonequilibriumNon-linearitySelf-organisationCo-evolution The thesis of the book is that complexity of the environment in which we work and live offers new opportunities and that the best strategy for surviving and prospering under conditions of complexity is to develop adaptability to perpetually changing conditions. An effective method for designing adaptability into business processes using multi-agent technology is presented and illustrated by several extensive examples, including adaptive, real-time scheduling of taxis, see-going tankers, road transport, supply chains, railway trains, production processes and swarms of small space satellites. Additional case studies include adaptive servicing of the International Space Station; adaptive processing of design changes of large structures such as wings of the largest airliner in the world; dynamic data mining, knowledge discovery and distributed semantic processing. Finally, the book provides a foretaste of the next generation of complex issues, notably, The Internet of Things, Smart Cities, Digital Enterprises and Smart Logistics.