He believes that we should investigate what makes some countries more capable than others. Complex products—from films to robots, apps to automobiles—are a physical distillation of an economy’s knowledge, a measurable embodiment of its education, infrastructure, and capability. Economic wealth accrues when applications of this knowledge turn ideas into tangible products; the more complex its products, the more economic growth a country will experience.A radical new interpretation of global economics, Why Information Grows overturns traditional assumptions about the development of economies and the origins of wealth and takes a crucial step toward making economics less the dismal science and more the insightful one.

    In the past, discoveries often had to wait for the rise of the next generation to see questions in a new light and let go of old truisms. Today, in a world that is defined by a rapid rate of change, staying on the cutting edge has as much to do with shedding outdated notions as adopting new ones. In this spirit, John Brockman, publisher of the online salon Edge.org ("the world's smartest website"—The Guardian), asked 175 of the world's most influential scientists, economists, artists, and philosophers: What scientific idea is ready for retirement?Jared Diamond explores the diverse ways that new ideas emerge * Nassim Nicholas Taleb takes down the standard deviation * Richard Thaler and novelist Ian McEwan reveal the usefulness of "bad" ideas * Steven Pinker dismantles the working theory of human behavior * Richard Dawkins renounces essentialism * Sherry Turkle reevaluates our expectations of artificial intelligence * Physicist Andrei Linde suggests that our universe and its laws may not be as unique as we think * Martin Rees explains why scientific understanding is a limitless goal * Alan Guth rethinks the origins of the universe * Sam Harris argues that our definition of science is too narrow * Nobel Prize winner Frank Wilczek disputes the division between mind and matter * Lawrence Krauss challenges the notion that the laws of physics were preordained * plus contributions from Daniel Goleman, Mihaly Csikszentmihalyi, Nicholas Carr, Rebecca Newberger Goldstein, Matt Ridley, Stewart Brand, Sean Carroll, Daniel C. Dennett, Helen Fisher, Douglas Rushkoff, Lee Smolin, Kevin Kelly, Freeman Dyson, and others.

    He shows that the scientists of ancient and medieval times not only did not understand what we understand about the world—they did not understand what there is to understand, or how to understand it. Yet over the centuries, through the struggle to solve such mysteries as the curious backward movement of the planets and the rise and fall of the tides, the modern discipline of science eventually emerged. Along the way, Weinberg examines historic clashes and collaborations between science and the competing spheres of religion, technology, poetry, mathematics, and philosophy.An illuminating exploration of the way we consider and analyze the world around us, To Explain the World is a sweeping, ambitious account of how difficult it was to discover the goals and methods of modern science, and the impact of this discovery on human knowledge and development.

    Widely praised and much-discussed, this classic book explores how cooperation can emerge in a world of self-seeking egoists—whether superpowers, businesses, or individuals—when there is no central authority to police their actions. The problem of cooperation is central to many different fields. Robert Axelrod recounts the famous computer tournaments in which the “cooperative” program Tit for Tat recorded its stunning victories, explains its application to a broad spectrum of subjects, and suggests how readers can both apply cooperative principles to their own lives and teach cooperative principles to others.

    Hardcover published by Quadragle/The New York Times Book Co., third printing, August 1977, copyright 1975.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function.This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.

    Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter -- on a self-contained page -- corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

    In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.

    Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tablesStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet starter code and solutions to help you re-implement and extend original experiments in complexityExplore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topicsExamine case studies of complex systems submitted by students and readers

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Gordon strips engineering of its confusing technical terms, communicating its founding principles in accessible, witty prose.For anyone who has ever wondered why suspension bridges don't collapse under eight lanes of traffic, how dams hold back--or give way under--thousands of gallons of water, or what principles guide the design of a skyscraper, a bias-cut dress, or a kangaroo, this book will ease your anxiety and answer your questions.Structures: Or Why Things Don't Fall Down is an informal explanation of the basic forces that hold together the ordinary and essential things of this world--from buildings and bodies to flying aircraft and eggshells. In a style that combines wit, a masterful command of his subject, and an encyclopedic range of reference, Gordon includes such chapters as "How to Design a Worm" and "The Advantage of Being a Beam," offering humorous insights in human and natural creation.Architects and engineers will appreciate the clear and cogent explanations of the concepts of stress, shear, torsion, fracture, and compression. If you're building a house, a sailboat, or a catapult, here is a handy tool for understanding the mechanics of joinery, floors, ceilings, hulls, masts--or flying buttresses.Without jargon or oversimplification, Structures opens up the marvels of technology to anyone interested in the foundations of our everyday lives.

    They are vehicles, a series of hypothetical, self-operating machines that exhibit increasingly intricate if not always successful or civilized behavior. Each of the vehicles in the series incorporates the essential features of all the earlier models and along the way they come to embody aggression, love, logic, manifestations of foresight, concept formation, creative thinking, personality, and free will. In a section of extensive biological notes, Braitenberg locates many elements of his fantasy in current brain research.

    It's about states, armies and corporations. It's about orders from on high. Even history "from below" is often about trade unions and workers' parties. But what if that's simply because hierarchical institutions create the archives that historians rely on? What if we are missing the informal, less well documented social networks that are the true sources of power and drivers of change?The 21st century has been hailed as the Age of Networks. However, in The Square and the Tower, Niall Ferguson argues that networks have always been with us, from the structure of the brain to the food chain, from the family tree to freemasonry. Throughout history, hierarchies housed in high towers have claimed to rule, but often real power has resided in the networks in the town square below. For it is networks that tend to innovate. And it is through networks that revolutionary ideas can contagiously spread. Just because conspiracy theorists like to fantasize about such networks doesn't mean they are not real.From the cults of ancient Rome to the dynasties of the Renaissance, from the founding fathers to Facebook, The Square and the Tower tells the story of the rise, fall and rise of networks, and shows how network theory--concepts such as clustering, degrees of separation, weak ties, contagions and phase transitions--can transform our understanding of both the past and the present.Just as The Ascent of Money put Wall Street into historical perspective, so The Square and the Tower does the same for Silicon Valley. And it offers a bold prediction about which hierarchies will withstand this latest wave of network disruption--and which will be toppled.