“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

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).

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.

"Put together one of the world's best science writers with one of the universe's most fascinating subjects and you are bound to produce a wonderful book. . . . The subject of complexity is vital and controversial. This book is important and beautifully done."—Stephen Jay Gould "[Complexity] is that curious mix of complication and organization that we find throughout the natural and human worlds: the workings of a cell, the structure of the brain, the behavior of the stock market, the shifts of political power. . . . It is time science . . . thinks about meaning as well as counting information. . . . This is the core of the complexity manifesto. Read it, think about it . . . but don't ignore it."—Ian Stewart, Nature This second edition has been brought up to date with an essay entitled "On the Edge in the Business World" and an interview with John Holland, author of Emergence: From Chaos to Order.

There is a big difference between assigning complex texts and teaching complex texts No matter what discipline you teach, learn how to use complexity as a dynamic, powerful tool for sliding the right text in front of your students’ at just the right time. Updates to this new edition include How-to’s for measuring countable features of any written work A rubric for analyzing the complexity of both literary and informational texts Classroom scenarios that show the difference between a healthy struggle and frustration The authors’ latest thinking on teacher modeling, close reading, scaffolded small group reading, and independent reading

Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. 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 tables Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get starter code and solutions to help you re-implement and extend original experiments in complexity Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics Examine case studies of complex systems submitted by students and readers

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.