Complexity

“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.

Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.

By virtue of the close relationship between logic and relational databases, it turns out that complexity has important applications to databases such as analyzing the parallel time needed to compute a query, and the analysis of nondeterministic classes. This book is a relatively self-contained introduction to the subject, which includes the necessary background material, as well as numerous examples and exercises.

A complete treatment of fundamentals and recent advances in complexity theory Complexity theory studies the inherent difficulties of solving algorithmic problems by digital computers. This comprehensive work discusses the major topics in complexity theory, including fundamental topics as well as recent breakthroughs not previously available in book form. Theory of Computational Complexity offers a thorough presentation of the fundamentals of complexity theory, including NP-completeness theory, the polynomial-time hierarchy, relativization, and the application to cryptography. It also examines the theory of nonuniform computational complexity, including the computational models of decision trees and Boolean circuits, and the notion of polynomial-time isomorphism. The theory of probabilistic complexity, which studies complexity issues related to randomized computation as well as interactive proof systems and probabilistically checkable proofs, is also covered. Extraordinary in both its breadth and depth, this volume: * Provides complete proofs of recent breakthroughs in complexity theory * Presents results in well-defined form with complete proofs and numerous exercises * Includes scores of graphs and figures to clarify difficult material An invaluable resource for researchers as well as an important guide for graduate and advanced undergraduate students, Theory of Computational Complexity is destined to become the standard reference in the field.

How can you turn dry statistics into attractive and informative graphs? How can you present complex data sets in an easily understandable way? How can you create narrative diagrams from unstructured data? This handbook of information design answers these questions. Nicole Lachenmeier and Darjan Hil condense their extensive professional experience into an illustrated guide that offers a modular design system comprised of 80 elements. Their systematic design methodology makes it possible for anyone to visualize complex data attractively and using different perspectives. At the intersection of design, journalism, communication and data science, Visualizing Complexity opens up new ways of working with abstract data and invites readers to try their hands at information design.

This research monograph presents an inter-disciplinary study of the impact, and current status, of applications of complexity-related concepts in the early stages of development of Agile Project Management (APM). The results serve as an introduction for exploring more profound relations between complexity-related ideas and APM in the future. The increasing complexity of software projects and their environment in the 1990s constituted the main determinants of the development of the family of methodological frameworks called Agile Project Management. Development of APM has been shaped by a broadly defined area of research called complexity science or complexity theory based on complex adaptive systems (CAS) and on their characteristics: Complexity, chaos, the edge of chaos, emerging properties, non-linearity, self-organization, etc. In the 21st century, due to the expansion of Agile beyond software development, the challenges deriving from the complexity of projects and the environment are even more urgent or compelling. Such phenomena demand more profound inter- and multi-disciplinary studies. This book examines the impact of applications of complexity-related ideas deriving from intuitive complexity and from complexity science in the early stages of development of the Agile methodological frameworks in project management and considers the current status of those applications. It questions the usefulness of those applications for the practice and theory of APM, and then proposes a conceptual framework for further theoretical studies and several ways of improvement and refinement of the Agile Project Management necessary to deal with broadly defined complexity in project management. Requiring a medium-level knowledge of complexity studies and knowledge of project management, this book is written for the research community studying the links between the various methodological frameworks included in APM and complexity-related ideas. It will also be interesting for studies of the impact of complexity on modern management, and for master’s students on IT and management courses.