Complexity Science: An Introduction
Complexity Science: An Introduction

Author: Mark A Peletier

Publisher: World Scientific

Published: 2019-03-20

Total Pages: 428

ISBN-13: 9813239611

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This book on complexity science comprises a collection of chapters on methods and principles from a wide variety of disciplinary fields — from physics and chemistry to biology and the social sciences.In this two-part volume, the first part is a collection of chapters introducing different aspects in a coherent fashion, and providing a common basis and the founding principles of the different complexity science approaches; the next provides deeper discussions of the different methods of use in complexity science, with interesting illustrative applications.The fundamental topics deal with self-organization, pattern formation, forecasting uncertainties, synchronization and revolutionary change, self-adapting and self-correcting systems, and complex networks. Examples are taken from biology, chemistry, engineering, epidemiology, robotics, economics, sociology, and neurology.

Complexity
Complexity

Author: John Henry Holland

Publisher: Oxford University Press, USA

Published: 2014

Total Pages: 121

ISBN-13: 0199662541

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In this very short introduction, John Holland presents an introduction to the science of complexity. Using examples from biology and economics, he shows how complexity science models the behaviour of complex systems.

Think Complexity
Think Complexity

Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

Published: 2012-02-23

Total Pages: 159

ISBN-13: 1449331696

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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

Dealing with Complexity
Dealing with Complexity

Author: Robert L. Flood

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 291

ISBN-13: 1475722354

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Contents 11. 2. 2. Four Main Areas of Dispute 247 11. 2. 3. Summary . . . 248 11. 3. Making Sense of the Issues . . 248 11. 3. 1. Introduction . . . . 248 11. 3. 2. The Scientific Approach 248 11. 3. 3. Science and Matters of Society . 249 11. 3. 4. Summary . 251 11. 4. Tying It All Together . . . . 251 11. 4. 1. Introduction . . . . 251 11. 4. 2. A Unifying Framework 251 11. 4. 3. Critical Systems Thinking 253 11. 4. 4. Summary 254 11. 5. Conclusion 254 Questions . . . 255 REFERENCES . . . . . . . . . . . . . . . . . . . 257 INDEX . . . . . . . . . . . . . . . . . . . . . . 267 Chapter One SYSTEMS Origin and Evolution, Terms and Concepts 1. 1. INTRODUCTION We start this book with Theme A (see Figure P. I in the Preface), which aims to develop an essential and fundamental understanding of systems science. So, what is systems science? When asked to explain what systems science is all about, many systems scientists are confronted with a rather daunting task. The discipline tends to be presented and understood in a fragmented way and very few people hold an overview understanding of the subject matter, while also having sufficient in-depth competence in many and broad-ranging subject areas where the ideas are used. Indeed, it was precisely this difficulty that identified the need for a comprehensive well-documented account such as is presented here in Dealing with Complexity.

An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications

Author: Ming Li

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 655

ISBN-13: 1475726066

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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).

Complexity Theory and the Social Sciences
Complexity Theory and the Social Sciences

Author: David Byrne

Publisher: Routledge

Published: 2002-01-04

Total Pages: 215

ISBN-13: 1134714742

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Chaos and complexity are the new buzz words in both science and contemporary society. The ideas they represent have enormous implications for the way we understand and engage with the world. Complexity Theory and the Social Sciences introduces students to the central ideas which surround the chaos/complexity theories. It discusses key concepts before using them as a way of investigating the nature of social research. By applying them to such familiar topics as urban studies, education and health, David Byrne allows readers new to the subject to appreciate the contribution which complexity theory can make to social research and to illuminating the crucial social issues of our day.

Complexity
Complexity

Author: M. Mitchell Waldrop

Publisher: Open Road Media

Published: 2019-10-01

Total Pages: 492

ISBN-13: 150405914X

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“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

New Software Engineering Paradigm Based on Complexity Science
New Software Engineering Paradigm Based on Complexity Science

Author: Jay Xiong

Publisher: Springer Science & Business Media

Published: 2011-02-14

Total Pages: 769

ISBN-13: 1441973265

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This book describes a complete revolution in software engineering based on complexity science through the establishment of NSE – Nonlinear Software Engineering paradigm which complies with the essential principles of complexity science, including the Nonlinearity principle, the Holism principle, the Complexity Arises From Simple Rules principle, the Initial Condition Sensitivity principle, the Sensitivity to Change principle, the Dynamics principle, the Openness principle, the Self-organization principle, and the Self-adaptation principle. The aims of this book are to offer revolutionary solutions to solve the critical problems existing with the old-established software engineering paradigm based on linear thinking and simplistic science complied with the superposition principle, and make it possible tohelp software development organizations double their productivity, halve their cost, and remove 99% to 99.99% of the defects in their software products, and efficiently handle software complexity, conformity, visibility, and changeability. It covers almost all areas in software engineering. The tools NSE_CLICK- an automatic acceptance testing platform for outsourcing (or internally developed) C/C++ products, and NSE_CLICK_J - an automatic acceptance testing platform for outsourcing (or internally developed) Java products are particularly designed for non-technical readers to view/review how the acceptance testing of a software product developed with NSE can be performed automatically, and how the product developed with NSE is truly maintainable at the customer site.

Introduction to the Theory of Complexity
Introduction to the Theory of Complexity

Author: Daniel Pierre Bovet

Publisher: Prentice Hall PTR

Published: 1994

Total Pages: 304

ISBN-13:

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Using a balanced approach that is partly algorithmic and partly structuralist, this book systematically reviews the most significant results obtained in the study of computational complexity theory. Features over 120 worked examples, over 200 problems, and 400 figures.

Sociology and Complexity Science
Sociology and Complexity Science

Author: Brian Castellani

Publisher: Springer Science & Business Media

Published: 2009-10-03

Total Pages: 292

ISBN-13: 3540884629

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By now, most academics have heard something about the new science of complexity. In a manner reminiscent of Einstein and the last hundred years of physics, complexity science has captured the public imagination. ® One can go to Amazon. com and purchase books on complexification (Casti 1994), emergence (Holland 1998), small worlds (Barabási 2003), the web of life (Capra 1996), fuzzy thinking (Kosko 1993), global c- plexity (Urry 2003) and the business of long-tails (Anderson 2006). Even television has incorporated the topics of complexity science. Crime shows ® ® such as 24 or CSI typically feature investigators using the latest advances in computational modeling to “simulate scenarios” or “data mine” all p- sible suspects—all of which is done before the crime takes place. The ® World Wide Web is another example. A simple search on Google. Com using the phrase “complexity science” gets close to a million hits! C- plexity science is ubiquitous. What most scholars do not realize, however, is the remarkable role sociologists are playing in this new science. C- sider the following examples. 0. 1 Sociologists in Complexity Science The first example comes from the new science of networks (Barabási 2003). By now, most readers are familiar with the phenomena known as six-degrees of separation—the idea that, because most large networks are comprised of a significant number of non-random weak-ties, the nodes (e. g. , people, companies, etc.