Think Complexity

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. 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 worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

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

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

The underlying idea and motive for the book is that the notion of complexity may humanize the social sciences, may conceive the complex human being as more human, and turn reality as assumed in our doing social science into a more complex, that is a richer reality for all. The main focus of this book is on new thinking in complexity, with complexity to be taken as derived from the Latin word complexus: ‘that which is interwoven.’ The trans-disciplinary approach advocated here will be trans-disciplinary in two ways: firstly, by going beyond the separate disciplines within the fields of both natural sciences and social sciences, and, secondly, by going beyond the separate cultures of the natural sciences and of the social sciences and humanities.

“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

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. 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 worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Systems Thinking, Third Edition combines systems theory and interactive design to provide an operational methodology for defining problems and designing solutions in an environment increasingly characterized by chaos and complexity. This new edition has been updated to include all new chapters on self-organizing systems as well as holistic, operational, and design thinking. The book covers recent crises in financial systems and job markets, the housing bubble, and environment, assessing their impact on systems thinking. A companion website is available at interactdesign.com. This volume is ideal for senior executives as well as for chief information/operating officers and other executives charged with systems management and process improvement. It may also be a helpful resource for IT/MBA students and academics. Four NEW chapters on self-organizing systems, holistic thinking, operational thinking, and design thinking Covers the recent crises in financial systems and job markets globally, the housing bubble, and the environment, assessing their impact on systems thinking Companion website to accompany the book is available at interactdesign.com

The book describes what it means to say the world is complex and explores what that means for managers, policy makers and individuals. The first part of the book is about the theory and ideas of complexity. This is explained in a way that is thorough but not mathematical. It compares differing approaches, and also provides a historical perspective, showing how such thinking has been around since the beginning of civilisation. It emphasises the difference between a complexity worldview and the dominant mechanical worldview that underpins much of current management practice. It defines the complexity worldview as recognising the world is interconnected, shaped by history and the particularities of context. The comparison of the differing approaches to modelling complexity is unique in its depth and accessibility. The second part of the book uses this lens of complexity to explore issues in the fields of management, strategy, economics, and international development. It also explores how to facilitate others to recognise the implications of adopting a complex rather than a mechanical worldview and suggests methods of research to explore systemic, path-dependent emergent aspects of situations. The authors of this book span both science and management, academia and practice, thus the explanations of science are authoritative and yet the examples of changing how you live and work in the world are real and accessible. The aim of the book is to bring alive what complexity is all about and to illustrate the importance of loosening the grip of a modernist worldview with its hope for prediction, certainty and control.