What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of neurons produce something as extraordinarily complex as consciousness? In this remarkably clear and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time.

Melanie Mitchell's book is most enjoyable, truly inspiring, skillfully written, and, above all, beautifully clear. The author's enthusiasm and passion for the field make the book fascinating to read. Her rigor, clarity, and healthy skepticism make the book sound and the field scientifically stronger. It is an excellent and rigorous account of the scientific field of complexity. She proves by example that it is possible to explain complex systems science with rigor, breadth, depth, and- above all-exquisite clarity.

Autorentext

Melanie Mitchell is Professor of Computer Science at Portland State University and External Professor at the Santa Fe Institute.

Klappentext

A fascinating look at the exciting new sciences of complexity and what they reveal about everything from ant colonies to the World Wide Web, now available in paperback

Inhalt

Preface Acknowledgments Part I: Background and History Chapter 1: What is Complexity? Chapter 2: Dynamics, Chaos, and Prediction Chapter 3: Information Chapter 4: Computation Chapter 5: Evolution Chapter 6: Genetics, Simplified Chapter 7: Defining and Measuring Complexity Part II: Life and Evolution in Computers Chapter 8: Self-Reproducing Programs Chapter 9: Genetic Algorithms Part III: Computation Writ Large Chapter 10: Cellular Automata, Life, and the Universe Chapter 11: Computing with Particles Chapter 12: Information Processing in Living Systems Chapter 13: How to Make Analogies (If You Are A Computer) Chapter 14: Prospects of Computer Modeling Part IV: Network Thinking Chapter 15: The Science of Networks Chapter 16: Applying Network Science to Real-World Networks Chapter 17: The Mystery of Scaling Chapter 18: Evolution, Complexified Part V: Conclusion Chapter 19: The Past and Future of the Sciences of Complexity Notes Bibliography Index