By Melanie Mitchell
What permits separately uncomplicated bugs like ants to behave with such precision and function as a bunch? How do trillions of neurons produce anything as terribly complicated as cognizance? during this remarkably transparent and companionable e-book, best advanced structures scientist Melanie Mitchell offers an intimate journey of the sciences of complexity, a huge set of efforts that search to provide an explanation for how large-scale advanced, prepared, and adaptive habit can emerge from basic interactions between myriad members. in keeping with her paintings on the Santa Fe Institute and drawing on its interdisciplinary concepts, Mitchell brings readability to the workings of complexity throughout a huge diversity of organic, technological, and social phenomena, searching out the overall rules or legislation that practice to them all. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa publication Award in Science--offers a wide-ranging assessment of the guidelines underlying advanced platforms technology, the present learn on the leading edge of this box, and the customers for its contribution to fixing one of the most vital clinical questions of our time.
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Extra resources for Complexity
Cm. Includes bibliographical references and index. ISBN 978-0-19-512441-5 1. Complexity (Philosophy) I. Title. C65M58 2009 501—dc22 2008023794 9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper To Douglas Hofstadter and John Holland CONTENTS Preface Acknowledgments PART ONE Background and History CHAPTER ONE What Is Complexity? CHAPTER TWO Dynamics, Chaos, and Prediction CHAPTER THREE Information CHAPTER FOUR Computation CHAPTER FIVE Evolution CHAPTER SIX Genetics, Simplified CHAPTER SEVEN Defining and Measuring Complexity PART TWO Life and Evolution in Computers CHAPTER EIGHT Self-Reproducing Computer Programs CHAPTER NINE Genetic Algorithms PART THREE Computation Writ Large CHAPTER TEN Cellular Automata, Life, and the Universe CHAPTER ELEVEN Computing with Particles CHAPTER TWELVE Information Processing in Living Systems CHAPTER THIRTEEN How to Make Analogies (if You Are a Computer) CHAPTER FOURTEEN Prospects of Computer Modeling PART FOUR Network Thinking CHAPTER FIFTEEN The Science of Networks CHAPTER SIXTEEN Applying Network Science to Real-World Networks CHAPTER SEVENTEEN The Mystery of Scaling CHAPTER EIGHTEEN Evolution, Complexified PART FIVE Conclusion CHAPTER NINETEEN The Past and Future of the Sciences of Complexity Notes Bibliography Index PREFACE REDUCTIONISM is the most natural thing in the world to grasp.
These days, the existence of chaotic systems is an accepted fact of science. It is hard to pin down who first realized that such systems might exist. The possibility of sensitive dependence on initial conditions was proposed by a number of people long before quantum mechanics was invented. ” Possibly the first clear example of a chaotic system was given in the late nineteenth century by the French mathematician Henri Poincaré. Poincaré was the founder of and probably the most influential contributor to the modern field of dynamical systems theory, which is a major outgrowth of Newton’s science of dynamics.
However, effects of Heisenberg’s principle exist only in the quantum world of tiny particles, and most people viewed it as an interesting curiosity, but not one that would have much implication for prediction at a larger scale—predicting the weather, say. It was the understanding of chaos that eventually laid to rest the hope of perfect prediction of all complex systems, quantum or otherwise. The defining idea of chaos is that there are some systems—chaotic systems—in which even minuscule uncertainties in measurements of initial position and momentum can result in huge errors in long-term predictions of these quantities.