Emergence

Emergence : From Chaos to Order

3.94 (169 ratings by Goodreads)
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Description

This new book, by acclaimed scientist- John Holland, introduces the reader to the exciting new theory of 'emergence', which many people now consider to be the single most characteristic feature of complex, adaptive systems. By 'emergence' it is meant that such systems tend to involve large numbers of intelligent, adaptive agents, interacting on the basis of local information possessed by each agent. These interactions produce global behaviour that cannot be understood simply by knowledge of the individual agents; it is 'emergent behaviour'. Examples of phenomena of this kind are price movements on speculative markets (where agents are individual traders) or road-traffic patterns (where the agents are individual drivers). The book explores the theory of 'emergence', demonstrating how a small number of rules or laws can generate systems of surprising complexity. Board games provide an ancient and direct example: Chess is defined by fewer than two dozen rules, but the myriad patterns that result lead to perpetual novelty and emergence. The discovery of similar patterns in other facets of our world opens the way to a deeper understanding of the complexity of life, answering such questions as: How does a fertilised egg program the development of a trillion-cell organism? How can we build human organisations that respond rapidly to change through innovation? Throughout the book, Holland compares the different systems and models that exhibit emergence in the quest for common rule or laws. These range from the tiny seed "that encloses specifications that produce structures as complicated and distinctive as the giant redwood and the common daisy", to the checkers-playing computer that learned to beat its creator consistently, to the ant colonies that build bridges over chasms and navigate leaf-boats on streams, to the emotive creations of the past. All are explored in a book that will have important ramifications for every aspect of human intellectual endeavour. Reviews "John Holland is an exceptionally imaginative person. Often surprising, and always engaging, he takes the reader on a journey from simplicity to complexity, showing how a few 'rules of engagement' can lead to systems as bewilderingly rich as the neural networks in our brains, our immune defenses against pathogens, and even the ecosystems that maintain the biosphere so that life can flourish" Sir Robert May, Chief Scientific Advisor to the UK Government, and one of the founders of Chaos Theory" "Holland at his best - a crisp, insightful framework for analysing and modelling emergent phenomena" John Seely Brown, Chief Scientist, Xerox Corporation "I think it's safe to say that this book will certainly be a significant contribution to the field of complex system theory. And I can't think of a person more well-qualified to write such an account." Professor John Casti, Santa Fe Institute, and author or Would-be worlds.show more

Product details

  • Hardback | 272 pages
  • 156 x 234mm | 570g
  • Oxford University Press
  • Oxford, United Kingdom
  • 40 line illustrations
  • 0198504098
  • 9780198504092

Review Text

We hesitated about recommending this one, because it really is heavy going in places. But it is also extremely important, and deals with the big question of the origin of complex things in the Universe, including life. If you cut your teeth on the works of Ilya Prigogine, you may feel ready to tackle this; if you are frightened of equations, though, give it a miss. A profound book which repays the effort of coming to terms with the ideas it describes. (Kirkus UK)show more

Table of contents

Before we proceed; Games and numbers; Maps, game theory, and computer-based modelling; Checkers; Neural nets; Toward a general setting; Constrained generating procedures; Samuel's checkersplayer and other models as Cgps; Variation; Levels of description; Metaphor and innovation; Closingshow more

Rating details

169 ratings
3.94 out of 5 stars
5 28% (48)
4 43% (73)
3 23% (39)
2 5% (8)
1 1% (1)
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