- Paperback: 208 pages
- Publisher: Basic Books (13 Aug. 1996)
- Language: English
- ISBN-10: 0201442302
- ISBN-13: 978-0201442304
- Product Dimensions: 14 x 1.3 x 21.6 cm
- Average Customer Review: 4.0 out of 5 stars See all reviews (2 customer reviews)
- Amazon Bestsellers Rank: 691,564 in Books (See Top 100 in Books)
- See Complete Table of Contents
Hidden Order: How Adaptation Builds Complexity (Helix Books) Paperback – 13 Aug 1996
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Most Helpful Customer Reviews on Amazon.com (beta)
While this book was originally published in 1995, few books on this topic have matched its clarity in explaining the relationship between adaptation and its effects on injecting complexity into systems. There are numerous biological analogies, yet the overall approach addresses the more general application of cas to include human systems at a level accessible to most readers.
Holland's seven basic principles of complex systems remain relevant today and establish important insights about how complexity evolves. These consist of both properties and mechanisms that reveal how interaction affects adaptation and emergence. Holland describes them as:
Internal Models (mechanism)
Building Blocks (mechanism)
These are introductory in nature and serve as conceptual ideas about the workings of complexity as well as the components for building models and simulation networks. A thorough understanding of these conceptual components is an important starting point for a broader understanding of complexity across a number of domains, including human organizations.
For those interested in modeling complex adaptive systems, Holland guides his readers though the basics of constructing models while identifying some of the key challenges and approaches to representing human behavior in cas models.
Since the book was published, there has been a growing interest in how intervention might be successfully achieved in complex networks. Holland touched on this topic by introducing "lever points" (p 5) and identifying "clusters of effectors" (p. 45) but never explored the possibility of purposely injecting change into networks.
My only mild annoyance with this book is its reliance on overly complex diagrams which are sometimes hard to follow and not particularly complementary to the text.
Overall, this book remains a significantly strong contribution even 18 years after its initial publication. Such enduring value speaks to its role in contemporary complexity scholarship.
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However, if you are new to the phenomena of complex adaptive systems (CAS) or agent-based models (ABM), this might not be the best intro book for you. This is particularly true if you are wondering what a genetic algorithm is right now. I think you will get the most out of the book if you are already somewhat familiar with CAS and ABM as Holland does not dwell on illustrative examples. (Yes there are examples, but they are very short compared to other authors on this topic.) Because of this, I think this book will be rather dry and technical and non-intuitive for a real newbie. If you have no idea where to begin, try _Growing Artificial Societies_ by Joshua Epstein and Robert Axtell.
One final comment: for excellent in-depth look at the reiterated Prisoner's Dilemna model with genetic algorithms that Holland briefly discusses, read _The Complexity of Cooperation_ by Robert Axelrod. (Axelrod and Holland mention each other in their books.)