This is an excellent book to study if you are just beginning to read about complexity and are interested in the application of the science of complexity to artificial and real life. Melanie Mitchell is well qualified to teach us about this field as she has been connected with Santa Fe Institute since 1989, five years after it was founded. The book is well written, easy to read and follows impeccable logic.
The book begins with a quote from Douglas Hofstadter, Godel, Escher, Bach, “Reductionism is the most natural thing in the world to grasp. It's simply the belief that 'a whole can be understood completely if you understand its parts, and the nature of their sum.' No one her left brain could reject reductionism.” In complexity reductionism doesn't apply. You can't understand a complex system as a sum of its parts. It can only be understood as a gestalt.
“How is it that those systems in nature we call complex and adaptive-brains, insect colonies, the immune system, cells, the global economy, biological evolution-produce such complex and adaptive behavior from underlying, simple rules? How can interdependent yet self-interested organisms come together to cooperate on solving problems that affect their survival as a whole? And are there any general principles or laws that apply to such phenomena? Can life, intelligence, and adaptation be seen as mechanistic and computational? If so, could we build truly intelligent and living machines? And if we could, would we want to?
I have learned that as the lines between disciplines begin to blur, the content of scientific discourse also gets fuzzier. People in the field of complex systems talk about many vague and imprecise notions such as spontaneous order, self-organization, and emergence (as well as "complexity" itself).”
The author describes the following properties of complex adaptive systems:
“When looked at in detail, these various systems are quite different, but viewed at an abstract level they have some intriguing properties in common:
Complex collective behavior
Signaling and information processing: All these systems produce and use information and signals from both their internal and external environments.
Adaptation: All these systems adapt-that is, change their behavior to improve their chances of survival or success-through learning or evolutionary processes.”
She follows this with two versions of a definition of a complex system:
1. “A system in which large networks of components with no central control and simple rules of operation give rise to complex behavior, sophisticated information processing, and adaption via learning or evolution”
2. “A system that exhibits nontrivial emergent and self organizing behavior”
She adds the comment that some people apply these concepts only to adaptive complex systems, but that she does not. Which is unfortunate because her definition limits complexity essentially to living systems, leaving out many non adaptive complex systems generally accepted as examples of complexity. But, her book is limited to a train of thought that leads to artificial and real life, and she stays almost consistent with that view.
She also reports that there are no agreed measurements of complexity.
With this review of the development and state of complexity science in general, the author then launches into the logical development of the history and state of artificial life:
• Life and evolution in computers
• Computation writ large
• Network thinking
• The past and future of the sciences of complexity
“…how did life originate in the first place? And what exactly constitutes being alive? As you can imagine, both questions are highly contentious in the scientific world, and no one yet has definitive answers. Although I do not address the first question here, there has been some fascinating research on it in the complex systems community.
The second question-what is life, exactly?-has been on the minds of people probably for as long as "people" have existed. There is still no good agreement among either scientists or the general public on the definition of life. Questions such as "When does life begin?" or "What form could life take on other planets?" are still the subject of lively, and sometimes vitriolic, debate.
The idea of creating artificial life is also very old, going back at least two millennia to legends of the Golem and of Ovid's Pygmalion, continuing in the nineteenth-century story of Frankenstein's monster, all the way to the present era of movies such as Blade Runner and The Matrix, and computer games such as "Sim Life."
These works of fiction both presage and celebrate anew, technological version of the "What is life?" question: Is it possible for computers or robots to be considered "alive"? This question links the previously separate topics of computation and of life and evolution.”
This book is not an easy read, but it's worth the effort. The author has done an outstanding job of writing about the science of complexity in a way that facilitates understanding.
Complexity: A Guided Tour, Melanie Mitchell, Oxford University Press, 2009, 349 p