Mitchell M. Complexity. A Guided Tour

Oxford University Press, 2009. — 880 p.The idea for this book came about when I was invited to give the Ulam Memorial Lectures in Santa Fe—an annual set of lectures on complex systems for a general audience, given in honor of the great mathematician Stanislaw Ulam. The title of my lecture series was The Past and Future of the Sciences of Complexity. It was very challenging to figure out how to introduce the audience of non-specialists to the vast territory of complexity, to give them a feel for what is already known and for the daunting amount that remains to be learned. My role was like that of a tour guide in a large, culturally rich foreign country. Our schedule permitted only a short time to hear about the historical background, to visit some important sites, and to get a feel for the landscape and culture of the place, with translations provided from the native language when necessary.
This book is meant to be a much expanded version of those lectures—indeed, a written version of such a tour. It is about the questions that fascinate me and others in the complex systems community, past and present: 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). A central purpose of this book is to provide a clearer picture of what these people are talking about and to ask whether such interdisciplinary notions and methods are likely to lead to useful science and to new ideas for addressing the most difficult problems faced by humans, such as the spread of disease, the unequal distribution of the world’s natural and economic resources, the proliferation of weapons and conflicts, and the effects of our society on the environment and climate.
The chapters that follow give a guided tour, flavored with my own perspectives, of some of the core ideas of the sciences of complexity—where they came from and where they are going. As in any nascent, expanding, and vital area of science, people’s opinions will differ (to put it mildly) about what the core ideas are, what their significance is, and what they will lead to. Thus my perspective may differ from that of my colleagues. An important part of this book will be spelling out some of those differences, and I’ll do my best to provide glimpses of areas in which we are all in the dark or just beginning to see some light. These are the things that make science of this kind so stimulating, fun, and worthwhile both to practice and to read about. Above all else, I hope to communicate the deep enchantment of the ideas and debates and the incomparable excitement of pursuing them.Part One Background and History
What Is Complexity?
Dynamics, Chaos, and Prediction
Information
Computation
Evolution
Genetics, Simplified
Defining and Measuring Complexity
Part Two Life and Evolution in Computers
Self-Reproducing Computer Programs
Genetic Algorithms
Part Three Computation Writ Large
Cellular Automata, Life, and the Universe
Computing with Particles
Information Processing in Living Systems
How to Make Analogies (if You Are a Computer)
Prospects of Computer Modeling
Part Four Network Thinking
The Science of Networks
Applying Network Science to Real-World Networks
The Mystery of Scaling
Evolution, Complexified
Part Five Conclusion
The Past and Future of the Sciences of Complexity