Complexity: A Guided Tour 1st Edition

I'm a philosopher of science specializing in the foundations of complex systems theory, and this is absolutely the best comprehensive introduction to the field that I've come across. Mitchell is a computer science professor, as well as part of the Santa Fe Institute, so she's absolutely a reliable source on this topic. The book is extremely accessible for someone with very little background in dynamical systems theory or higher mathematics, and despite being mostly non-technical, does a good job actually articulating the central problems and concepts clearly without sacrificing precision or accuracy. It's a wonderful overview of the field, and I highly recommend it to anyone who is curious about what the hell a "complex system" is, why they're worth studying, and how science is learning to deal with them. Anyone interested in where science (and philosophy of science) will be headed during the 21st century should pick this book up: the study of complex systems is poised to be the next "big thing" (or paradigm shift) within the natural sciences, and it's relevant to a really mind-boggling array of contemporary scientific, social, political, and philosophical problems.

In “Complexity”, Mitchell utilizes her talent for explaining difficult material. She also seems wise, objective, humble despite her qualifications. Whenever possible she uses simple, concrete examples to get her points across. Ironically, the one subject where the book almost totally failed me was in the explanation of an AI computer program to generate analogies, Mitchell’s Ph.D. thesis subject. As Mitchell observes, “what we might call modern complex systems science is, like its forebears, still not a unified whole but rather a collection of disparate parts with some overlapping concepts.” A key concept is the complexity that can arise from simple rules. In fractal geometry a simple rule is applied repeatedly. In the behavior of ants, complex, seemingly purposeful behavior arises from each ant following a simple set of rules, but in endeavors like foraging, only probabilistically. This is typical whether it is ants foraging or the immune system combatting infection; many agents are working in parallel, and the most likely paths are followed the most intensively, but with some probability less likely paths or solutions are tried. Genetic algorithms, which are heuristics for solving problems that cannot be solved by mathematical optimization, also work probabilistically, combining what have been the most successful solutions in each iteration with some chance of trying what are likely to be unsuccessful variants, thereby avoiding finding only local optima. Many subjects, such as neural connections in the brain, can be modelled as networks, embodying common concepts. For example, often many nodes are connected to “nearby” hubs, which then provide longer connections to more distant hubs. The distribution of the number of links to each node can often be approximated by a power law. Mitchell uses a simple equation to illustrate chaos. For certain values of the equation parameter, successive iterations of the equation are supersensitive to the initial value of the input x, and generate a string of outputs that appear to be random numbers, whereas for other parameter values, the string of outputs converges to a single point, or oscillates between a fixed number of points.Mitchell often illustrates concepts by using biology. I was surprised to learn that the eyes in many different creatures, humans vs. octopi, may not illustrate convergent evolution, but all start with the same critical gene – if a mouse version is implanted into a fruit fly leg during development, you get a fruit fly eye on the leg. Conversely, not only is there not a single exponent for how metabolic rates vary with animal mass across all animals as Mitchell discusses, but the metric to examine is not resting metabolism, but maximum metabolic rate, for that is what is constrained by blood supply (cf “Power, Sex, Suicide: Mitochondria and the Meaning Of Life” by Nick Lane).

This is not an encyclopedia. To appreciate it, you will have to spend just a little time considering what Dr. Mitchell has to present. While no math is required, it is helpful to know some. No science background is required, but it is beneficial to have had at least a couple of classes in science.I read many of the one and two star grumbles below before I posted this. Somehow, they missed the point of her book. The world is far more complex and fascinating than we imagined. She integrates birds, broccoli, social networks, earthquakes, and economic concepts by presenting some of the hidden common factors.Is this complete? No. The field seems to be at a similar point to where the mathematics was before the birth of Leibnitz and Newton. On the other hand, you might suddenly see a connection no one else has. Here is an example. There is a similarity between the studies of cities, information theory concepts, and ants. Enjoy the exploration.

This book doesn't take you very far into the specifics of any field it addresses. I'd expected this to some degree, but I was surprised to see no development of any tangible, unifying theoretical framework in a manner that commits to at least one or some other discipline. Again, I'm sure that's by design to some degree. But it wasn't for me, and ultimately felt bland. I felt the insights here can be found from within most other traditions in a more dedicated, practical way, because you never get the sense you're being given something you can use beyond a very general taste.So I think it serves mostly to stimulate the imagination, if that's what you're looking for. But it doesn't persuade and doesn't do more than point in general directions.

If you are coming to this subject from an unrelated maths or physics field and looking to explore complexity and how it appears in Nature, this book may be perfect for you. Unfortunately, as a biochemist, I found the biology chapters far too basic and the maths ones far too advanced. I worked hard at it and managed to get my head around most of the tougher chapters, but beware biologists looking for an easy read - if you aren't in the mood for some hard thinking, this may not be the book for you!

This is a very good introduction to complexity. It's easy to read, doesn't restrict itself to one discipline but instead covers a broad range, and whilst it doesn't go in-depth it definitely has enough for any reader to finish with a fairly good grasp of the main issues and areas of complexity.

Very good and simple to read

Must read. Fairly basic yet very good and accessible intro to complexity.

Simple, short and covers all the complex systems topics