Good discussion of the topics, but it 2021, it feels rather dated. Had previously read about most of it in other works, but this book was a nice summary that got me back up to speed. ( )

Good book to refresh everything you studied in university in a not-too-boring way. Also contains an introduction into the field of network science which I did not know about before. ( )

At times on the technical side of a general audience science book, Complexity: A Guided Tour covers the full range of what complexity science means, at its core (in terms of self-organising behaviour, networks and so on) and how it applies to various sciences (neuroscience, evolution, genetics, etc.).

There are some particularly inspiring, fascinating chapters. Highlights for me were her coverage of fractals, of the different ways to measure complexity, and how genetics is increasingly seen as an incredibly complex set of networks of genetic switches and machines. Occasionally I thought there were sections, or even the odd chapter, that were a little superfluous. But on the whole, Mitchell makes the case that this is an incredibly important scientific discipline that may hold profound answers to many fields of science, even if complexity science itself is still in its relative infancy. ( )

This is very very good ( if not as little .... complex ) ( )

Pretty readable and broad introduction to a lot of ideas. A good source for more detailed things to read on particular subject of interest. ( )

What makes a popular science book exciting to non-specialists? It is not enough to be informative, it has also be lively and engaging. Melanie Mitchell’s “Complexity: A Guided Tour”, is such a book. Melanie is a professor of computer science at Portland State University and Santa Fe Institute, specialised in the study of complexity. In her book, she explores dynamical systems, information technology, genetic algorithms, cellular automata, chaos, and network theory.

complexityMitchell is a wonderful writer and her love for the subject is evident and infectious. Complexity: A Guided Tour” is stimulating and fun; it is not an easy read, but it is immensely worthwhile.

Reductionism has been the dominant approach to science since the 1600s, but it has reached its limitation, argues Mitchell. There are phenomena that are better described from a complex perspective, such as incest swarms, the brain, the immune system, the ecosystems, the economic markets, the World Wide Web. Complexity science is a broad and multi-disciplinary subject, it touches on almost all aspects of modern technology and science. The rise of an interest in understanding general properties of complex systems has paralleled the rise of the computer, because the computer has made it possible for the first time in history to make more accurate models of complex systems in nature.

“I have learned” says Mitchell in the preface of the book, “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 the climate”.

Up to this time, there is no consensus formal definition of complexity. Andrew Ilachinski defines it as the study of systems in which an “increasing number of independent variables are interacting in interdependent and unpredictable ways. Informally, “a complex system is a large network of relatively simple components with no central control, in which emergent complex behaviour is exhibited.” Relatively simple components” means that the individual components, or at least their functional roles in the system’s collective behaviour, are simple with respect to that collective behaviour. For example, a single neuron or a single ant are complicated entities in their own right. However, the functional role of these single entities in the context of an entire brain or an entire colony is relatively simple as compared with the behaviour of the entire system.”

The notion of nonlinearity is important here: the whole is more than the sum of the parts. The complexity of the system’s global behaviour is typically characterised in terms of the patterns it forms, the information processing that it accomplishes, and the degree to which this pattern formation and information processing are adaptive for the system, that is, increase its success in some evolutionary or competitive context. In characterising behaviour, complex-systems scientists use tools from a variety of disciplines, including nonlinear dynamics, information theory, behavioural psychology, and evolutionary biology, among others.*

Mitchell examines several proposals for common and universal principles that attempt to explain the regulation of all the diverse complex dynamical systems that we find in nature; John von Neumann’ s principles of self-reproduction; Robert Axelrod’s general conditions for the evolution of cooperation; W ( )

This is very very good ( if not as little .... complex ) ( )

A good survey book on complex systems. A sort of a cross between a popular presentation and an academic work. It does have numerous notes and a large bibiliography. ( )

A primer on complexity, genially and plainly written, and probably most useful as a jumping off point when something in it rings your bell and you pursue your interests.

Complexity a Guided Tour
Review of Melanie Mitchell’s book “Complexity: A Guided Tour”

This is a thoroughly disappointing book; or an eye opener. Or maybe both.
Disappointing because the book does not cover much more than many popular science books already in the market (and it promised a bit more than that). An eye opener because the topic surveyed is still fairly fashionable and comes up in the end as fairly vacuus.

Who is the author, what are her stated goals?

The author is a well known computer scientist from the world renowned Santa Fe institute. Her goal is to survey what she implicitly holds to be “the great unexplored frontier of science”. So far so good. She is actually careful to point out that as she will be talking about work in progress, some of the concepts might be a bit fuzzy around the edges and the book will be as much about clarifying “whether such interdisciplinary notions and methods [as complexity, emergence etc...] 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”.

Judge and party

The first problem with the book is that it is far from being impartial. Mrs. Mitchell does not hide her fascination for the topics that she studies (as a matter of fact someone not enthusiastic about one own’s work would probably not go very far), but this makes her less credible in her attempt to provide an objective assessment of the usefulness of her own field of studies. I found she was doing a credible job until chapter 17 (out of 19), which would not be too bad if the last chapters were not those dealing most directly with the relevance and prospect of “complexity science”. But a couple of sentences really rubbed me the wrong way. More on this in the note about “the mystery of scaling”, but suffice it to say at this point I don’t believe anybody deserves my attention who writes with a straight face that the so-called “metabolic scaling theory” has “the potential to unify all of biology” (or for that matter anyone relaying such a claim as even credible).

Surveying old chestnuts

For a book attempting to survey “the cutting edge of science”, much is covered that is fairly old and well established. Let us survey the table of content. The chapters 2 to 6 are respectively “dynamics, chaos and prediction”, “information”, “computation”, “evolution” and “genetics, simplified”. While each chapter in itself is not particularly bad, one would find better introduction to all these topics elsewhere. As I don’t imagine too many readers of Mrs. Mitchell are complete science novices, the material in these chapters is therefore not particularly useful. One could object that maybe the idea is not to expose the readers to the basic facts of these disciplines, but rather to present them within a new framework that would act as an eye opener. Unfortunately, I did not find that the presentation made of these topics was enlightening in this way.

Evolution in Computers and “Computation Writ Large”

These are the parts 2 and 3 of the book and in my view one of the better ones. The presentation of genetic algorithms through one example was one of the more interesting I’ve seen (little robot picking up garbage comes up with a neat trick that one would not necessarily have programmed a-priori). Again, I’ll levy the charge that the author does not make it particularly clear how the material she deals with in this part of the book relates to the rest and fits into the big picture. The author also covers cellular automata (a topic beaten to death by Wolfram’s A New Kind of Science) and provides some examples of current research in this field that are less likely to have been previously encountered by the reader. Then comes a vanity chapter dealing primarily with the author’s PhD thesis. While not uninteresting in itself, the subject does not warrant being put on equal footing with the other themes dealt within the book, but this is probably one of the lesser shortcomings of the book and one of the most understandable one.

Network Thinking and “The Mystery of Scaling”: I’ll bite

The next part of the book annoyed me to the extreme. Full disclosure: this is going to get emotional and somewhat ugly. If you don’t like this type of stuff, please move on! Ok, if you’re still reading, here’s my main issues with this part of the book: the “science” it describes is all style no substance. At its mediocre seems to specialize in producing factoids that can be usefully integrated in your average popular science article or Malcolm Gladwell book. At its pathetic worst, it becomes some sort of post-modern science where the clever positioning of the results matters more than their intrinsic worth. I won’t cover here all the issues I have, but will instead focus only on one example provided by the author (and already mentioned in my review above), the so-called case of the “mystery of scaling”. What’s going on here is that big animals have less surface to dissipate heat proportionally to their volume than smaller animals. This is something a high school student can easily understand. Given big animals do not routinely die of overheating, they must have a lower metabolic rate than small animals. One can through some sort of back of the envelope calculation predict how the metabolic rate should vary with size. The naive calculation does not seem to match experimental data very well. Then low and behold, a few heroic complexity theorist come up with a fractal network theory that seems to fit the data a bit better. My view is that this is a “cute and clever” explanation for a marginally interesting factoid. The book presents this as a revolution. I mean, come on! that’s just a bit of basic geometry that does not provide any insight whatsoever into any underlying biological process. Any assertion something like this would play a role in biology “similar to the theory of genetics” is either shameless and cynical self promotion, or the result of a total lack of perspective. To be fair, the author mentions that the claims made here are a bit controversial, but I find this part a bit disingenuous to say the least. If this type of theory can in any way be put on equal footing with genetic theory, one would expect at least some sort of application. Look for it and return when you’ve found it... you’re not going to be back any time soon.

I said I’d cover only one example, but the last chapter has a couple of nuggets that I just can’t avoid mentioning. Basically according to this chapter, biology and genetics are a massive failure (I’m exaggerating somewhat, but this is a summary). Junk DNA is not junk (that’s actually possible) but the most important bit of our genome (highly speculative but not flagged as such) and really understanding biology will require understanding biological networks (well, as the Dude said in the Big Lebowski, that’s like your opinion). It’s hard to keep one’s cool when reading things like this. Basically, bench scientists who have sweated all their life to look at the details of how things actually work are wasting their time. All that one needs is a self indulgent theoretician who will come up with suggestive analogies that a biological system is like the internet and then we’ll have the final word. Hmm.

Putting It All Together

Ok, I’m getting carried away a bit, so let’s come back to factual facts. I quoted Mrs. Mitchell when I started my review. Her goal was to clarify “whether such interdisciplinary notions and methods [as complexity, emergence etc...] 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”. Did she clarify this at all?
As a matter of fact, she touched upon these topics only briefly and certainly did not provide any evidence that complexity theory had anything useful to say on these topics. If one is looking for interesting ideas on how to deal with the tragedy of commons for instance, one would be much better served by referring to the work of someone having looked carefully at practical, real world examples. Someone like Elinor Ostrom for example. One will find recommendations on how to manage the complex by understanding the specifics of one complex situation. This looks to me much more promising than drawing remote analogies between non-commensurable systems. How long can scientists get a job to ponder fascinating similarities between fractal exponents? This would actually be a good subject for a sociology of science study. ( )

The kind of stuff the Santa Fe Institute has concerned itself with -- things like evolution, genetics, artificial life, genetic algorithms, small-world and scale-free networks. More up-to-date than similar books of years past, of course, but whether the subject is cohering better is questionable.