What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of individual neurons produce something as extraordinarily complex as consciousness? What is it that guides self-organizing structures like the immune system, the World WideWeb, the global economy, and the human genome? These are just a few of the fascinating and elusive questions that the science of complexity seeks to answer.In this remarkably accessible and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate, detailed tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge fromsimple interactions among myriad individuals. Comprehending such systems requires a wholly new approach, one that goes beyond traditional scientific reductionism and that re-maps long-standing disciplinary boundaries. Based on her work at the Santa Fe Institute and drawing on its interdisciplinarystrategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. She explores as well the relationship between complexity and evolution, artificialintelligence, computation, genetics, information processing, and many other fields.Richly illustrated and vividly written, Complexity: A Guided Tour offers a comprehensive and eminently comprehensible overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for the field's contribution to solving some ofthe most important scientific questions of our time.
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.