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Harnessing Complexity
The Scenario
Complexity science has grown increasingly popular in the past few years, with increased tools available for modeling, and increased examples of successful interventins in complex systems. Unfortunately, books on complexity have remained mostly crap, until now. In this slim book is a framework for not only understanding complex systems, but for doing something besides standing at the sideline and watching them unfold.
Axelrod and Cohen are founding members of the BACH group, which has been very influential in complexity research. They have been long standing members at either the Santa Fe Institute, which is the premiere complexity research facility in the world, or the University of Michigan's Study for Complex Systems, which has also had a large effect on the science. In other words, these are two cats who know their bidness. Now here's the good part. Axelrod and Cohen are solidly academic, but this book is not. The weakness of books on complexity is that they have either been written for other complexity theorists, making them inacessible, or for the general population, making them insipid. Even though both researchers have been studying this field for decades, and could have written something brillant yet obtuse, but instead they wrote something brilliant and useful.
The authors describe the characteristics of Complex Adaptive Systems in terms of the three main elements of those systems: variation, interaction and selection. The book is divided into roughly three parts, each dealing with one of these aspects. The systems described have many different components, and one of the contributions of this book is to provide a common vocabulary for these elements. Here's a sample bit of text that the authors claim would give you a rough summation of the book:
"Agents, of a variety of types, use their strategies, in patterned interaction, with each other and with artifacts. Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination, thus changing the frequencies of the types within the system."
There are many examples of complex systems that the authors use to bolster their explanations of complexity theory. How a disease spreads, how the military makes far reaching changes in philosophy, and of course evolution all drive home concisely crafted observations about complex adaptive systems. There's even a little gem that talks about the development of an open source project, specifically Linux. The authors discuss some conditions under which an open source development model might thrive, or at least make sense. As a favor to the authors, we'll make you read the book to find out what those are.
Complexity theory is not the same as chaos. Complex systems are not chaotic, though they do depend on variation in order to adapt, or change the equilibrium point. The important message here is that complex systems are not beyond our understanding, though it may be tough. Also, because complex systems depend on churn, if we can arrange ourselves at that point of churn, and try to direct we can affect systems that have been previously thought unalterable.
What's Good?
The tone of this book is killer. Combining lucid explanations with meaningful descriptions makes this very readable without diminishing the topic at all. The final chapter even outlines the rest of the book for you, boiling it down to the bare bones points that you should really take from the text. It might be helpful to read it first, and then go through and read the rest of the book.
The other strength of the book is how the authors manage to follow a strong academic tradition of supporting points with evidence without succumbing to making the book sound like the usual academic crap. All of the points made are supported not only with the great examples, but with evidence from a large body of research, mostly academic. The bibliography for this book would be a great place to start for any person or group interested in delving deeper into issues surounding complexity theory.
This assertion that we can understand complex systems, and exert influence over them is an important concept for a new paradigm for thinking. The systems being developed, computer or otherwise, are mostly examples of complexity in action. Whether it is an open source project being created or a new design team you are putting together, they are rarely systems that can be boiled down to simple cause and effects. The Newtonian view of a mechanical universe has polluted the very way in which we think about systems, the way in which we understand the universe. The people researching complex adaptive systems are working against that, and this book is a definitely volley in the right direction.
What's Bad?
This question is a matter of audience in the case of this book. It is definitely written for laymen, so if you are into the math of complexity research, or the modeling, then seek on crazy diamond. The intended audience here is the person who has to deal with complex, adaptive systems, but is not an expert in math. This book is intentionally short and brief, designed for those without a lot of leisure reading time. If you're after the uber compendium of complexity theory, this is not your book either.
While it is a minor point, the title of the book is annoying. It is understandable for marketing reasons, but it could turn off some smart people to reading the book, fearing it might be of the "Business Guru" shallow variety. Do not listen to these fear, buy this book.
So What's In It For Me?
If you've been interested in complexity theory, or need to work with complex adaptive systems (which everyone must) then this book has quite a bit to offer to you. Practical advice on how to exert influence in a complex environment could be invaluable to the reader. Besides the practical good it can do the reader, this book also has something to teach you about how you think about the world in general. Being aware of the complex systems around you, and thinking more deeply than black and white, or even gray, about these systems has benefits that far exceed your current job or project.
This book could also become valuable for the open source movement in general. Understanding complex, adaptive systems will also increase the chances for success of a number of possible open source projects as well as how to position them in software markets, which are themselves great examples of complex systems. It would be great if people involved with open source could champion this method of worldview both for its intrinsic and extrinsic benefits.
Purchase this book at ThinkGeek.
I doubt I really want to get a Phd in math just to undetstand something like this. Chaos theory is replete with mind bending difficulty.
Respond to s
Complexity theory solutions fall into two types:Ones where you can't understand the underlying process, in which case you can't trust the complexity theory result.
It's a pile of charlatanry. I say don't spend another tax dollar on it.
-- the most controversial site on the Web
So the next time you go past that desk at work that looks like the desk from "Shoe", just leave it. Chances are that that desk jockey has everything straightened out in some kind of system.
"Ancillary does not mean you get to rule the world." --U.S. Circuit Judge Harry Edwards, speaking to the FCC's lawyer
Well, I wasn't too impressed with it. It took a 'backwards' look at the success of linux and (it seemed) made up something to fit the information available. With a lot of his statements, the author was having to reach just to make things seem to 'fit' together. As for the statement where we should try to patent ourselves with this book -- it has some interesting ideas but they seem to far-fetched to trust your properity to (as of yet).
The information cannot be found in a satisfactory format anywhere else.
Respond to s
I believe you may have missed the purpose of book reviews and the 'open source' movement many /.'ers enjoy.
/.'ers to a book that they might find useful and enjoyable. It shouldn't matter if the book isn't "free." In college, you MUST purchase many (see MANY) books -- and occasionally your prof's might alert you to a 'very good book.' Whether or not it is 'free' says nothing to whether someone should have alerted you to that. Want to be "free FREE FREE" in everything -- Go to the store and demand your free meal.. they will escort you to the dumpster.
The book review was to alert
I believe that any sufficiently advanced topic an be broken down into a format where almost anyone could undetstand.
In the practical world most people even in science and engineering usually only use up through some basic level of differential equations.
The rest is pure theory that dosn't work in the physical universe.
Respond to s
"Complexity theory is not the same as chaos. Complex systems are not chaotic, though they do depend on variation in order to adapt, or change the equilibrium point. The important message here is that complex systems are not beyond our understanding, though it may be tough" Isn't that the whole point of Chaos theory, That there is an underlying order to any complex system? Sounds like Complexity theory IS Chaos theory (or very closely related)
Just read the book and adapt the contents to your own open source book. It's just that people are usually lazy.
Respond to s
My K dropped from 33 to 15, after the 3 AMD articles, and various other instances where I was outnumbered. Think of this as a form of personal redemption. Personally, I'm willing to moderate, the moderation would moderate my karma and offset gains. I just wish that I could find out how to moderate.
"Ancillary does not mean you get to rule the world." --U.S. Circuit Judge Harry Edwards, speaking to the FCC's lawyer
It's a useful artificial impliment which enhances one's preformance of tasks which are cryptographic in nature.
Next time you want to critize sigs please look at some of the rather banal, smug ones that you see on other's and give them a whirl.
If english isn't your first language then ask what it means first.
Respond to s
by Heinz R Pagels, ISBN 0553347101
When I was first introduced to the idea of fuzzy sets, it was a little bizarre. Then it became clear that it was completely natural to think in terms of fuzzy sets. Discussing it with others, most have come to the same conclusion. And in discussing other aspects of complex systems, even amoung the average joe, it becomes pretty clear, that people are more than well enough equipped to deal with complex systems - both phsyical and mental. I don't know if describing it as some new paradigm of thinking is fair (and borders on sensationalism to sell a book).
Look at children. They learn to master extremely difficult complex systems like locomotion, language and exploration - all by the time they are five years old. How is most of this accomplished? Through imitation and experimentation. Children imitate what they see but they also explore their world. It's how many things are accomplished. Flight was mastered by looking at how birds fly and experimenting. So was medicine, math and every other complex system.
We may develop new terminology or methodology, but it all boils down to the same concepts.
Well, first of all there is no such thing as "complexity science". There is a multidisciplinary field of study, very broad one, populated mostly by statisticians and physicists with some sprinkling of mathematicians, biologists and some other assorted folk. What unites this field is attempts to deal with complex systems which cannot be modeled by "classic", mostly linear, methods. Currently it's mostly a motley collection of approaches and techniques.
:-)
[rough summation of the book]: "Agents, of a variety of types, use their strategies, in patterned interaction, with each other and with artifacts. Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination, thus changing the frequencies of the types within the system."
They did say this is written for a layman, right?
Anyway, this basically describes "genetic algorithms", an optimization technique. Nothing particularly innovative and there are better books about it. Certainly, the complexity study field is much wider than this.
Complexity theory is not the same as chaos.
Well, a theory is not the same as chaos. That's reassuring to know.
Complex systems are not chaotic, though they do depend on variation in order to adapt, or change the equilibrium point.
Err... "complex systems" is a very very wide term (usually meaning "I can't understand how it works and I've been looking at it for five minutes already!") and generally chaotic systems are treated as a subset of complex systesm. Not that the quoted sentence makes much sense, anyway. "Depend on variation in order to adapt"? Ahem, and how else could one adapt except by changing something?
The important message here is that complex systems are not beyond our understanding, though it may be tough.
No kidding! That is the important message of the book? Wow! That's really, really useful to know.
[flame] It sure looks tough, for complex systems are clearly beyond the understanding of the reviewer. Hint: before reviewing a book on Slashdot it's useful to know at least something about the field. [/flame]
Kaa
Kaa
Kaa's Law: In any sufficiently large group of people most are idiots.
If this is the article author's idea of "accessible" and "lucid," I'd hate to see what he'd consider obtuse.
The author's right about the prose not being either academic or Bizspeak. It's a hybrid of both, as unintelligible as either but without the former's precision or the latter's accessibility.
-- He's fantastic, made of plastic....
I've bought a couple books off the reviews, and liked the ones I've gotten. But it'd be interesting to see reviews of the full range (in goodness to badness) of books--
It is time for everyone to simply think outside the BACHs!!
(Yeah, I know, but the alternative is for me to do some real work. ;-)
Practice random senselessness and act kind of beautiful.
...some of it might end up in your brain. Not much chance of that though!
Complexity theory seems to be another word for chaos theory. In chaos theory the fist assumption one makes is that the system is not perfectly chaotic, thus it is 'merely' complex. The second condition is that the initial conditions are well known. The genetic algorithm is a bottom up approch to the same conditions. Given an initial condition and a well known ideal goal find the best solution. Thus, the algorithm creates order from chaos through chaos. I would go so far as to say that complexity, chaos, and the genetic algorithm are all subsets the same discipline.
So... what's the other type?
-- It only takes 20 minutes for a liberal to become a conservative thanks to our new outpatient surgical procedure!
Is there a difference between the PII and the PIII besieds Mhz?
...and business are not common at this point, but there have been several good books about emergent phenomena and complexity-theory attempts to explain them:
Complexity: the Emerging Science at the Edge of Order and Chaos by M. Mitchell Waldrop is my favorite. It's very readable and a good introduction for the layperson.
Complexity: Life at the Edge of Chaos by Roger Lewin takes a more biological perspective, using the Cambrian explosion and subsequent extinctions as a primary theme of inquiry. I haven't finished it yet, but find it almost as interesting as Waldrop's book. (Personally Lewin's style is chatty for my taste with its constant recreation of his conversations with various scientists.)
Lewin and Birute Regine have recently written a book called The Soul at Work, but I haven't read it yet. It may prove interesting since Regine's area of specialty is developmental psychology (which is a natural for complexity studies, but whose practitioners have not yet become interested in studying emergence). This book is perhaps the most direct competition for this book being reviewed. I hope someone who has read it will post something to this discussion.
John Holland has two good books out which may /.ers may relate more directly to, since he is a bit of a renagade in the computer science community (or was until he started turning out to be proven right). His books are very good for the detail (and even some math). But they are almost written from a lab-notebook perspective, recreating the evolution of his thought even to the point of exploring dead ends which he later abandons.
Given all this, I have some difficulty with this reviewer's blanket denunciation of the field. None of these books is long on business-babble or psycho-pspeak, so I'm at a loss to understand his generalization.
I will check this book out, but I would offer the following caveat: Complexity science is an interesting outgrowth of chaos theory which is still controversial within the scientific community. It's on the bleeding edge of current scientific thought and may yet pan out to be a dead end (or a world-changing advance).
I am personally following it with considerable interest and have already come up with a number of applications which helped with both my programming and my business. But anyone who makes blanket evaluations of it (pro or con) is probably exaggerating their actual knowledge.
Eternal vigilance only works if you look in every direction.
Chaos theory involves a formula whose output varies widely with a very small change in the input. The equation can be quite simple (like many fractal-generators), or very complex (like trying to model the weather), but the main distinguishing feature is that even with exact calculations, an immeasurably small difference at the start will give you completely different final results--but in the _mathematically interesting_ cases of chaos, the results are bounded. (A butterfly in Singapore might cause a hurricane in Tampa, but it never snows in Detroit in July...) Complexity theory, on the other hand, requires things to be so complex from so many interacting agents that you can't calculate them exactly, but this does not say that you can't do an approximate calculation and get a good prediction--unless it is both complex and chaotic, like the weather and the stock market.
I dunno what they are up to at the Santa Fe Institute, and I dunno why people would equate "complexity theory" with "chaos theory" when they are two distinctly different things. But basic computational complexity theory as I studied it in Grad School is about as controversial as the notion that 2+2 equals 4.
It is definitely, absolutely NOT chaos theory, which I've never formally studied but which I'm given to understand involves order spontaneously emerging out of disorder. Two totally different things from what I understand....
Which *bad* ideas are you talking about?
I'd agree that the notion of partial set membership is a Good Idea.
However (I would say) the rules used for doing computations with fuzzy membership numbers -- at least, the ones typically advocated -- are arbitrary, ad-hoc, and fundamentally plain wrong. Sometimes they are useful as a very rough and ready engineering fix when nothing can go too badly adrift, but basically they are Not A Good Thing At All.
A more principled way to deal with fuzzy numbers is to use the machinery of Bayesian calculation, treating fuzzy values as ordinary probabilities, but relating to a wider ontology than just the physical state of reality.
Such extended ontologies arise very naturally from communication theory when we try to summarise data. For example, consider transmitting a set of points on a 2D grid using a mixture of Gaussians model. For each point, one sends the probability that it was generated by Gaussian A rather than B (less than one bit, using BitsBack), followed by the bit string to code its position using one or the other Gaussian.
Gaussians extend to infinity, so we can never definitiely allocate a point to one bump or the other -- it is always a mixture of the two. Thus even a knowledge of the whole of reality is not sufficient to resolve the probability to a definite 0/1 state. "Generation by bump A rather than bump B" is therefore technically a fuzzy proposition, rather than a classical one -- the variable is part of our description of the system (our extended ontology), rather than underlying reality.
In summary:
Endnotes:
1. All of which is entirely irrelevant to the subject of far-from-equilibrium pattern formation (which is what complex systems theory is mostly all about?).
2. For a more extensive discussion of Bayesian inference and fuzzy systems, there are classic papers by Cheeseman.
I don't argue the difference in the mechanism--I made that point myself. And, circulation is indeed present over the wing surface. I'm betting here that you haven't taken a course in aerodynamics. It's circulation that accelerates and decelerates the flow to create the pressure gradient across an airfoil.
Last I checked, the laws of aerodynamics don't accurately model separated flow. You can make a stab at it, but you don't get anything near "conventional"--at least not yet. Separated and turbulent flows are a bitch to model. Laminar flow is easy--and crisp.
--
-- Geof F. Morris
I read this book, and though I did enjoy it, I didn't think it was worth $26. There is good information here, but it could have been presented in about 10 pages. This book felt padded, even to the point where the margins and the font size seemed big.
There are severall theries called Complexity Theory. And one of them is also called chaos theory.