By Kevin Kelly, Perseus Books Group, May 1995, 978-0201483406

Kevin Kelly has a brilliant mind, and this is a great and fun book. Kelly has grand ideas encompassing evolution and how it maps to just about every complex problem we faced in the 1990s from the “hive mind” to “creating multimillion-line programs to fly airplanes”. If you like thinking about the “why” of the world, this is a book for you.

Needless to say, I love this stuff. I have grand ideas, too, and having worked in and given up on the Crystal Ball Society, it’s fun to poke holes in the grand theories. To be fair to Kelly, however, this book was written at a time when everything was predictable. And, predictably, he chatted with the “gurus” at the Prediction Company, a rival to where I worked at the time, Olsen & Associates AG (O&A), in Zurich. The easiest way to poke fun at the grand theories is to look at where they have led: nowhere. When I visit today (11/24/2007), I see a company bought out in its heyday by a large Swiss bank – much to the chagrin of Richard Olsen – and still not predicting anything predictably.

I’ll poke more holes in the actual text below, which is probably untolerably long for most people. This book taught be a lot about a lot of things. Lots of sticky notes in its pages that have been converted by the magic of OCR technology into text. It even converted a LISP program, which amazes me, even though, it’s just the march of progress that allows me to write these notes with so much source material.

[p68] Ecologies and organisms have always been grown. Today computer networks and intricate silicon chips are grown too. Even if we owned the blueprints of the existing telephone system, we could not assemble a replacement as huge and reliable as the one we had without in some sense recapitulating its growth from many small working networks into a planetary web.

Creating extremely complex machines, such as robots and software programs of the future, will be like restoring prairies or tropical islands. These intricate constructions will have to be assembled over time because that is the only way to make sure they work from top to bottom. Unripe machinery let out before it is fully grown and fully integrated with diversity will be a common complaint. ‘‘We ship no hardware before its time,” will not sound funny before too long.

We own the blueprints to the Internet, and we can assemble an Internet quite quickly. The problem is that we can’t connect the computers to the people, which is what makes the Internet interesting. With a single download, the Google Toolbar turns every computer connected to the Internet into a single massively parallel computer. The biggest computer assembled in the world, in a matter of hours of downloads, all directed by a single entity (Google), which can upgrade its networks, also with a push of a button. The Google network including all machines which run its Toolbar is the most complex machine ever built, perhaps, except, of course, for the Microsoft network, which connects all computers running Windows. It’s pretty difficult to imagine this complexity, except when, say, a bug is detected, which brings down the whole network. All of this was a nascent dream at the time Kelly wrote his book. Now that we see how complex machines are built today, we know that it has little to do with the hardware, and everything to do with the way the software interacts.

[p127] Lao Tzu’s wisdom could be a motto for a gung-ho 21st-century Silicon Valley startup. In an age of smartness and superintelligence, the most intelligent control methods will appear as uncontrol methods. Investing machines with the ability to adapt on their own, to evolve in their own direction, and grow without human oversight is the next great advance in technology. Giving machines freedom is the only way we can have intelligent control.

What little time left in this century is rehearsal time for the chief psychological chore of the 21st century: letting go, with dignity.

A hard psychological lesson for the best of us. The new billionaires have not had (psychological, not evolutionary) time to adapt to their billions, and they receive too little feedback to adapt quickly. A theme throughout Kelly’s book is the hive mind, yet he is drawn to the gurus, not the hive mind, for discussions. In the two thousand years since Lao Tzu published his great book we have not learned to let go, I don’t know why we would learn to let got now. We cling to guru-worship and populism as a means of learning, when learning, I believe, is best done by doing. This book is a monument to holding on to myriad details assembled into a monolith that is the antithesis of “Out of Control”.

[p155] The biospherians showed the officer receipts and other paperwork that proved the late finches were mere captive-bred store pets, a status that was okay with the Wildlife Department. “By the way, what other birds do you have in there?” he asked them.

“Right now, only some English sparrows and a curved-bill thrasher”

“Do you have a permit for that curved-bill thrasher?”

“Uhhh, no.”

“You know that under the Migratory Bird Treaty it’s against federal law to contain a curved-bill thrasher. I’ll have to give you a citation if you are holding him deliberately.”

“Deliberately? You don’t understand. He’s a stowaway. We tried very hard to get him out of here. We tried trapping him every way we could think of. We didn’t want him here before and we don’t want him here now. He eats our bees, and butterflies, and as many insects as he can find, which isn’t many by now.”

The game warden and the biospherians were facing each other on either side of a thick airtight window. Although their noses were inches apart they talked on walkie-talkies. The surreal conversation continued. “Look,” the biospherians said, “we couldn’t get him out now even if we could catch him. We are _completely_sealed up in here for another year and a half.”

“Oh. Umm. I see.” The warden pauses. “Well, since you aren’t keeping him intentionally, I’ll issue you a permit for a curved-bill thrasher, and you can release him when you open up.”

Anyone want to bet he won’t ever leave?

[p181] A future “hydrogen economy” would use sunlight to crack water into hydrogen and oxygen, and then pump the hydrogen around like natural [p182] gas, burning it for energy where needed. Such an environmentally benign carbon less energy system would ape the photon-based powerpacks in plant cells.


Industry will inevitably adopt biological ways because:

  • It takes less material to do the same job better. […]
  • The complexity of built things now reaches biological complexity. […]
  • Nature will not move, so it must be accommodated. […]
  • The natural world itself-genes and life forms-can be engineered (and patented) just like industrial systems. […]

US-centric. Other governments don’t allow patenting of genes. This book is quite US-centric, and for the most part, intellectual-centric. The “hive mind” doesn’t like intellectuals, because, indeed, they aren’t on average. The “hive mind” aims for mediocrity, because it survives. Gurus do not exist in bee hives or other “swarmy” species. Specializations do occur but not in the form of directed intelligence or benevolent dictatorship. Any bee can talk to any other bee to communicate the source of a good place to move or a nice flowers.

[p183] Note the woolly flavor of these recent technical conferences and workshops: Adaptive Computation (Santa Fe, April 1992), modeling organic flexibility into computer programs; Biocomputation (Monterey, June 1992), claiming that “natural evolution is a computational process of adaptation to an ever changing environment”; Parallel. Problem Solving from Nature (Brussels, September 1992), treating nature as a supercomputer; The Fifth International Conference on Genetic Algorithms (San Diego, 1992), mimicking DNA’s power of evolution; and uncountable conferences on neural networks, which focus on copying the distinctive structure of the brain’s neurons as a model for learning.

Ten years from now the wowiest products in your living room, office, or garage will be based on ideas from these pioneering meetings.

Uh oh, a prediction with a date attached. I love those. It’s quite wrong. So-called Adaptive Systems, including neural networks, are too static. They are limited by their creators’ knowledge. Ten years leater, the coolest gadgets have been ultra-engineered – as they were in 1995 – by brilliant minds with the hive mind in mind. :-)

[p199] Ted Kaehler invents new kinds of software languages for his living. He was an early pioneer of object-oriented languages, a codeveloper of SmallTalk and HyperCard. He’s now working on a “direct manipulation” language for Apple Computers. When I asked him about zero-defect software at Apple he waved it off. “I think it is possible to make zero defects in production software, say if you are writing yet another database program. Anywhere you really understand what you are doing, you can do it without defects.”

Ted would never get along in a Japanese software mill. He says, “A good programmer can take anything known, any regularity, and cleverly reduce it in size. In creative programming then, anything completely understood disappears. So you are left writing down what you don’t know …. So, yeah, you can make zero-defect software, but by writing a program that may be thousands of lines longer than it needs to be.”

This is what nature does: it sacrifices elegance for reliability. The neural pathways in nature continue to stun scientists with how non-optimized they are. Researchers investigating the neurons in a crayfish’s tail reported astonishment at how clunky and inelegant the circuit was. With a little work they could come up with a more parsimonious design. But the crayfish tail circuit, more redundant than it perhaps needed to be, was error free.

The price of zero-defect software is that it’s over-engineered, overbuilt, a bit bloated, and never on the edge of the unknown where Ted and friends hang out. It trades efficiency of execution for efficiencies of production.

Where is Ted Kaehler today? He’s no longer a guru. The hive mind rejected his ideas. SmallTalk and HyperCard are dead (effectively). Tandem Computers, Inc. is dead, too (effectively). Tandem had a zero-defect policy for its software. It failed to evolve in a world where defects could be fixed on the fly. That’s what Kelly is missing: software can be repaired rapidly, and if the programmers are clever, the defect usually involves eliminating the defective software without any loss in function. That’s counter-intuitive if you think of programs being written by lots of programmers. If your “fitness” criterium for the evolution of programming is team size and larger is better, then yes, you’ll evolve a larger and larger team. If your fitness criteria includes smaller and smaller, you’ll evolve towards that. The danger of Kelly’s swarmy philosophy is that is large by definition, and any problems we encounter require mass not intellect.

[p225] Ubiquitous digital cash dovetails well with massive electronic networks. It’s a pretty sound bet that the Internet will be the first place that e-money will infiltrate deeply. Money is another type of information, a compact type of control. As the Net expands, money expands. Wherever information goes, money is sure to follow. By its decentralized, distributed nature, encrypted e-money has the same potential for transforming economic structure as personal computers did for overhauling management and communication structure. Most importantly, the privacy/security innovations needed for e-money are instrumental in developing the next level of adaptive complexity in an information-based society. I’d go so far as to say that truly digital money-or, more accurately, the economic mechanics needed for truly digital cash-will rewire the nature of our economy, communications, and knowledge.

E-money has not evolved as Kelly hoped, except that many people eliminate the hyphen and call it ecash.

[p279] In evolutionism, the borrowed concepts of mutation and sexual reproduction spawn the art. Instead of painting or creating textures for computer graphic models, artist Sims evolves them. He drifts into a region of woodlike patterns and then evolves his way to the exact grainy, knot-ridden piney look which he can use to color a wall in a video he is making.

You can now do this on a Macintosh with a commercial template for Adobe Photoshop software. Written by Kai Krause, the Texture Mutator lets ordinary computer owners breed textures from a choice of eight offspring every generation.

Evolutionism reverses the modern trend in the design of artist’s tools that bends toward greater analytical control. The ends of evolution are more subjective (“survival of the most aesthetic”), less controlled, more related to art generated in a dream or trance; more found.

We haven’t moved towards evolutionary art. The web (if you call it art) is all about more and more exacting control, be it asetic Web 2.0 or abundant MySpace aesthics, it’s about the artist’s control over every last detail.

[p280] In the end, breeding a useful thing becomes almost as miraculous as creating one. Richard Dawkins echoes this when he asserts that “effective searching procedures become, when the search-space is sufficiently large, indistinguishable from true creativity.” In the library of all possible books, finding a particular book is equivalent to writing it.

This sentiment was recognized centuries ago, long before the advent of computers. As Denis Diderot wrote in 1755:

“The number of books will grow continually, and one can predict that a time will come when it will be almost as difficult to learn anything from books as from the direct study of the whole universe. It will be almost as convenient to [p281] search for some bit of truth concealed in nature as it will be to find it hidden away in an immense multitude of bound volumes.”

[p308] Parallel computers embody the challenge of all distributed swarm systems, including phone networks, military systems, the planetary 24-hour financial web, and large computer networks. Their complexity is taxing our ability to steer them. ‘The complexity of programming a massively parallel machine is probably beyond us,” Tom Ray told me. “I don’t think we’ll ever be able to write software that fully uses the capacity of parallelism.”

Little dumb creatures in parallel that can “write” better software than humans can suggests to Ray a solution for our desire for parallel software. “Look,” he says, “ecological interactions are just parallel optimization techniques. A multicellular organism essentially runs massively parallel code of an astronomical scale. Evolution can ‘think’ of parallel programming ways that would take us forever to think of. Ifwe can evolve software, we’ll be way ahead.” When it comes to distributed network kinds of things, Rays says, “Evolution is the natural way to program.”

The natural way to program! That’s an ego-deflating lesson. Humans should stick to what they do best: small, elegant, minimal systems that are fast and deep. Let natural evolution (artificially injected) do the messy big work.

Danny Hillis has come to the same conclusion. He is serious when he says he wants his Connection Machine to evolve commercial software. ‘‘We want these systems to solve a problem we don’t know how to solve, but merely [p309] know how to state.” One such problem is creating multimillion-line programs to fly airplanes. Hillis proposes setting up a swarm system which would try to evolve better software to steer a plane, while tiny parasitic programs would try to crash it. As his experiments have shown, parasites encourage a faster convergence to an error-free, robust software navigation program. Hillis: “Rather than spending uncountable hours designing code, doing error-checking, and so on, we’d like to spend more time making better parasites!”

Thinking Machines was another casualty of the exuberance of the 1990s. Do we need “multimillion-line programs to fly airplanes”? Are there that many concepts that need to be encoded? I think not. When you think big IT, you get big IT. When you think about the problem and design tests which embody it, you get small programs that can be refactored (not evolved!) by programmers who know how to match patterns, because evolution has taught them that’s what they need to do. We are not simple parasitic organisms but rather the culmination of billions of years of parallel time. That’s why we can do better than parasites, if we simple focus on programming, and not try to come up with grand theories of how we might program better.

[p338] (SIN (IFLTE (IFLTE (+ Y Y) (+ x Y) (- x Y) (+ Y Y» (* x X) (SIN (IFLTE (% Y Y) (% (SIN (SIN (% YO. 30400002») X) (% Y 0.30400002) (IFLTE (IFLTE (% (SIN (% (% Y (+ X Y» 0.30400002» (+ X Y» (% X 0.10399997) (- X Y) (* (+ -0.12499994 -0.15999997) (- X Y») 0.30400002 (SIN (SIN (IFLTE (% (SIN (% (% Y 0.30400002) 0.30400002» (+ X Y» (% (SIN Y) Y) (SIN (SIN (SIN (% (SIN X) (+ -0.12499994 -0.15999997»») (% (+ (+ X Y) (+ Y Y» 0.30400002»» (+ (+ X Y) (+ Y Y»») (SIN (IFLTE (IFLTE Y (+ X Y) (- X Y) (+ Y Y» (* X X) (SIN (IFLTE (% Y Y) (% (SIN (SIN (% Y 0.30400002») X) (% Y 0.30400002) (SIN (SIN (IFLTE (IFLTE (SIN (% (SIN X) (+ -0.12499994 -0.15999997») (% X -0.10399997) (- X Y) (+ X Y» (SIN (% (SIN X) (+ -0.12499994 -0.15999997») (SIN (SIN (% (SIN X) (+ -0.12499994 -0.15999997»» (+ (+ X Y) (+ Y Y»»») (% Y 0.30400002»»).

NOTE: I think the above is very cool, because the OCR system pulled this out of prose without missing a step, and the OCRed text contains enough mistakes to yield the intent for the book’s purpose, yet the software is not executable by any interpreter that is built today, and you don’t need to understand LISP to recognize the breaks in the pattern. The errors are visible, because we understand patterns, and no known program can fix them. The OCR program didn’t convert it correctly to ASCII (even 7 bit ASCII, which is something it should have known to do), and it had the source. The OCR program did not know it was LISP, and it probably would never have known that, because LISP is a programming language and the rest of the scanned text was not. The fact that it was in a fixed with font could have tipped it off, but it didn’t. Could an evolutionary computer program figured out this was LISP and it should be executable? What is executable? What is LISP? Inquiring minds want to know, and parasites just want to get their next meal.

[p338] There was no evolutionary pressure in [John] Koza’s world toward simple solutions. His experiment could not have found that distilled equation because it wasn’t structured to do so. Koza tried applying parsimony in other runs but found that parsimony added to the beginning of a run dampened the efficiency of the solutions. He’d find simple but mediocre to poor solutions. He has some evidence that adding parsimony at the end of evolutionary procedure-that is, first let the system find a solution that kind of works and then start paring it down-is a better way to evolve succinct equations.

But Koza passionately believes parsimony is highly overrated. It is, he says, a mere “human esthetic.” Nature isn’t particularly parsimonious. For instance, David Stork, then a scientist at Stanford, analyzed the neural circuits in the muscles of a crayfish tail. The network triggers a curious backflip when the crayfish wants to escape. To humans the circuit looks baroquely complex and could be simplified easily with the quick removal of a couple of superfluous loops. But the mess works. Nature does not simplify simply to be elegant.

When I was at O&A, I called Koza to try to get him to let use his genetic programming techniques for free. He wouldn’t. They were patented. I could probably use them today for free, but I wouldn’t want to. They have failed to prove themselves. Koza is no closer to a genetically programmed system to predict the stock markets. He’s not on the Forbes 400 list, but two guys named Serey Brin and Larry Page are tied for spot #5. They weren’t Koza’s students at Stanford, and at the time this book was written, it would have been laughable for someone to come up with a new type of search that would have beaten the existing players. First-movers were all the rage. Yet another grand theory down the tubes.

[p379] Small changes can be magnified as development unfolds. In this way, morphogenesis skips Darwinian gradualism. This point was made by the Berkeley geneticist Richard Goldschmidt, whose ideas on nongradual evolution were derided and scorned throughout his life. His major work, The Material Basis of Evolution (1940), was dismissed as near-crackpot until Steven Jay Gould began a campaign to resurrect his ideas in the [p380] 1970s. Goldschmidt’s title mirrors a theme of mine here: that evolution is an intermingling of material and information, and that genetic logic cannot be divorced from the laws of material form in which it dwells. (An extrapolation of this idea would be that artificial evolution will run slightly differently from natural evolution as long as it is embedded on a different substrate.)

Goldschmidt spent a unrewarded lifetime showing that extrapolating the gradual transitions of microevolution (red rose to yellow rose) could not explain macroevolution (worm to snake). Instead, he postulated from his work on developing insects that evolution proceeded by jumps. A small change made early in development would lead to a large change-a monster-at the adult stage. Most radically altered forms would abort, but once in a while, large change would cohere and a hopeful monster would be born. The hopeful monster would have a full wing, say, instead of the halfwinged intermediate form Darwinian theory demanded. Organisms could arrive fully formed in niches that a series of partially formed transitional species would never get to. The appearance of hopeful monsters would also explain the real absence of transitional forms in fossil lineages.

Goldschmidt made the intriguing claim that his hopeful monsters could most easily be generated by small shifts in developmental timing. He found “rate genes” that controlled the timing of local growth and differentiation processes. For instance, a tweak in the gene controlling the rates of pigmentation would produce caterpillars of wildly different color patterns. As his champion Gould writes, “Small changes early in embryology accumulate through growth to yield profound differences among adults …. Indeed, if we do not invoke discontinuous change by small alterations in rates of development, I do not see how most major evolutionary transitions can be accomplished at all.”

[p385] These fruitful questions about the constitutional laws of evolution are being asked, not in biological terms, but in the language of a new science, the science of complexity. Biologists find it most grating that the impetus for this postdarwinian convergence comes chiefly from mathematicians, physicists, computer scientists, and whole systems theorists-people who couldn’t tell the difference between Cantharellus cibarius and Amanita muscaria (one of them a deadly mushroom) if their lives depended on it. Naturalists have had nothing but scorn for those so willing to simplify nature’s complexity into computer models, and to disregard the conclusions of that most awesome observer of nature, Charles Darwin.

Of Darwin’s insights, Darwin himself reminded readers in his update to the third edition of Origin of Species:

As my conclusions have lately been much misrepresented, and it has been stated that I attribute the modification of species exclusively to natural selection, I may be permitted to remark that in the first edition of this work, and subsequently, I place in a most conspicuous position-namely at the close of the Introduction-the following words: “I am convinced that natural selection has been the main, but not the exclusive means of modification.” This has been of no avail. Great is the power of steady misrepresentation.

Neodarwinism presented a wonderful story of evolution through natural selection, a just-so story whose logic was impossible to argue with: since natural selection could logically create all things, all things were created via natural selection. AJ; long as the argument was over the history of our one life on Earth, one had to settle for this broad interpretation unless inarguable evidence would come along to prove otherwise.

It has not yet come. The clues I present here of symbiosis, directed mutation, saltationism, and self-organization, are far from conclusive. But they are of a pattern: that evolution has multiple components in addition to natural selection. And furthermore, these bits and questions are being stirred up by a bold and daring vision: to synthesize evolution outside of biology.


“You can’t ask the experimental question until, roughly speaking, the intellectual framework is in place. So the critical thing is asking importan questions,” [Stuart] Kauffman warned me. Often during our conversations, I’d catch Kauffman thinking aloud. He’d spin off wild speculations and then seize one and twirl it around to examine it from various directions. “How do you ask that question?” he asked himself rhetorically. His quest was for the Question of All Questions rather than the Answer of All Answers. “Once you’ve asked the question,” he said, “there’s a good chance of finding some sort of answer.

A Question Worth Asking. That’s what Kauffman thought of his notion of self-organized order in evolutionary systems. Kauffman confided to me:

“Somehow, each of us in our own heart is able to ask questions that we think are profound in the sense that the answer would be truly important. The enormous puzzle is why in the world any of us ask the questions that we do.”

The essence of programming is asking the right questions, not seeking the right answers (tools).

[p401] The art of evolution is the art of managing dynamic complexity. Connecting things is not difficult; the art is finding ways for them to connect in an organized, indirect, and limited way.

[p417] The evolution of evolution does not mean merely that the mutation rate is evolving, although it could entail this. In fact, the mutation rate is remarkably constant over time throughout not only the organic world but also the world of machines and hyperlife. (It is rare for mutation rates to go above a few percent and rare for them to drop below a hundredth of a percent. Somewhere around a tenth of a percent seems to be ideal. That means that a nonsensical wild idea once in a thousand is all that is needed to keep things evolving. Of course one in a thousand is pretty wild for some places.)

Natural selection tends to maintain a mutation rate for maximal evolv-, ability. But for the same advantage, natural selection will move all parameters of a system to the optimal point where further natural selection can take place. However that point of optimal evolvability is a moving target shifted by the very act of reaching for it. In one sense, an evolutionary system is stable because it continually returns itself to the preferred state of optimal evolvability. But because that point is moving-like a chameleon’s colors on a mirror-the system is perpetually in disequilibrium.

The genius of an evolutionary system is that it is a mechanism for generating perpetual change. Perpetual change does not mean recurrent change, as the kaleidoscope of pedestrian action on a street corner may be said to endure perpetual change. That’s really perpetual dynamism. Perpetual change means persistent disequilibrium, the permanent almost-fallen state. It means change that undergoes change itself. The result will be a system that is always on the edge of changing itself out of existence.

Or into existence. The capacity to evolve must be evolved itself. Where else did evolution come from in the first place?

If we accept the theory that life evolved from some kind of nonlife, or protolife, then evolution had to precede life. Natural selection is an abiological consequence; it could very well work on protoliving populations. Once fundamental varieties of evolution were operating, more complex varieties kicked in as the complexity of forms allowed. What we witness in the fossil record of Earthly life is the gradual accumulation of various types of simpler evolutions into the organic whole we now call evolution. Evolution is a conglomeration of many processes which form a society of evolutions. As evolution has evolved over time, evolution itself has increased in diversity and complexity and evolvability. Change changes itself.

[p420] I’m sitting on a sofa in the guru’s office. I’ve trekked to this high mountain outpost at one of the planet’s power points, the national research labs at Los Alamos, New Mexico. The office of the guru is decorated in colorful posters of past hi-tech conferences that trace his almost mythical career: from a maverick physics student who formed an underground band of hippie hackers to break the bank at Las Vegas with a wearable computer, to a principal character in a renegade band of scientists who invented the accelerating science of chaos by studying a dripping faucet, to a founding father of the artificial life movement, to current head of a small ‘lab investigating the new science of complexity in an office kitty-corner to the museum of atomic weapons at Los Alamos.

The guru, Doyne Farmer, looks like Ichabod Crane in a bolo tie. Tall, bony, looking thirty-something, Doyne (pronounced Doan) was embarking on his next remarkable adventure. He was starting a company to beat the odds on Wall Street by predicting stock prices with computer simulations.

“I’ve been thinking about the future, and I have one question,” I begin.

“You want to know if IBM is gonna be up or downl” Farmer suggests with a wry smile.

“No. I want to know why the future is so hard to predict.”

“Oh, that’s simple.”

I was asking about predicting because a prediction is a form of control. It is a type of control particularly suited to distributed systems. By anticipating the future, a vivisystem can shift its stance to preadapt to it, and in this way control its destiny. John Holland says, “Anticipation is what complex adaptive systems do. “

Farmer likes to use a favorite example when explaining the anatomy of a prediction. “Here catch this!” he says tossing you a ball. You grab it. ‘‘You know how you caught that?” he asks. “By prediction.”

Deceived by simplicity and lots of zeroes. Can a bee catch a ball? For that matter, can a whole swarm catch a ball? And, catching a ball is a one-body problem. After I finished this book, I went out to have a snowball fight with my kids. Do you realize how hard it is to hit a kid with a snowball even if he is sitting on the ground? We have not evolved to solve the two-body problem all that well. We consider it great skill to be able to hit a standing target with a powerful weapon, and an even greater skill to hit a moving target with that same powerful weapon. Have you ever gone skit shooting with a bow and arrow? The stock market is an N-body problem. Catching a ball has been drilled in over evolutionary time, and it’s a trivial problem. One more point, “If you can dodge a wrench, you can dodge a ball” is a very funny line, just because it is still a hard problem to not get hit by something that is moving at human speeds.

The second issue with this quote is that it begins Chapter 22 titled: Prediction Machinery. Farmer is the “god”, and Kelly is the “supplicant”. Farmer’s insatiable ego fills the beginning of this chapter before Kelly really gets going. Who is Farmer trying to please with the statement: “Oh, that’s simple”? Why trivialize a problem that Kelly has been searching 21 chapters to figure out? To show that Caveman Farmer can wield a club better than Caveman Kelly. What does this have to do with evolution? Nothing and everything. The way we react as the “hive mind” to tree-pissing is pretty much the same as we reacted thousands of years ago. We no longer fear for our physical safety, but when someone tries to pee higher intellectually, we react in the same way: fear. Not to say that Kelly was afraid, but the text above demonstrates his extreme deference to a “god” in the field of prediction. Where is Doyne Farmer today? He’s not on the Forbes 400 list, and he surely was trying to get there at the Prediction Company. Gurus are not evolved, they are created by the hive mind.

[p428] Sure, you can’t predict where the water will go a half-mile downstream, but for five seconds-or five hours on Wall Street-you can predict the unfolding show. That’s all you really need to be useful (or rich). Find any pattern and exploit it. The Prediction Company’s algorithms grab a fleeting bit of order and exploit this ephemeral archetype to make money. Farmer and Packard emphasize that while economists are obliged by their profession to unearth the cause of such patterns, gamblers are not bound so. The exact reason why a pattern forms is not important for the Prediction Company’s purposes. In inductive models-the kind the Prediction Company constructs-the abstracted causes of events are not needed, just as they aren’t needed for an outfielder’s internalized ballistic notions, or for a dog to catch a tossed stick.

[p447] The dream of [Dana] Meadows is the same as that of [Jay] Forrester, the U.S. Command Central wargamers, Farmer and the Prediction Company, and myself, for that matter: to create a system (a machine) that sufficiently mirrors the real evolving world so that this miniature can run faster than real life and thus project its results into the future. We’d like prediction machinery not for a sense of predestiny but for guidance. And ideally it must be a Kauffman or von Neumann machine that can create things more complex that itself.

A machine that can “sufficiently mirror the real evolving world” would need to take into account itself. It would be more than a butterly flying in Brazil. It would be a machine whose output would change the course of events (larger or small), guaranteed. Anything or anyone which could read the output would be able to act or not act on it, and that decision itself would have to be incorporated in the model. Impossible and to which Kelly alludes as follows:

[p449] On Earth, there is no outside platform from which to send an intelligent hand into the vivisystem, and no point inside where a control dial waits to be turned. The direction of large swarm like systems such as human society is controlled by a messy multitude of interconnecting, self-contradictory agents who have only the dimmest awareness of where the whole is at anyone moment. Furthermore, many active members of this swarmy system are not individual human intelligences; they are corporate entities, groups, institutions, technological systems, and even the nonbiological systems of the Earth itself.

The song goes: No one is in charge. We can’t predict the future.

NOTE: But he still believes we can control it at some level.

Now hear the flip side of the album: We are all steering. And we can learn to anticipate what is immediately ahead. To learn is to live.

[p456] Alan Lightman and Owen Gingerich, writing in a 1991 Science article, ‘‘When Do Anomalies Begin?,” claim that contrary to the reigning Kuhnian model of science, “certain scientific anomalies are recognized only after they are given compelling explanations within a new conceptual framework. Before this recognition, the peculiar facts are taken as givens or are ignored in the old framework.” In other words, the real anomalies that eventually overthrow a reigning paradigm are at first not even perceived as anomalies. They are invisible.

[p457] The final section in my book is a short course in what we, or at least I, don’t know about complex adaptive systems and the nature of control. It’s a list of questions, a catalogue of holes. A lot of the questions may seem silly, obvious, trivial, or hardly worth worrying about, even for nonscientists. Scientists in the pertinent fields may say the same: these questions are distractions, the ravings of a amateur science-groupie, the ill-informed musing of a techno-transcendentalist. No matter. I am inspired to follow this unorthodox short course by a wonderful paragraph written by Douglas Hofstadter in an forward to Pentti Kanerva’s obscure technical monograph on sparse distributed computer memory. Hofstadter writes:

I begin with the nearly trivial observation that members of a familiar perceptual category automatically evoke the name of the category. Thus, when we see a staircase (say), no matter how big or small it is, no matter how twisted or straight, no matter how ornamented or plain, modern or old, dirty or clean, the label “staircase” spontaneously jumps to center stage without any conscious effort at all. Obviously, the same goes for telephones, mailboxes, milkshakes, butterflies, model airplanes, stretch pants, gossip magazines, women’s shoes, musical instruments, beach balls, station wagons, grocery stores, and so on. This phenomenon, whereby an external physical stimulus indirectly activates the proper part of our memory, permeates human life and language so thoroughly that most people have a hard time working up any interest in it, let alone astonishment, yet it is probably the most key of all mental mechanisms.

To be astonished by a question no one else can get worked up about, or to be astonished by a matter nobody considers a problem, is perhaps a better paradigm for the progress of science.

This book is based on my astonishment that nature and machines work at all. I wrote it by trying to explain my amazement to the reader. When I came to something I didn’t understand, I wrestled with it, researched, or read until I did, and then started writing again until I came to the next question [p458] tion I couldn’t readily answer. Then I’d do the cycle again, round and round. Eventually I would come to a question that stopped me from writing further. Either no one had an answer, or they provided the stock response and would not see my perplexity at all. These halting questions never seeme& weighty at first encounter-just a question that seems to lead to nowhere for now. But in fact they are protoanomalies. Like Hofstadter’s unappreciated astonishment at our mind’s ability to categorize objects before we recognize them, out of these quiet riddles will come future insight, and perhaps revolutionary understanding, and eventually recognition that we must explain them.


And what is “complexity” anyway? I looked forward to the two 1992 science books identically titled Complexity, one by Mitch Waldrop and one by Roger Lewin, because I was hoping one or the other would provide me with a practical measurement of complexity. But both authors wrote books on the subject without hazarding a guess at a usable definition. How do we know one thing or process is more complex than another? Is a cucumber more complex that a Cadillac? Is a meadow more complex than a mammal brain? Is a zebra more complex than a national economy? I am aware of three or four mathematical definitions for complexity, none of them broadly useful in answering the type of questions I just asked. We are so ignorant of complexity that we haven’t yet asked the right question about what it is.

If evolution tends to grow more complex, why? And if it really does not, then why does it appear to? Is complexity in fact more efficient than simplicity?

[p459] How far can you compress a meadow into seeds? This was the question the prairie restorers inadvertently asked. Can you reduce the treasure of information contained in an entire ecosystem into several bushels of seeds, which, when watered, would reconstitute the awesome complexity of prairie life? Are there important natural systems which simply cannot be reduced and modeled accurately? Such a system would be its own smallest expression, its own model. Are there any artificial large systems that cannot be compressed or abstracted?

I believe that the limits of abstraction are the limits of our minds (or other software) to understand abstraction. We can abstract systems, because we can recognize patterns, and then give them a name. The choice of name is everything, as it is the guide to our adaptive taxonomy of the system itself. Computers don’t need to give names to patterns, because they can use the “long name” (the thing itself) to find the thing they are looking for. The act of naming a bit of code is a requirement for a human programmer to communicate with another human programmer. It compresses communication, not the thing itself, in order to create even greater abstractions. When I use the world LISP, you know what it means, if you are a reasonably versed programmer. A computer simply calls up the interpreter when I mention LISP. It doesn’t understand the abstraction of LISP; it merely understands how to interpret LISP programs.

[p459] I’d like to know more about stability. If we build a “stable” system, is there some way we can define that? What are the boundary conditions, the requirements, for stable complexity? When does change cease to be change?

[p460] Every figure I’ve heard for both natural and artificial self-sustaining systems puts the self-stabilizing mutation rate between 1 percent and 0.01 percent. Are mutation rates universal?

[p461] The ever insightful [Jay David] Bolter writes, “Critics accuse the computer of promoting homogeneity in our society, of producing uniformity through automation, but electronic reading and writing have just the opposite effect.” Computers promote heterogeneity, individualization, and autonomy.

No one has been more wrong about computerization than George Orwell in

  1. So far, nearly everything about the actual possibility-space which computers have created indicates they are the end of authority and not its beginning.

Swarm-works have opened up not only a new writing space for us, but a new thinking space. If parallel supercomputers and online computer networks can do this, what kind of new thinking spaces will future technologies–such as bioengineering-offer us? One thing bioengineering could do for the space of our thinking is shift our time scale. We moderns think in a bubble of about ten years. Our history extends into the past five years and our future runs ahead five years, but no further. We don’t have a structured way, a cultural tool, for thinking in terms of decades or centuries. Tools for thinking about genes and evolution might change this.

It’s an odd term “bubble” in the above, for that is what it was named afterwards. Quite prescient. We don’t have a way of communicating the concept of time in non-human terms. I thought I knew everything about programming while I was at O&A, and then I went to Tandem, and a few other companies before starting bivio. I realize now how little I understand about programming, and how much more I can learn by programming. It’s our nature to not be able to look decades ahead, because the average human lifespan (in organic evolutionary terms) is just a couple of decades.

[p469] C How do you make something from nothing? Although nature knows this trick, we haven’t learned much just by watching her. We have learned more by our failures in creating complexity and by combining these lessons with small successes in imitating and understanding natural systems. So from the frontiers of computer science, and the edges of biological research, and the odd corners of interdisciplinary experimentation, I have compiled The Nine Laws of God governing the incubation of somethings from nothing:

  • Distribute being
  • Control from the bottom up
  • Cultivate increasing returns
  • Grow by chunking
  • Maximize the fringes
  • Honor your errors
  • Pursue no optima; have multiple goals
  • Seek persistent disequilibrium
  • Change changes itself.

These nine laws are the organizing principles that can be found operating in systems as diverse as biological evolution and SimCity. Of course I am not suggesting that they are the only laws needed to make something from nothing; but out of the many observations accumulating in the science of complexity, these principles are the broadest, crispest, and most representative generalities. I believe that one can go pretty far as a god while sticking to these nine rules.

Chapter 24: The Nine Laws of God. Why nine? Why not seventeen? Why 24 chapters? Numbers are not evolutionary. 24 is a nice number so is nine. Are these the only laws that work? I believe programming is pursuing an optimum: eliminating dupclication. Encoding a problem is relatively easy, and I don’t really consider that programming. It’s understanding the solution as a problem to be reduced, and reducing it to its barest solution, while verifiably preserving its existing function. That’s the easy rule of programming and the hard part. It means that we are just automotons trying to do a very simple thing: detect patterns and name them. It’s the last bit that’s the hard part.