Out Of Control: The New Biology Of Machines, Social Systems, And The Economic World

by Kevin Kelly

This is a universal law of vivisystems: higher-level complexities cannot be inferred by lower-level existences.

Certain physical attributes such as temperature depend on collective behavior. A single molecule floating in space does not really have a temperature. Temperature is more correctly thought of as a group characteristic that a population of molecules has. Though temperature is an emergent property, it can be measured precisely, confidently, and predictably. It is real.

As we wire ourselves up into a hivish network, many things will emerge that we, as mere neurons in the network, don’t expect, don’t understand, can’t control, or don’t even perceive. That’s the price for any emergent hive mind.

The second important aspect of generic distributed control is that the chunking of control must be done incrementally from the bottom. It is impossible to take a complex problem and rationally unravel the mess into logical interacting pieces. Such well-intentioned efforts inevitably fail. For example, large companies created ex nihilo, as in joint ventures, have a remarkable tendency to flop. Large agencies created to solve another department’s problems become problem departments in themselves.

The law is concise: Distributed control has to be grown from simple local control. Complexity must be grown from simple systems that already work.

Listen to these most modern words from the hand of the mystical pundit Lao Tzu, writing in the Tao Teh King 2,600 years ago: Intelligent control appears as uncontrol or freedom. And for that reason it is genuinely intelligent control. Unintelligent control appears as external domination. And for that reason it is really unintelligent control. Intelligent control exerts influence without appearing to do so. Unintelligent control tries to influence by making a show of force.

When GM tests a new car on its track field, it puts the car through its paces at different speeds. It will test how it handles a sharp curve going at 50, 60, 70 mph. To no one’s surprise, the car’s performance varies continuously with the speed. If the car passed the curve test at 50, 60, and 70 mph, then the GM engineers know—without explicit testing—that it will also pass at all the intermediate speeds of 55 and 67 mph. They don’t have to worry that at 55 mph the car will sprout wings or go into reverse. How it behaves at 55 will be some interpolated function of what it does at 50 and 60 mph. A car is a continuous system. Computer software, distributed networks, and most vivisystems are discontinuous systems. In complex adaptive systems you simply can’t rely on interpolated functions. You can have software that has been running reliably for years, then suddenly, at some particular set of values (63.25 mph), kabooml, the system bombs, or something novel emerges.

Cypherpunks intend to level the playing field against centralized computer resources with the Fax Effect. If you have the only fax machine in the world it is worth nothing. But for every other fax installed in the world, your fax machine increases in value. In fact, the more faxes in the world, the more valuable everybody’s fax becomes. This is the logic of the Net, also known as the law of increasing returns. It goes contrary to classical economic theories of wealth based on equilibratory tradeoff. These state that you can’t get something from nothing. The truth is, you can. (Only now are a few radical economics professors formalizing this notion.) Hackers, cypherpunks, and many hi-tech entrepreneurs already know that. In network economics, more brings more. This is why giving things away so often works, and why the cypherpunks want to pass out their tools gratis. It has less to do with charity than with the clear intuition that network economics reward the more and not the less—and you can seed the “more” at the start by giving the tools away.

Some students of the human mind make a strong argument that thinking is a type of evolution of ideas within the brain. According to this argument, all created things are evolved. As I write these words, I have to agree. I began this book not with a sentence formed in my mind but with an arbitrarily chosen phrase, “I am.” Then in unconsciously rapid succession I evaluated a headful of possible next words. I picked one that seemed esthetically fit, “sealed.” After “I am sealed,” I went on to the next word, choosing from among 100,000s of possible ones. Each selected word bred the choices for the next until I had evolved almost a sentence of words. Toward the end of the sentence my choices were constrained somewhat by the words I had already chosen at the beginning, so learning helped the breeding go more quickly. But the first word of the next sentence could have been any word. The end of my book, 150,000 choices away, looked as distant and improbable as the end of the galaxy. A book is improbable. Out of all the books written or to be written in the world, only this book, for instance, would have found the preceding two sentences in a row. Now that I’m in the middle of the book, I’m still evolving the text. What will the next words be that I write in this chapter? In a real sense I don’t know. There are probably billions of possibilities of what they might be, even taking into account the restriction that they must logically follow from the last sentence. Did you guess this...

Parallelism is one of the ways around the inherent stupidity and blindness of random mutations. It is the great irony of life that a mindless act repeated in sequence can only lead to greater depths of absurdity, while a mindless act performed in parallel by a swarm of individuals can, under the proper conditions, lead to all that we find interesting.

“There are only two ways we know of to make extremely complicated things,” says Hillis. “One is by engineering, and the other is evolution. And of the two, evolution will make the more complex.”

The great irony puzzling cognitive scientists is why human consciousness is so unable to think in parallel, despite the fact that the brain runs as a parallel machine. We have an almost uncanny blind spot in our intellect. We cannot innately grasp concepts in probability, horizontal causality, and simultaneous logic. We simply don’t think like that. Instead our minds retreat to the serial narrative—the linear story. That’s why the first computers were programmed in von Neumann’s serial design: because that’s how humans think. And this, again, is why parallel computers must be evolved rather than designed: because we are simpletons when it comes to thinking in parallel. Computers and evolution do parallel; consciousness does serial.

systems that are most adaptive are so loose they are a hairsbreadth away from being out of control. Life, then, is a system that is neither stagnant with noncommunication nor grid-locked with too much communication. Rather life is a vivi-system tuned “to the edge of chaos"—that lambda point where there is just enough information flow to make everything dangerous. Rigid systems can always do better by loosening up a bit, and turbulent systems can always improve by getting themselves a little more organized. Mitch Waldrop explains Langton’s notion in his book Complexity, thusly: if an adaptive system is not riding on the happy middle road, you would expect brute efficiency to push it toward that sweet spot. And if a system rests on the crest balanced between rigidity and chaos, then you’d expect its adaptive nature to pull it back onto the edge if it starts to drift away. “In other words,” writes Waldrop, “you’d expect learning and evolution to make the edge of chaos stable.”

Chess rules of thumb are actually pretty good rules to live by. (Notes to my daughters: Favor moves that increase options; shy away from moves that end well but require cutting off choices; work from strong positions that have many adjoining strong positions. Balance looking ahead to really paying attention to what’s happening now on the whole board.)

The hard part is keeping it simple. Says Farmer, “The more complex the problem is, the simpler the models that you end up having to use. It’s easy to fit the data perfectly, but if you do that you invariably end up just fitting to the flukes. The key is to generalize.”

Neither constancy nor relentless change will support a creation. A good creation, like good jazz, must balance the stable formula with frequent out-of-kilter notes. Equilibrium is death. Yet unless a system stabilizes to an equilibrium point, it is no better than an explosion and just as soon dead. A Nothing, then, is both equilibrium and disequilibrium. A Something is persistent disequilibrium—a continuous state of surfing forever on the edge between never stopping but never falling.