Complexity Quotes

“The word "emergence" seemed to crop up frequently. And most of all, there was this incredible energy and camaraderie in the air-a sense of barriers crumbling, a sense of new ideas let loose, a sense of spontaneous, unpredictable, open-ended freedom. In an odd, intellectual sort of way, the artificial life workshop felt like a throwback, like something right out of the Vietnam-era counterculture.

And, of course, in an odd, intellectual sort of way, it was.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“But now Holland was beginning to realize just how prescient Samuel's focus on games had really been. This game analogy seemed to be true of any adaptive system. In economics the payoff is in money, in politics the payoff is in votes, and on and on. At some level, all these adaptive systems are fundamentally the same. And that meant, in turn, that all of them are fundamentally like checkers or chess: the space of possibilities is vast beyond imagining. An agent can learn to play the game better-that's what adaptation is, after all. But it has just about as much chance of finding the optimum, stable equilibrium point of the game as you or I have of solving chess.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“In non linear systems-and the economy is most certainly nonlinear-chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions will often grow inexorably. After a while, your predictions are nonsense.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“Furthermore, Kauffman felt that it might ultimately be possible to apply these ideas far beyond the economy. "I think these kinds of models are the place for contingency and law at the same time," he says. "The point is that the phase transitions may be lawful, but the specific details are not. So maybe we have the starts of models of historical, unfolding processes for such things as the Industrial Revolution, for example, or the Renaissance as a cultural transformation,a nd why it is that an isolated society, or ethos, can't stay isolated when you start plugging some new ideas into it." You can ask the same thing about the Cambrian explosion: the period some 570 million years ago when a world full of algae and pond scum suddenly burst forth with complex, multicellular creatures in immense profusion. "Why all of a sudden do you get all this diversity?" Kauffman asks. "Maybe you had to get to a critical diversity to then explode. Maybe it's because you've gone from algal mats to something that's a little more trophic and complex, so that there's an explosion of processes acting on processes to make new processes. It's the same thing as in an economy.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“Most obviously, they agreed, an autocatalytic set was a web of transformations among molecules in precisely the same way that an economy is a web of transformations among goods and services. In a very real sense, in fact, an autocatalytic set was an economy-a submicroscopic economy that extracted raw materials (the primordial "food" molecules) and converted them into useful products (more molecules in the set).

Moreover an autocatalytic set can bootstrap its own evolution in precisely the same way that an economy can, by growing more and more complex over time. This was a point that fascinated Kauffman. If innovations result from new combinations of old technologies, then the number of possible innovations would go up very rapidly as more and more technologies became available. In fact, he argued, once you get beyond a certain threshold of complexity you can expect a kind of phase transition analogous to the ones he had found in his autocatalytic sets. Below that level of complexity you would find countries dependent upon just a few major industries, and their economies would tend to be fragile and stagnant. In that case, it wouldn't matter how much investment got poured into the country. "If all you do is produce bananas, nothing will happen except that you produce more bananas." But if a country ever managed to diversify and increase its complexity above the critical point, then you would expect it to undergo an explosive increase in growth and innovation-what some economists have called an "economic takeoff."

The existence of that phase transition would also help explain why trade is so important to prosperity, Kauffman told Arthur. Suppose you have two different countries, each one of which is subcritical by itself. Their economies are going nowhere. But now suppose they start trading, so that their economies become interlinked into one large economy with a higher complexity. "I expect that trade between such systems will allow the joint system to become supercritical and explode outward."

Finally, an autocatalytic set can undergo exactly the same kinds of evolutionary booms and crashes that an economy does. Injecting one new kind of molecule into the soup could often transform the set utterly, in much the same way that the economy transformed when the horse was replaced by the automobile. This was part of autocatalysis that really captivated Arthur. It had the same qualities that had so fascinated him when he first read about molecular biology: upheaval and change and enormous consequences flowing from trivial-seeming events-and yet with deep law hidden beneath.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“At the same time, Kaufmann discovered that in developing his genetic networks, he had reinvented some of the most avant-garde work in physics and applied mathematics-albeit in a totally new context. The dynamics of his genetic regulatory networks turned out to be a special case of what the physicists were calling "nonlinear dynamics." From the nonlinear point of view, in fact, it was easy to see why his sparsely connected networks could organize themselves into stable cycles so easily: mathematically, their behavior was equivalent to the way all the rain falling on the hillsides around a valley will flow into a lake at the bottom of the valley. In the space of all possible network behaviors, the stable cycles were like basins-or as the physicists put it, "attractors.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“It didn’t take very long for Arthur to realize that, when it came to real-world complexities, the elegant equations and the fancy mathematics he’d spent so much time on in school were no more than tools—and limited tools at that. The crucial skill was insight, the ability to see connections.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“Living systems are actually very close to this edge-of-chaos phase transition, where things are much looser and more fluid. And natural selection is not the antagonist of self-organization.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“You can go extinct, or broke. But here we are on the edge of chaos because that’s where, on average, we all do the best.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“The royal road to a Nobel Prize has generally been through the reductionist approach,”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“generally treating free-market capitalism as a kind of state religion.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“he had to find out what was important about self-reproduction, independent of the detailed biochemical machinery.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“To me, coming from applied mathematics, a theorem was a statement about an everlasting mathematical truth—not the dressing up of a trivial observation in a lot of formalism.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“I've just reached the age when I don't bother with people I have to make allowances for." Toward”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“The alternative-the complex apporach-is total Taoist. In Taoism there is no inherent order. 'The world started with one, and the one became two, and the two became many, and the many led to myriad things.' The universe in Taoism is perceived as vast, amorphous, and ever-changing. You can never nail it down. The elements always stay the same, yet they're always rearranging themselves. So it's like a kaleidoscope: the world is a matter of patterns that change, that partly repeat, but never quite repeat, that are always new and different.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“Langton's impression was that evolutionary theories of culture still carried a stigma from the time of social Darwinism in the nineteenth century when people were defending both war and gross social inequity on the grounds of "the survival of the fittest." But while he could certainly see the problem-after all, he'd been protesting war and social inequity most of his life-he just couldn't accept the gaping hole. If you could create a real theory of cultural evolution, as opposed to some pseudoscientific justification for the status quo, he reasoned, then you might be able to understand how cultures really worked-and among other things, actually do something about war and social inequity.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“But now, says Holland, look what happens with that 1000-gene seaweed when you assume that the genes are not independent. To be sure of finding the highest level of fitness in this case, natural selection would now have to examine every conceivable combination of genes, because each combination potentially has a different fitness. And when you work out the total number of combinations, it isn't 2 multiplied by 1000. It's 2 multiplied by itself 1000 times. that's 2^1000 , or about 10^300- a number so vast that it makes even the number of moves in chess seem infinitesimal. "Evolution can' even begin to try out that many things," says Holland. "And no matter how good we get with computers, we can't do it." Indeed, if every elementary particle in the observable universe were a supercomputer that had been number-crunching away since the Big Bang, they still wouldn't be close. And remember, that's just for seaweed. Humans and other mammals have roughly 100 times as many genes-and most of those genes come in many more than two varieties.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“To Holland, evolution and learning seemed much more like-well, a game. In both cases, he thought, you have an agent playing against its environment, trying to win enough of what it needed to keep going. In evolution that payoff is literally survival, and a chance for the agent to pass its genes on to the next generation. In learning, the payoff is a reward of some kind, such as food, a pleasant sensation, or emotional fulfillment. But either way, the payoff (or lack of it) gives agents the feedback they need to improve their performance: if they're going to be "adaptive" at all, they somehow have to keep the strategies that pay off well, and let the others die out.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“A single gene for green eyes isn't worth very much unless it's backed up by the dozens or hundreds of genes that specify the structure of the eye itself. Each gene had to work as part of a team, realized Holland. And any theory that didn't take that fact into account was missing a crucial part of the story. Come to think on it, that was also what Hebb had been saying in the mental realm. Hebb's cell assemblies were a bit like genes, in that they were supposed to be the fundamental units of thought. But in isolation the cell assemblies were almost nothing. A tone, a flash of light, a command for a muscle twitch-the only way they could mean anything was to link up into larger concepts and more complex behaviors.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“But to Holland, the concept of prediction and models actually ran far deeper than conscious thought-or for that matter, far deeper than the existence of a brain. "All complex, adaptive systems- economies, minds, organisms-build models that allow them to anticipate the world," he declares. Yes, even bacteria. As it turns out, says Holland, many bacteria havve special enzyme systems that cause them to swim toward stronger concentrations of glucose. Implicitly, those enzyme model a crucial aspect of the bacterium's world: that chemicals diffuse outward from their source, growing less and less concentrated with distance. And the enzymes simultaneously encode an implicit prediction: If you swim toward higher concentrations, then you're likely to find something nutritious. "It's not a conscious model or anything of that sort," says Holland. "But it gives that organism an advantage over one that doesn't follow the gradient.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“He sat on a rock by the falls, thinking about his autocatalysis work and what it meant. "And suddenly," he says, "I knew that God had revealed to me a part of how his universe works." Not a personal God, certainly; Kauffman had never been able to believe in such a being. "But I had a holy sense of a knowing universe, a universe unfolding, a universe of which we are privileged to be a part. In fact, it was quite the opposite of a vainglorious feeling. I felt that God would reveal how the world works to anyone who cared to listen.

"It was a lovely moment," he says, "the closest I've ever come to a religious experience.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“And when he finally plotted it all up, there it was: the number of cell types in an organism did indeed scale roughly as the square root of the number of genes it had.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“All right, thought Kauffman, imagine that you had a primordial soup containing some molecule A that was busily catalyzing the formation of another molecule B. the first molecule probably wasn't a very effective catalyst, since it essentially formed at random. But then, it didn't need to be very effective. Even a feeble catalyst would have made B-type molecules form faster than they would have otherwise.

Now, thought Kauffman, suppose that molecule B itself had a weak catalytic effect, so that it boosted the production of some molecule C. And suppose that C also acted as a catalyst, and so on. If the pot of primordial soup was big enough, he reasoned, and if there were enough different kinds of molecules in there to start with, then somewhere down the line you might very well have found a molecule Z that closed the loop and catalyzed the creation of A. But now you would have had more A around, which means that there would have been more catalyst available to enhance the formation of B, which then would have enhanced the formation of C, and on and on.

In other words, Kauffman realized, if the conditions in your primordial soup were right, then you wouldn't have to wait for random reactions at all. The compounds in the soup could have formed a coherent, self-reinforcing web of reactions. Furthermore, each molecule in the web would have catalyzed the formation of other molecules in the web-so that all the molecules in the web would have steadily grown more and more abundant relative to molecules that were not part of the web. Taken as a whole, in short, the web would have catalyzed its own formation. It would have been an "autocatalytic set.”
― M. Mitchell Waldropll Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos

“If the origin of life had really been a random event , then it had really been a miracle.”
― M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos