Stuart Kauffman: What kinds of complex systems can evolve by accumulation of successive useful variations? Does selection by itself achieve complex systems able to adapt? Are there lawful properties characterizing such complex systems? The overall answer may be that complex systems constructed so that they're on the boundary between order and chaos are those best able to adapt by mutation and selection.
Chaos is a subset of complexity. It's an analysis of the behavior of continuous dynamical systems — like hydrodynamic systems, or the weather — or discrete systems that show recurrences of features and high sensitivity to initial conditions, such that very small changes in the initial conditions can lead a system to behave in very different ways. A good example of this is the so called butterfly effect: the idea is that a butterfly in Rio can change the weather in Chicago. An infinitesimal change in initial conditions leads to divergent pathways in the evolution of the system. Those pathways are called trajectories. The enormous puzzle is the following: in order for life to have evolved, it can't possibly be the case that trajectories are always diverging. Biological systems can't work if divergence is all that's going on. You have to ask what kinds of complex systems can accumulate useful variation.
We've discovered the fact that in the evolution of life very complex systems can have convergent flow and not divergent flow. Divergent flow is sensitivity to initial conditions. Convergent flow means that even different starting places that are far apart come closer together. That's the fundamental principle of homeostasis, or stability to perturbation, and it's a natural feature of many complex systems. We haven't known that until now. That's what I found out twenty-five years ago, looking at what are now called Kauffman models — random networks exhibiting what I call "order for free."
Complex systems have evolved which may have learned to balance divergence and convergence, so that they're poised between chaos and order. Chris Langton has made this point, too. It's precisely those systems that can simultaneously perform the most complex tasks and evolve, in the sense that they can accumulate successive useful variations. The very ability to adapt is itself, I believe, the consequence of evolution. You have to be a certain kind of complex system to adapt, and you have to be a certain kind of complex system to coevolve with other complex systems. We have to understand what it means for complex systems to come to know one another — in the sense that when complex systems coevolve, each sets the conditions of success for the others. I suspect that there are emergent laws about how such complex systems work, so that, in a global, Gaia- like way, complex coevolving systems mutually get themselves to the edge of chaos, where they're poised in a balanced state. It's a very pretty idea. It may be right, too.
My approach to the coevolution of complex systems is my order-for-free theory. If you have a hundred thousand genes and you know that genes turn one another on and off, then there's some kind of circuitry among the hundred thousand genes. Each gene has regulatory inputs from other genes that turn it on and off. This was the puzzle: What kind of a system could have a hundred thousand genes turning one another on and off, yet evolve by creating new genes, new logic, and new connections?
Suppose we don't know much about such circuitry. Suppose all we know are such things as the number of genes, the number of genes that regulate each gene, the connectivity of the system, and something about the kind of rules by which genes turn one another on and off. My question was the following: Can you get something good and biology-like to happen even in randomly built networks with some sort of statistical connectivity properties? It can't be the case that it has to be very precise in order to work — I hoped, I bet, I intuited, I believed, on no good grounds whatsoever — but the research program tried to figure out if that might be true. The impulse was to find order for free. As it happens, I found it. And it's profound.
One reason it's profound is that if the dynamical systems that underlie life were inherently chaotic, then for cells and organisms to work at all there'd have to be an extraordinary amount of selection to get things to behave with reliability and regularity. It's not clear that natural selection could ever have gotten started without some preexisting order. You have to have a certain amount of order to select for improved variants.
Think of a wiring diagram that has ten thousand light bulbs, each of which has inputs from two other light bulbs. That's all I'm going to tell you. You pick the inputs to each bulb at random, and put connecting wires between them, and then assign one of the possible switching rules to each of the light bulbs at random. One rule might be that a light bulb turns on at the next moment if both of its inputs are on at the previous moment. Or it might turn on if both of its inputs are off.
If you go with your intuition, or if you ask outstanding physicists, you'll reach the conclusion that such a system will behave chaotically. You're dealing with a random wiring diagram, with random logic — a massively complex, disordered, parallel- processing network. You'd think that in order to get such a system to do something orderly you'd have to build it in a precise way. That intuition is fundamentally wrong. The fact that it's wrong is what I call "order for free."
There are other epistemological considerations regarding "order for free." In the next few years, I plan to ask, "What do complex systems have to be so that they can know their worlds?" By "know" I don't mean to imply consciousness; but a complex system like the E. coli bacterium clearly knows its world. It exchanges molecular variables with its world, and swims upstream in a glucose gradient. In some sense, it has an internal representation of that world. It's also true that IBM in some sense knows its world. I have a hunch that there's some deep way in which IBM and E. coli know their worlds in the same way. I suspect that there's no one person at IBM who knows IBM's world, but the organization gets a grip on its economic environment. What's the logic of the structure of these systems and the worlds that they come to mutually live in, so that entities that are complex and ordered in this way can successfully cope with one another? There must be some deep principles.
For example, IBM is an organization that knows itself, but I'm not quite talking about Darwinian natural selection operating as an outside force. Although Darwin presented natural selection as an external force, what we're thinking of is organisms living in an environment that consists mostly of other organisms. That means that for the past four billion years, evolution has brought forth organisms that successfully coevolved with one another. Undoubtedly natural selection is part of the motor, but it's also true that there is spontaneous order.
By spontaneous order, or order for free, I mean this penchant that complex systems have for exhibiting convergent rather than divergent flow, so that they show an inherent homeostasis, and then, too, the possibility that natural selection can mold the structure of systems so that they're poised between these two flows, poised between order and chaos. It's precisely systems of this kind that will provide us with a macroscopic law that defines ecosystems, and I suspect it may define economic systems as well.
While it may sound as if "order for free" is a serious challenge to Darwinian evolution, it's not so much that I want to challenge Darwinism and say that Darwin was wrong. I don't think he was wrong at all. I have no doubt that natural selection is an overriding, brilliant idea and a major force in evolution, but there are parts of it that Darwin couldn't have gotten right. One is that if there is order for free — if you have complex systems with powerfully ordered properties — you have to ask a question that evolutionary theories have never asked: Granting that selection is operating all the time, how do we build a theory that combines self-organization of complex systems — that is, this order for free — and natural selection? There's no body of theory in science that does this. There's nothing in physics that does this, because there's no natural selection in physics — there's self organization. Biology hasn't done it, because although we have a theory of selection, we've never married it to ideas of self-organization. One thing we have to do is broaden evolutionary theory to describe what happens when selection acts on systems that already have robust self-organizing properties. This body of theory simply does not exist.
There are a couple of parallels concerning order for free. We've believed since Darwin that the only source of order in organisms is selection. This is inherent in the French biologist François Jacob's phrase that organisms are "tinkered-together contraptions." The idea is that evolution is an opportunist that tinkers together these widgets that work, and the order you see in an organism has, as its source, essentially only selection, which manages to craft something that will work. But if there's order for free, then some of the order you see in organisms is not due to selection. It's due to something somehow inherent in the building blocks. If that's right, it's a profound shift, in a variety of ways.
The origin of life might be another example of order for free. If you have complex-enough systems of polymers capable of catalytic action, they'll self-organize into an autocatalytic system and, essentially, simply be alive. Life may not be as hard to come by as we think it is.
There are some immediate possibilities for the practical application of these theories, particularly in the area of applied molecular evolution. In l985, Marc Ballivet and I applied for a patent based on the idea of generating very, very large numbers of partly or completely random DNA sequences, and therefrom RNA sequences, and from that proteins, to learn how to evolve biopolymers for use as drugs, vaccines, enzymes, and so forth. By "very large" I mean numbers on the order of billions, maybe trillions of genes — new genes, ones that have never before existed in biology. Build random genes, or partly random genes. Put them into an organism. Make partly random RNA molecules; from that make partly random proteins, and learn from that how to make drugs or vaccines. Within five years, I hope we'll be able to make vaccines to treat almost any disease you want, and do it rapidly. We're going to be able to make hundreds of new drugs.
A related area is that probably a hundred million molecules would suffice as a roughed-in universal toolbox, to catalyze any possible reaction. If you want to catalyze a specific reaction, you go to the toolbox, you pull out a roughed-in enzyme, you tune it up by some mutations, and you catalyze any reaction you want. This will transform biotechnology. It will transform chemistry.
There are also connections to be made between evolutionary theory and economics. One of the fundamental problems in economics is that of bounded rationality. The question in bounded rationality is, How can agents who aren't infinitely rational and don't have infinite computational resources get along in their worlds? There's an optimizing principle about precisely how intelligent such agents ought to be. If they're either too intelligent or too stupid, the system doesn't evolve well.
Economist colleagues and I are discussing the evolution of a technological web, in which new goods and services come into existence and in which one can see bounded rationality in a nonequilibrium theory of price formation. It's the next step toward understanding what it means for complex systems to have maps of their world and to undertake actions for their own benefit which are optimally complex or optimally intelligent — boundedly rational. It's also part of the attempt to understand how complex systems come to know their world.
Excerpted from The Third Culture: Beyond the Scientific Revolution by John Brockman (Simon & Schuster, 1995) . Copyright © 1995 by John Brockman. All rights reserved.