Complexity: The Emerging Science at the Edge of Order and Chaos

by M. Mitchell Waldrop

complex behavior need not have complex roots,"

the way to achieve lifelike behavior is to simulate populations of simple units instead of one big complex unit. Use local control instead of global control. Let the behavior emerge from the bottom up, instead of being specified from the top down. And while you're at it, focus on ongoing behavior instead of the final result. As Holland loved to point out, living systems never really settle down.

Key point for conclusion of progressivism piece.

genotype—the genetic blueprint encoded in its DNA—and its phenotype—the structure that is created from those instructions.

Now, what's beautiful about all this, said Langton, is that once you've made the link between life and computation, you can bring an immense amount of theory to bear. For example, Why is life quite literally full of surprises? Because, in general, it is impossible to start from a given set of GTYPE rules and predict what their PTYPE behavior will be—even in principle. This is the undecidability theorem, one of the deepest results of computer science: unless a computer program is utterly trivial, the fastest way to find out what it will do is to run it and see. There is no general-purpose procedure that can scan the code and the input and give you the answer any faster than that. That's why the old saw about computers only doing what their programmers tell them to do is both perfectly true and virtually irrelevant; any piece of code that's complex enough to be interesting will always surprise its programmers. That's why any decent software package has to be endlessly tested and debugged before it is released—and that's why the users always discover very quickly that the debugging was never quite perfect. And, most important for artificial life purposes, that's why a living system can be a biochemical machine that is completely under the control of a program, a GTYPE, and yet still have a surprising, spontaneous behavior in the PTYPE.

First, says Farmer, this putative law would have to give a rigorous account of emergence: What does it really mean to say that the whole is greater than the sum of its parts? "It's not magic," he says. "But to us humans, with our crude little human brains, it feels like magic."

Humans likewise interact with each other to satisfy less quantifiable goals, thereby forming families, religions, and cultures. Somehow, by constantly seeking mutual accommodation and self-consistency, groups of agents manage to transcend themselves and become something more. The trick is to figure out how, without falling back into sterile philosophizing or New Age mysticism.

connectionism:

the idea of representing a population of interacting agents as a network of "nodes" linked by "connections."

This might be where I want to start on the long run essay.

In a neural network, of course, the node-and-connection structure is obvious. The nodes correspond to neurons, and the connections correspond to synapses linking the neurons.

But perhaps the most important reason for having a common framework, says Farmer, is that it helps you distill out the essence of the models, so that you can focus on what they actually have to say about emergence. And in this case, the lesson is clear: the power really does lie in the connections. That's what gets so many people so excited about connectionism. You can start with very, very simple nodes—linear "polymers," "messages" that are just binary numbers, "neurons" that are essentially just on-off switches—and still generate surprising and sophisticated outcomes just from the way they interact.

the connections encode the GTYPE of the network. So to modify the system's PTYPE behavior, you simply have to change those connections. In fact, says Farmer, you can change them in two different ways. The first way is to leave the connections in place but modify their "strength."

The second, more radical way of adjusting the connections is to change the network's whole wiring diagram. Rip out some of the old connections and put in new ones. This corresponds to what Holland calls exploration learning:

you should look at systems in terms of how they behave instead of how they're made. And when you do, he says, then what you find are the two extremes of order and chaos. It's a lot like the difference between solids, where the atoms are locked into place, and fluids, where the atoms tumble over one another at random. But right in between the two extremes, he says, at a kind of abstract phase transition called "the edge of chaos," you also find complexity: a class of behaviors in which the components of the system never quite lock into place, yet never quite dissolve into turbulence, either.