Dembski and Displacement

I've been requested to comment on a relatively recent paper by Dembski,
Searching Large Spaces : Displacement and the No Free Lunch Regress, in which he essentially attempts to use his "No Free Lunch" theorem to show that something like evolution requires an intelligent guide.

Frankly, I'm pretty tired of talking about Dembski; but I think this paper is interesting in a few ways: it's blatantly dishonest in a way that's unusual even for Dembski; it demonstrates some classic creationist misdirections; and it demonstrates what the ultimate point of NFL is for Dembski.

The Blatant Dishonesty: Uniformity

As usual, Dembski is using his No Free Lunch (NFL) theorems as a way of arguing that evolution couldn't possibly have produced life as it exists on earth. But it's got the same problem as all of this other NFL stuff; in fact, it's got the same problem in an even more extreme form. Dembski's argument models evolution as a search function; and it treats the search space as perfectly uniform.

Yeah, if you've got a perfectly uniform search space, and you want to search it with a fixed search function, then guess what: the probability of your search finding its target is damn poor. Yippee. Not exactly a profound statement.

The "novelty" of this paper is that it also considers various kinds of meta-searches: that is, searches where instead of just choosing a search function, you use a search to find a search function well-suited to the search space.

Guess what? If you've got a uniform search space, then searching for a good search function that can find the target doesn't work well: the probability of finding a search function that will find the target even lower than the probability of a random search function finding the target. Going blindly meta on the search isn't better than just doing a blind search; in fact, it's worse.

This is not a profound result.

But the real problem here, the real fundamental dishonesty, is that Dembski continues to use a deliberately ridiculous model - one whose errors have been pointed out to him numerous times, and which he continues to ignore. He models evolution as a search for a specific target in a uniform search space using a single fixed search function. His concession to the criticisms of this model is to allow the search function to be stochastic - which is really no concession at all. (A stochastic search function doesn't pick a direction for the next step of the search; it assigns probabilities to the various directions that you could progress in, where the probabilities are its guesses about how likely you are to find the target in that direction.

Evolution is not a search using a fixed search function over an arbitrary uniform space. The search space is not uniform; the "search function" is not fixed; there is no specific target. It's the same old nonsense: if you create a model of the evolutionary process which eliminates the properties that make evolution work, then you can use that model to show that evolution doesn't work.

Dembski even pulls out that favorite creationist canard: the old "probability of a protein 100 amino acids in length". Dembski knows full well that this is garbage - it's been pointed out to him numerous times. But he doesn't let that stop him - and it's a perfect demonstration of exactly what's wrong with his entire argument:

Take the search for a very modest protein, one that is, say, 100 amino acids in length (most proteins are at least 250 to 300 amino acids in length). The space of all possible protein sequences that are 100 amino acids in length has size 20^100, or approximately 1.27x10^130.

No process in evolution searches for a specific protein. Not every chain of amino acids is even possible. The "probability space" of protein chains is very non-uniform: because of geometric constraints, some chains are very likely; some are very unlikely; and some are impossible. So if you want to model a search for a protein, the search space is not a search for 1 in 20^100; it's a search for some protein which is suited to some purpose (which is likely a family of similar proteins) in a non-uniform search space in which some things are far more likely to appear than others.

This is exactly the problem with all of Dembski's NFL arguments: the insistence for uniform search spaces; the insistence on a single fixed target; and the insistence on a single fixed search function.

Misdirections

The fundamental point of this paper is to try to turn NFL into some kind of support for intelligent design: it wants to argue that blind searches can't work, and that directed searches can work, but only if they have intelligent guidance.

It's a total misdirection. He's structured things so that no search can work unless you know how to get to the target in advance, and then argues that you can't get to the target unless you know it in advance. Phrased in simple english, this is incredibly obvious. But Dembski plays the standard trick of creationists: dazzle you with so much verbose nonsense and impressive-looking math that the tautological nature of the basic argument is hidden.

Here's an example:



What the heck does this mess say? If you used a meta-search to add information to a search function, then you used a meta-search to add information to a search function.

The Point: Dembski's Agenda

This paper is by far the clearest demonstration of exactly what the point of all of Dembski's NFL stuff is about. Dembski wants to argue for intelligent design. He's trying to create a mathematical argument for why there must be an intelligent guide for evolution to work. His agenda for this is to argue that first, there's no way that simple unguided evolution can possibly work. That's the basic NFL stuff. This paper augments that: it tries to show that it can work if the process is guided - but only if the guide has the property that its guidance adds information to a search through its guidance; and finally, he wants to argue that no random process can create a "guide" for search that adds information - the guide must be an intelligent being.

It's intelligent design expressed through mathematical misdirection. By carefully structuring things, and throwing lots of math around, he builds the assumption that an intelligent designer is needed into his framework, and then uses that framework to show that an intelligent designer is needed.