As Aesop didn’t actually say:

The Mice once called a meeting to decide on a plan to free themselves of their enemy, the Cat. At least they wished to find some way of knowing when she was coming, so they might have time to run away. Indeed, something had to be done, for they lived in such constant fear of her claws that they hardly dared stir from their dens by night or day. Many plans were discussed, but none of them was thought good enough. At last a very young Mouse got up and said: “I have a plan that seems very simple, but I know it will be successful. All we have to do is to hang a bell about the Cat’s neck. When we hear the bell ringing we will know immediately that our enemy is coming.” All the Mice were much surprised that they had not thought of such a plan before. But in the midst of the rejoicing over their good fortune, an old Mouse arose and said: “I will say that the plan of the young Mouse is very good. But let me ask one question: Who will bell the Cat?”

More heat than light seems to me to be generated by the demand for IDists to “define CSI” and the equations that are fired back in response. Nobody is disputing that we have plenty of equations.  Here is that bright young mouse, Dembski’s:

χ= –log₂[10¹²⁰ · φ_S(T)·P(T|H)]

The problem seems to me to lie in Belling the Cat.

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at ENV to get up to date on the Law of Conservation of Information:

So, what is the difference between the earlier work on conservation of information and the later? The earlier work on conservation of information focused on particular events that matched particular patterns (specifications) and that could be assigned probabilities below certain cutoffs. Conservation of information in this sense was logically equivalent to the design detection apparatus that I had first laid out in my book The Design Inference (Cambridge, 1998).

In the newer approach to conservation of information, the focus is not on drawing design inferences but on understanding search in general and how information facilitates successful search. The focus is therefore not so much on individual probabilities as on probability distributions and how they change as searches incorporate information. My universal probability bound of 1 in 10^150 (a perennial sticking point for Shallit and Felsenstein) therefore becomes irrelevant in the new form of conservation of information whereas in the earlier it was essential because there a certain probability threshold had to be attained before conservation of information could be said to apply. The new form is more powerful and conceptually elegant. Rather than lead to a design inference, it shows that accounting for the information required for successful search leads to a regress that only intensifies as one backtracks. It therefore suggests an ultimate source of information, which it can reasonably be argued is a designer. I explain all this in a nontechnical way in an article I posted at ENV a few months back titled “Conservation of Information Made Simple” (go here).

So what’s the take-home lesson? It is this: Stephen Meyer’s grasp of conservation of information is up to date. His 2009 book Signature in the Cell devoted several chapters to the research by Marks and me on conservation of information, which in 2009 had been accepted for publication in the technical journals but had yet to be actually published. Consequently, we can expect Meyer’s 2013 book Darwin’s Doubt to show full cognizance of the conservation of information as it exists currently. By contrast, Felsenstein betrays a thoroughgoing ignorance of this literature.

It seems to me that Dembski has still entirely missed the point of Felsenstein’s (and others’) critique of his “Law of Conservation of Information”.  But perhaps it’s worth tackling Dembski’s newer formulations here?  The Dembski-Marks published papers, or perhaps the For Dummies article at ENV he links to above.

(Link to previous discussion of Dembsi’s Specification paper here, now re-un-stickied)

I hadn’t realised that the Biologic Instute had a diary/blog.

February’s article is by Ann Gauger, although it consists largely of quotations from Douglas Axe, and is called Natural Selection’s Reach.

What continues to astonish me about ID proponents is just how ignorant they are of evolutionary theory.  Ann Gauger starts by setting out to address a reader’s query:

A reader wrote us recently to ask why natural selection can’t extract enough information from the fitness landscape to explain complex features.

Well, obviously I dispute the premise of the question, but assuming that the reader and Gauger share the view that natural selection’s “reach” is too limited to “explain complex features”, let’s see how Gauger explains her stance.  She starts by rightly saying that:

It all depends on what you think the fitness landscape looks like

and then lets Axe go on to explain further.  Unfortunately, Axe appears to think it looks like this:

with complex features situated on the high distant peaks, and natural selection only able to move a step at a time, and only upwards.

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has an interview on youtube (well, I assume it’s an interview – only his responses are shown).  Here’s a transcript, with some commentary by me, and no doubt other comments will be forthcoming 🙂

In Darwins’ day we knew very little about cellular chemistry, for one thing, we knew very little about  metabolism, about how cells go about making the chemicals that they need to make the big, the big parts of living cells.  We now understand that in some detail, we also understand about the proteins that do the chemistry of life. These are called enzymes.  We understand how large these enzymes are.  We understand that they are encoded by genes, and we understand how that encoding takes place, that’s called the genetic code.  So, really, you put all that together, we now understand something about digitally encoded information, in cells, encoded in the genome, we understand why it’s there, to code proteins, and how the proteins function to do the chemistry of life.  And we also have the ability to measure, to some degree, how much information is there.

All true, and clearly stated.  No issue from me there.

If you put all that together, we know see something that looks very much like human designs where we use digitally encoded information to accomplish things

Well, maybe.  A little.  One huge difference is that biological “designs” are self-reproducing organisms, and so far human designs are resolutely non-self-reproducing.  In fact, the obvious answer to the alien who finds a watch on a heath, and wonders if it was designed or not, is: “well, does it reproduce?”  If yes, it is probably biological.  If no, it’s probably designed by a person. But I’ll grant Axe his digitality – yes, nucleotides are discrete, and yes, their sequence is determines results in the cell products that go to make cells into reproducing organisms (and reproducing cells within organisms, of course.)

But after this excellent, clearly well informed and well-articulated start, he then adds a comment of mind-boggling ignorance:

and we know that it’s impossible to get information on that scale through a chance process that Darwinism employed.

What?

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A very nice post by Barry at UD struck me as worth reposting here (as I can’t post there), inspired by Neil Rickert:

Phinehas asks Neil Rickert a fascinating question about the supposed direction of evolution.  Neil says he will address it in a separate thread, and I started this one for that purpose.  The rest of the post is Phenehas’ question to Neil:

@Neil I also appreciate the professional tone. I am a skeptic regarding what evolution can actually accomplish. In keeping with your demonstrated patience, I’d be grateful if you would give serious consideration to something that keeps tripping me up. I’ve often thought of natural selection as the heuristic to random mutations’ exhaustive search.

A path-finding algorithm can be aided in finding a path from point A to point B by using distance to B as a heuristic to narrow the search space. Without a heuristic, you are left to blind chance. It is said that evolution has no purpose or goal, so there is no point B. It is also claimed that evolution isn’t simply the result of blind chance, so a heuristic would seem to be required. Somehow, natural selection is supposed to address both of these concerns. Nature selects for fitness, we are told, so somehow we have a heuristic even without a point B.

But what is fitness? How does it work as a heuristic? How is it defined? Evidently, it is all about reproductive success. But how does one measure reproductive success? This is where things get fuzzy for me. Surely evolution is a story about the rise of more and more complex organisms. Isn’t this how the tree of life is laid out? Surely it is the complexity of highly developed organisms that evolution seeks to explain. Surely Mt. Improbable has man near its peak and bacteria near its base. But by what metric is man more successful at reproducing than bacteria? If I am a sponge somewhere between the two extremes, how is a step toward bacteria any less of a point B for me than a step toward man? Why should the fitness heuristic prefer a step upward in complexity toward man in any way whatsoever over a step downward in complexity toward bacteria?

It seems that, under the more obvious metrics for calculating reproductive success, bacteria are hard to beat. Even more, a rise in complexity, if anything, would appear to lead to less reproductive success and not more. So how can natural selection be any sort of heuristic for helping us climb Mt Improbable’s complexity when every simpler organism at the base of the mountain is at least as fit in passing on its genes as the more complex organisms near it’s peak? And without this heuristic, how are we not back to a blind, exhaustive search?

 

Excellent questions.

As Neil Rickert was foolish enough to grant me posting rights, I had better take advantage of the offer before the sysops sensibly change their minds.

Consider an argument used consistently by the ID community: that the natural processes of genetic mutation and environmental selection acting upon the resultant variation are incapable of generating speciation.

Their recurring metaphor is of improbable islands of fitness separated by unbridgeable seas of non-functionality. Even if the hill-climbing capability of “random mutation plus natural selection” is real (and even incorporating unselected allele frequency changes), evolution can’t work because “you can’t get there from here”. For brevity, let me acronymise this argument as CANTSWIM.

CANTSWIM has a great many shortcomings as a metaphor for how evolutionary processes actually work, and you don’t need me to enumerate them. But let me hand over the title deeds to the evolutionary farm, and assume that CANTSWIM is factual.

At the same time, the ID community has adopted the claim made by the leadership of the ENCODE program that >80 per cent of the human genome is functional. The devil, of course, is in the detail of how “functional” is defined.

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