"Although the monkey/Shakespeare model is
useful for explaining the distinction between
single-step selection and cumulative
selection, it is misleading in important ways.
One of these is that each generation
of selective "breeding", the mutant "progeny"
phrases were judged according to the criterion
of resemblance to a *distant ideal* target,
the phrase METHINKS IT IS LIKE A WEASEL. Life
isn't like that. Evolution has no long-term
goal. There is no long-distance target, no
final perfection to serve as a criterion for
selection, although human vanity cherishes the
absurd notion that our species is the
final goal of evolution." (Dawkins, R., 1996, "The
Blind Watchmaker", pp. 50, original emphasis)

"NFL says that when you average the
performance of an algorithm over all "cost
functions" of a problem, it performs no better
on average than blind search. That is for
*any* algorithm, not just evolutionary
computation (which Dembski likes to imply).
This goes to early claims that certain forms of
evolutionary computation could be considered
as general problem-solvers that could be
deployed without much domain knowledge of a
problem. NFL says that if you are concerned
about the relative efficiency of getting a
solution, you have to apply domain knowledge
of the problem and cost function to select an
algorithm with good performance on that
problem and cost function. NFL isn't about
essential capacity of an algorithm to produce
a solution; it is about comparative efficiency
of algorithms in producing solutions.

It's my opinion that Dembski misconstrues or
misunderstands what the NFL theorems say.
I've passed word along that Dembski's choice
of "No Free Lunch" for the title of a book
that is due out this fall sets him up for
embarrassment. That's still the title, so far
as I know. It will be interesting to see
how the reviews turn out. The introduction to the
book is online at ."
(Wesley R. Elsberry, [online here])

.
[...]
META> Permission is granted to reproduce this e-mail
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META> 1998, 1999. William Grassie.
-------------------------------------------------------

-------------------------------------------------------
APPENDIX:

This is my attempt to calculate the specified
complexity of the sequence produced by Dawkins'
"Weasel applet". Throughout, remember that I'm
calculating specified complexity, *not* running it
through the explanatory filter.

"METHINKS*IT*IS*LIKE*A*WEASEL" is the event E to be
explained. It is specified with regard to <"E IS AN
English sentence from Hamlet",*> (see chp. 5 in TDI).
Whether Dembski's concept of specification is really
sound is questioned by Fitelson, Stephens & Sober in
<http://www.arn.org/docs/dembski/wd_wisconsinureview.htm>,
but I am here more concerned with Dembski's other
claims, and is willing to accept, for the moment, that
his specification criterion really *is* sound.

In addition to being specified, E also is of a certain
complexity. E is 28 symbols long, and if we assume the
only possible symbols are the 26 letters in the
English alphabet as well as the asterix (*) for a
total of 27 symbols, it is obvious that there are
28^27 ways that E *could* have been.

Now for the complexity-calculation. At the beginning
of his meta-post, Dembski caracterizes "complexity" as
requiring "a complicated instruction-set to
characterize". If we assume that no shorter
instruction-sets characterize
"METHINKS*IT*IS*LIKE*A*WEASEL", we can now begin.

Following Dembski's advice in Section 6.1 of
"Intelligent Design", I will now calculate the
complexity of event E in terms of propability.
Although Dembski is anything but clear on whether I
should use one chance hypoethesis or many, I will here
use only the chance hypothesis A

A: Each of the 28 symbols are randomly
selected from the possible 27 English letters
and the asterix.

, the total set of posibilities U (28^27), and the
desired set W

W: E is METHINKS*IT*IS*LIKE*A*WEASEL

and then calculate P(E|A) (the probability of E given
A):

P(E|A)= W/U= 1/(28^27)= 8.45 X 10^-40

Taking the negative logarithm of 2 for this (as
advocated in Section 6.1 of "Intelligent Design") gets
us about 130 bits. While not as high as the bound of
500 bits proposed in Section 6.3 of "Intelligent
Design", it is still considerably higher than the puny
36 bits contained in Dembski's favourite example of
Caputo cheating with the ballot positions, and I
therefore feel justified in characterizing
METHINKS*IT*IS*LIKE*A*WEASEL as an instance of
specified complexity.

Note again that I have *not* "done the calculation"
wrt the explanatory filter, but only wrt whether E
exhibits specified complexity or not.
-------------------------------------------------------



=====
Morgan

"Evolution is to the social sciences as statues are to
birds: a convenient platform upon which to deposit badly
digested ideas." (Steve Jones, 2000, "Darwin's Ghost", pp.
xxvii)


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