Specified Complexity"

I appologize for the long delay in posting this, but

the last couple of weeks have been rather hectic.

This is my second commentary on ID-texts. This week,

I'm commenting on Dembski's "Why Evolutionary

Algorithms Cannot Generate Specified Complexity",

Dembski's second post on why evolutionary algorithms

can't explain specified complexity, from

<http://www.metanexus.org/archives/message_fs.asp?&listtype=Magazine&ARCHIVEID=3080>

(also online at <http://www.geocities.com/evolutionsteori/IDC/3080.html>).

My last such post can be found at <http://www.geocities.com/evolutionsteori/IDC/001.html>.

Again, note that the original Metaview contains some

=20's and a few =9's, which I have removed. And again,

readers are invited to check for themselves if I have

correctly conveyed the original message by doing so.

-------------------------------------------------------

Metaviews 152: "Specified Complexity" by William

Dembski

grassie@VOICENET.COM William Grassie

Metaviews 152. 1999/11/1. Approximately 3575 words.

BG> Below is another posting from William Dembski at

BG> Baylor University in Waco, TX. Dembski

BG> continues his discussion of evolutionary

BG> algorithms (see Meta 139) and presents a

BG> mathematical argument for why such algorithms

BG> cannot generate specified complexity

Dembski does no such thing. The points of critique

launched in this essay are limited to:

1) Evolutionary algorithms always solve their

problems, setting the probability of success at 1, and

the complexity therefore at 0.

2) Evolutionary algorithms get their "specified

complexity" from the fitness functions, and thus have

not *created* it.

None of these are supported by any kind of

"mathematics", unless one considers any essay with

numbers in it to be "mathematical".

BG> as asserted by Richard Dawkins.

Dembski here continues the practice I also critiqued

in my previous installment of ID-Commentary: Namely,

to only criticize Dawkins' "misleading" Weasel

program, instead of dealing with *real* problems

solved by evolutionary algorithms, as asked by critics

of Dembski. This is especially suspect, since "Why

Evolutionary Algorithms Cannot Generate Specified

Complexity" (and its companion-piece "Explaining

Specified Complexity") is being presented as an *in

principle*-refutation of the possibility of

evolutionary algorithms producing "specified

complexity".

When speaking to the general public, who only know

Dawkins' Weasel program, this tactic might work very

well, but leave more informed skeptics wondering why

Dembski keeps avoiding the *real* challenges, if his

"explanatory filter" really is capable of doing what

has been attributed to it.

BG> A number of equations are presented in the

BG> appendix.

BG>

BG> Dembski concludes that "all the specified

BG> complexity we get out of an evolutionary algorithm

BG> has first to be put into the construction of the

BG> evolutionary algorithm and into the fitness

BG> function that guides the algorithm. Evolutionary

BG> algorithms therefore do not generate or create

BG> specified complexity, but merely harness already

BG> existing specified complexity." I am not sure I

BG> follow the entire argument,

Bill Grassie's confusion is understandable, since

Dembski has a wonderful ability to cloak everything he

says in a highly techincal and intimidating babble.

Therefore, most of my comments will deal with what

Dembski is actually *saying*, trying to "translate"

his impressive-sounding lingo, showing that it often

covers simple and uncontroversial statements.

BG> but I am certainly reminded of my first

BG> programming course as a freshman in college in

BG> 1975, when I clocked 70 hours one week in the lab

BG> trying to code a quick sort algorithm. Some more

BG> teleological interventions would have helped.

BG>

BG> I will entertain responses on the Metaviews list

BG> and try to run some compilation in a week or so.

BG> If you want immediate gratification conversation,

BG> check out the Reiterations List at for a higher

BG> volume, lightly moderated discussion.

BG>

BG> -- Billy Grassie

BG>

WAD> =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

WAD> =-=-= From: bill@desiderius.com (William A.

WAD> Dembski) Subject: Specified Complexity

WAD>

WAD> WHY EVOLUTIONARY ALGORITHMS CANNOT GENERATE

WAD> SPECIFIED COMPLEXITY by William A. Dembski

WAD>

WAD> In my last piece for META, I asserted that

WAD> evolutionary algorithms cannot generate specified

WAD> complexity and motivated this assertion by

WAD> pointing to the failure of Richard Dawkins's well-

WAD> known METHINKS IT IS LIKE A WEASEL example to

WAD> generate specified complexity. My point was that

WAD> Dawkins's evolutionary algorithm converged on

WAD> METHINKS IT IS LIKE A WEASEL with probability

WAD> one, and therefore reduced the complexity of

WAD> generating this sequence to zero. With reference

WAD> to specified complexity, complexity and

WAD> probability are inverse notions: High complexity

WAD> presupposes many live possibilities and

WAD> correspondingly assigns low probability to anyone

WAD> of these possibilities. Thus, while it's true

WAD> that shaking out random scrabble pieces would

WAD> render METHINKS IT IS LIKE A WEASEL highly

WAD> improbable (and therefore complex), Dawkins's

WAD> evolutionary algorithm renders that sequence

WAD> certain and thereby removes its complexity.

WAD>

WAD> Basically, the problem here is one of setting the

WAD> relevant probabilistic context. Within a random-

WAD> scrabble-shaking-scenario, this sequence is

WAD> complex and specified, but within Dawkins's

WAD> evolutionary algorithm it is no longer complex

WAD> (though it remains specified). I therefore

WAD> concluded my last piece by saying that just as

WAD> Darwinian evolution only delivers the

WAD> **appearance** of design (an assertion all

WAD> Darwinists perforce accept), so too it only

WAD> delivers the **appearance** of specified

WAD> complexity.

Dembski forgets the other half of his conlusion: That

his actual/appearant split of "specified complexity"

makes it considerably more difficult to determine

whether life indeed *is* an instance of specified

complexity:

"Does nature exhibit actual specified[...]

complexity? The jury is still out." (Dembski,

W.A., 1999, in Meta #3066)

WAD> In general terms, the problem of generating

WAD> specified complexity via an evolutionary

WAD> algorithm can be conceived as follows. We are

WAD> given a phase space of possible solutions to a

WAD> problem and a fitness function over that phase

WAD> space. Our task is to optimize this fitness

WAD> function by finding a point in the phase space

WAD> that attains a certain level of fitness. Think of

WAD> it this way: The phase space is a vast plane, the

WAD> fitness function is a vast hollowed-out mountain-

WAD> range over the plane (complete with low-lying

WAD> foothills and incredibly high peaks). The task of

WAD> an evolutionary algorithm is by moving around in

WAD> the plane to get to some point under the mountain-

WAD> range where it attains at least a certain height

WAD> (e.g., 10,000 feet). The collection of all such

WAD> places on the plane where the mountain range

WAD> attains at least that height (here 10,000 feet)

WAD> we will call the **target**. Thus the job of the

WAD> evolutionary algorithm is by navigating the phase

WAD> space to find its way into the target (see

WAD> Appendix 1 below).

What Dembski here calls the "phase space" is already

known to readers of Dawkins as "genetic space":

"Imagine a museum with gallaries stretchingIn the case of Dawkins' weasel program, the "phase

towards the horizon in every direction, and as

far as the eye can see upwards and downwards

as well. Preserved in the museum is every kind

of animal form that has ever existed, and

every kind that could be imagined. Each animal

is housed next door to those it most

resembles. Each dimension in the museum -that

is, each dimension along which a gallary

extends- corresponds to one dimension in which

the animals vary. For example, as you walk

north along a particular gallary you notice a

progressive lengthening of the horns of the

speciments in the cabinets. Turn round and

walk south and the horns shorten. Turn and

walk east and that horns stay the same but

something else changes, say the teeth get

sharper. Walk west and the teeth grow blunter.

Since horn length and teeth sharpness are only

two out of thousands of ways in which animals

can vary, the gallaries must criss-cross one

another in many-dimensional space, not just

the ordinary three-dimensional space that we,

with our limited minds, are capable of

visualizing." (Dawkins, R., 1996, "Climbing

Mount Improbable", pp. 200)

space" is 28-dimensional (since there are 28 positions

that can vary), where each dimension is 27 characters

long (since there are 26 letters + space). In the

weasel program, the algorithm can move any numbers of

characters, but is restricted to moving a certain

number of dimensions at a time (kinda' like the tower

in chess, which can only move either back-forth or

left-right, but can move any number of spaces).

WAD> Now, the phase space (which we are picturing as a

WAD> giant plane) usually comes with some additional

WAD> topological structure, typically given by a

WAD> metric or distance function (see Appendix 2).

WAD> This topological structure tells us how points in

WAD> the phase space are related geometrically to

WAD> nearby points.

The concept Dembski is trying to convey is that known

to the biological community as a "fitness landscape",

where increasing altitude stands for increasing

fitness, as defined in terms of reproductive sucess.

In the case of Dawkins' weasel program, the fitness

landscape is a 29-dimensional cone, placed "over" the

28-dimensional "chessboard" (a.k.a. "phase space"). On

the space directly under the top of the cone, the

target sequence ("methinks it is like a weasel") is

written, while the spaces around it are labelled with

sequences very close to it (e.g. "yethinks it is like

a weasel" and "methinks it is like a geasel").

WAD> Also, even though the phase space is huge, it

WAD> tends to be finite (strictly finite for problems

WAD> represented on computer and topologically finite,

WAD> or what topologists call "compact," in general).

This is quite uncontroversial. The number of possible

28-letter sequences, each position with 27 possible

outcomes (28^27 ~ 10^39) *is* "huge, [but] finite".

WAD> Moreover, such spaces typically come with a

WAD> uniform probability that is adapted to the

WAD> topology of the phase space (see Appendix 3).

With respect to Dawkins' weasel program, this pretty

much means that the very first sequence has no more

probability coming up "jhdhonfybyyeev nzyvqqtiilke"

than "xgyhsnszciuhanomqtwlpgwaaumu". I know of no

algorithms, where this is not the case. Dembski's

reason for mentioning this is unclear.

WAD> Basically this means that if you get out your

WAD> tape measure and measure off a three by five foot

WAD> area in one part of the phase space, the uniform

WAD> probability will assign it the same probability

WAD> as a three by five foot area in another portion

WAD> of the phase space. All the spaces to which I've

WAD> seen evolutionary algorithms applied do indeed

WAD> satisfy these two conditions of having a finite

WAD> topological structure (i.e., they are compact)

WAD> and possessing a uniform probability. Moreover,

WAD> this uniform probability is what typically gets

WAD> used to estimate the complexity/improbability of

WAD> the target (i.e., the area of the phase space

WAD> under the mountain range where it attains a

WAD> certain requisite level -- e.g., 10,000 feet).

WAD>

WAD> For instance, in Dawkins's

WAD> METHINKS*IT*IS*LIKE*A*WEASEL example, the phase

WAD> space consists of strings of upper case Roman

WAD> letters and spaces (represented by asterisks) of

WAD> length 28. A uniform probability on this space

WAD> assigns equal probability to each of these

WAD> sequences -- approximately 1 in 10^40. It's this

WAD> improbability that corresponds to the complexity

WAD> of the target sequence and with respect to which

WAD> this target sequence constitutes an instance of

WAD> specified complexity.

Again, Dembski is being very unclear about what

*exactly* he means by "specified complexity". Judging

from the above, one would think that the "complexity"

(i.e. "propability") of a certain event should be

calculated only with respect to a single chance

hypothesis. But in TDI (pp. 50) Dembski says that his

explanatory filter needs to "sweep the field clear of"

*all* chance hypotheses.

This wouldn't be much of a problem, since "specified

complexity" is never even mentioned in TDI. But since

Dembski is constantly referring to specified

complexity as a characteristic feature of "design", as

well as to TDI as his "scholarly argument" for his

ideas, this is unlikely to be anything *but* a

problem.

WAD> In general, given a phase space with a target

WAD> sitting under those places where the mountain

WAD> range attains at least a certain level (e.g.,

WAD> 10,000 feet), the (uniform) probability of

WAD> randomly choosing a point from the phase space

WAD> and landing in the target will be very small. In

WAD> Dawkins's example, the target equals the

WAD> character string METHINKS*IT*IS*LIKE*A*WEASEL and

WAD> the improbability is 1 in 10^40. For non-toy

WAD> examples the improbability is typically much less

WAD> than my universal probability bound of 1 in

WAD> 10^150 that I justify in The Design Inference

WAD> (Cambridge, 1998; cf. section 6.5). Indeed, if

WAD> the probability of the target were not small, a

WAD> random search through the phase space would

WAD> suffice to find a point in the target, and there

WAD> would be no need to construct an evolutionary

WAD> algorithm to find it.

Again, few people would disagree that "methinks it is

like a weasel" is too long to find just by randomly

selecting letters and spaces. Indeed, Dawkins himself

concluded that it "would be a long time coming" before

this would produce the target sequence ("The Blind

Watchmaker", pp. 47).

WAD> We therefore suppose that the target is just a

WAD> tiny portion of the whole phase space; or, in

WAD> slightly more technical language, the (uniform)

WAD> probability of the target in relation to the

WAD> phase space as a whole is exceedingly small.

WAD> What's more, the target, in virtue of its

WAD> explicit identification, is specified (certainly

WAD> this is the case in Dawkins's example where the

WAD> target includes but one point and coincides with

WAD> the character string

WAD> METHINKS*IT*IS*LIKE*A*WEASEL). Thus it would seem

WAD> that to find a point in the target would be to

WAD> generate specified complexity.

But just as Morris and Whitcomb thinks that

radiometric datings only show the "appearent age" of

the Earth, so Dembski believes that the solution

produced is only "appearant specified complexity".

WAD> But let's look deeper. Consider an evolutionary

WAD> algorithm that does in fact find the target. An

WAD> evolutionary algorithm can be conceived as a

WAD> stochastic process that moves around the phase

WAD> space some finite number of times (see Appendix

WAD> 4). Let's call the evolutionary algorithm E. The

WAD> evolutionary algorithm starts at some point E(0)

WAD> in the phase space (usually chosen at random).

WAD> Then it moves to E(1). Then to E(2). Then to E

WAD> (3). Etc. For E successfully to find the target

WAD> (i.e., to find a point under the mountain range

WAD> where it attains at least a certain level --

WAD> e.g., 10,000 feet) then means that within a

WAD> manageable number of steps n, E is very likely to

WAD> land in the target -- i.e., some one of E(0), E

WAD> (1), ..., E(n) is likely to land in the target

WAD> (see Appendix 5). Simply put, the algorithm E has

WAD> to get us into the target with high probability

WAD> and in a relatively short number of steps. In the

WAD> Dawkins example, E(n) rapidly converged to

WAD> METHINKS*IT*IS*LIKE*A*WEASEL for n around 40.

WAD>

WAD> An evolutionary algorithm needs to be contrasted

WAD> with pure random sampling. Pure random sampling

WAD> treats the phase space as a giant urn from which

WAD> we draw items at random according to the uniform

WAD> probability. In that case, a random sample from M

WAD> of size k will contain a point in the target with

WAD> probability better than 1/2 provided that k is

WAD> around the reciprocal of the (uniform)

WAD> probability of the target. Since we are assuming

WAD> that the probability of the target is less than

WAD> my universal probability bound of 1 in 10^150

WAD> given earlier, it follows that k will need to be

WAD> at least 10^150. This number is enormous and far

WAD> exceeds the number of computations conceivable

WAD> for any traditional computer. Moreover, it

WAD> doesn't seem that quantum computation is going to

WAD> render this number tractable either since the

WAD> points in phase space need to be made explicit in

WAD> any random sampling scheme (implying decoherence

WAD> and thus preventing us from exploiting quantum

WAD> superposition).

Since all of the above is the case, both with respect

to Dawkins' weasel program, as well as all examples of

evolutionary algorithms that I am aware of, I am

puzzled as to why Dembski finds it relevant to

mention.

WAD> Let's now return to the evolutionary algorithm E.

WAD> We're going to allow ourselves a certain number

WAD> of steps, call it m, for E to land in the target.

WAD> Clearly m is going to have to be much less than

WAD> 10^150 if we're going to program E on a computer

WAD> and have any hope of E landing in the target.

WAD> With m fixed, we can determine the probability

WAD> that E will land in any subset of phase space in

WAD> m steps (see Appendix 6). For instance, in the

WAD> Dawkins example, for m = 100 and the target

WAD> sequence METHINKS*IT*IS*LIKE*A*WEASEL and E the

WAD> cumulative selection algorithm Dawkins

WAD> constructed, the probability of E attaining the

WAD> target in m = 100 steps is approximately 1.

WAD>

WAD> What this means is that even though with respect

WAD> to the uniform probability on the phase space the

WAD> target has exceedingly small probability, the

WAD> probability for the evolutionary algorithm E to

WAD> get into the target in m steps is no longer

WAD> small. And since complexity and improbability are

WAD> for the purposes of specified complexity parallel

WAD> notions, this means that even though the target

WAD> is complex and specified with respect to the

WAD> uniform probability on the phase space, it

WAD> remains specified but is no longer complex with

WAD> respect to the probability induced by

WAD> evolutionary algorithm E.

Now Demsbki seems to have returned to claiming that

complexity needs to be calculated with regard to *all*

relevant chance hypotheses (as opposed to just the

"uniform probability").

While few would disagree that life is complex with

regard to the chance hypothesis of it being assembled

by throwing random molecules together, it is quite

another matter if it is complex with regard to it

having come about through the actualization of

heritable modifications, exclusion of certain

modifications through differental reproductive

success, and specified through the conditions of the

environment (i.e. natural selection). In fact, whether

this is so is the very point in question, and IDers

are just assuming their conclusion when they claim

that life contains "specified complexity".

WAD> Does this mean that the evolutionary algorithm

WAD> has in fact generated complex specified

WAD> information, but that in referring to a loss of

WAD> complexity with respect to E I'm simply engaging

WAD> in some fancy redefinitions to avoid this

WAD> conclusion? I don't think so. Remember that we

WAD> are interested in the **generation** of specified

WAD> complexity and not in its reshuffling.

This seems to be a complete non sequitur. Dembski

hasn't shown that the "specified complextiy" has been

"reshuffled", and his "reminding us of it" seems

obscure. Indeed, Dembski doesn't even think that there

is any specified complexity to be "reshuffled" to

begin with! According to his argument, the sequence

produced by Dawkins' weasel program doesn't contain

specified complexity because it is produced by

Dawkins' weasel program.

And contrary to Dembski's assertions, he *is* "simply

engaging in some fancy redefinitions", if only with

respect to claims that life contains "specified

complexity".

WAD> To see what's at stake here, we need to be clear

WAD> about a restriction that needs to be placed on E

WAD> if it is to count as a genuine evolutionary

WAD> algorithm (i.e., a legitimate correlative of the

WAD> Darwinian mutation-selection mechanism). It is

WAD> not, for instance, legitimate for E to be able to

WAD> survey the mountain range (i.e., fitness

WAD> landscape), see where in the phase space it

WAD> attains a global maximum, and then head in that

WAD> direction. That would be teleology. No, E must be

WAD> able to navigate its way to the target either by

WAD> randomly choosing points from the phase space or

WAD> by using those as starting points and then

WAD> selecting other points in the phase space based

WAD> **solely** on the topology of the phase space and

WAD> without recourse to the fitness function, except

WAD> to evaluate the fitness function at individual

WAD> points of the phase space already traversed by E.

WAD> In other words, E must move around the phase

WAD> space only on the basis of its topology and the

WAD> elevation of the fitness function at points in

WAD> the phase space already traversed by E.

Of course not! Again, since this doesn't apply to any

of the evolutionary algorithms that Dembski is

supposed to deal with, I am at a loss, trying to

understand why Dembski considers it relevant to

mention.

[...]

WAD> Certainly this means that the evolutionary

WAD> algorithm E is highly constrained in the use it

WAD> can make of the fitness function. But there's

WAD> more. It means that the success of E in hitting

WAD> the target depends crucially on the structure of

WAD> the fitness function.

Finally, Dembski seems to have arrived at his major

criticism of evolutionary algorithms as producers of

specified complexity: They don't produce specified

complexity, but gets it from the fitness function.

WAD> If, for instance, the fitness function is totally

WAD> flat and close to the ground whenever it is

WAD> outside the target, then it fails to discriminate

WAD> between points outside the target and so cannot

WAD> be any help guiding an evolutionary algorithm

WAD> into the target. For such a fitness function, the

WAD> probability of the evolutionary algorithm landing

WAD> in the target is no better than the probability

WAD> of pure random sampling landing in the target,

WAD> which as we know is inadequate to get us there

WAD> (see Appendix 7).

WAD>

WAD> But the problem is even worse. It follows by a

WAD> combinatorial argument that for any partition of

WAD> the phase space into pieces none of which has

WAD> probability more than the probability of the

WAD> target (which by assumption is less than 1 in

WAD> 10^150), for the vast majority of these partition

WAD> elements the probability of the evolutionary

WAD> algorithm E entering them is going to be no

WAD> better than pure random sampling. It follows that

WAD> the vast majority of fitness functions on the

WAD> phase space that coincide with our original

WAD> fitness function on the target but reshuffle the

WAD> function on the partition elements outside the

WAD> target will not land the evolutionary algorithm

WAD> in the target (this result is essentially a

WAD> corollary of the No Free Lunch theorems by

WAD> Wolpert and Macready).

As I also pointed out, last week, Dembski's (mis)use

of Wolpert and Macready's "No Free Lunch theorems" is

bordering on the intellectually dishonest. According

to Wesley, "NFL isn't about essential capacity of an

algorithm to produce a solution; it is about

comparative efficiency of algorithms in producing

solutions." (see my last Commentary)

WAD> Simply put, the vast majority of fitness

WAD> functions will not guide E into the target even

WAD> if they coincide with our original fitness

WAD> function on the target (see Appendix 8).

In order to put Dembski's objection into perspective,

allow me to use a specific example: An evolutionary

biologist might claim that a certain rodent can evolve

longer teeth, if having longer teeth confers a

reproductive advantage: Mutations for longer teeth

appear and are selected for, increasing the specified

complexity of the genome of the offspring of the

rodent (if only with respect to the "uniform

probability").

Dembski's hypothetical response to this would be that,

Yes, natural selection indeed *can* enlarge the teeth

of rodents, thereby increasing the specified

complexity (with respect to the "uniform probability")

of its genome. But since it depends on longer teeth

confering a reproductive advantage, this specified

complexity hasn't really been created, only

"reshuffled".

The creative act of the Intelligent Designer would in

this case be to determine that having longer teeth

would cause the rodent in question to have more

offspring.

One wonders if this is the same Dembski who wrote that

"design ... located in natural laws ... becomes an

empty metaphor":

"But as soon as design is located in naturalThis internal inconsistency on Dembski's part

laws, design becomes an empty metaphor. I know

what it is for a watch to be designed. I only

know what it is for the *process* of making a

watch to be designed in the derivative sense

that I know what it is for a watch to be

designed. Locating design in natural laws has

the effect of reversing this ordinary logic

and thereby vitiating design. If I can't

ascertain that a thing is designed, I can't

ascertain that that the process giving rise to

the thing is designed. Unless we can infer an

intelligent agent from the structure, dynamics

and function of *things*, we are not going to

infer such an agent from the *processes* that

agent supposedly used to bring about those

things. If imputing design to things is

problematic, then imputing design to the

processes that gave rise to those things is

doubly problematic." (Dembski, W.A.,

1999, "Intelligent Design: The Bridge Between

Science and Theology", pp. 78, original

emphasis)

notwithstanding, his objection suffers from serious

problems.

First of all, it is clear that the role of natural

selection in the production of specified complexity

still looms large enough to call into question the

sweeping claims made about Dembski's explanatory

filter having reinstated God within science. Even if

the objection of "Why Evolutionary Algorithms Cannot

Generate Specified Complexity" and the claim that life

contained specified complexity were to be taken at

face value, Dembski's explanatory filter would at most

allow for some sort of deist-god, creating the

universe with the physical constants and mechanis

ms that would make it possible for life to evolve by

purely natural processes. This might be what Dembski

means by "designed", but I doubt that the evangical

Christian community buying his books or in other ways

supporting the ID movement financially will like what

Dembski is saying.

Second, and more importantly, Dembski's objection is

difficult (if not impossible!) to test. At the moment,

we have no idea what causes the natural constants to

be the way they are, and thus, any hope of putting

fitness functions into Dembski's explanatory filter is

a far way into the future. And if those doesn't happen

to be designed either, Dembski can just claim that

whatever caused *them* to be that way must be

designed.

Indeed, there is a problem of infinite regress here.

Whenever the source of whatever feature in question is

offered, Dembski can just lean back and ask "Well, how

did *that* come about?" And since one must always

produce a new source to satisfy him, Dembski can

continue playing this game for as long as he wants (or

until his opponents grow tired of playing with him).

The third problem with Dembski's objection flows

naturally from the second. Earlier this year, Dembski

claimed that "[i]f it could be shown that biological

systems like the bacterial flagellum that are

wonderfully complex, elegant, and integrated could

have been formed by a gradual Darwinian process (which

by definition is non-telic), then intelligent design

would be falsified on the general grounds that one

doesn't invoke intelligent causes when purely natural

causes will do."

(<http://www.discovery.org/viewDB/index.php3?program=CRSC%20Responses&command=view&id=584>)

It could be argued that an unknown Designer, for

whatever reasons, created certain features with an

"appearant evolvability", and that this possible

falsification would only falsify the design of

biological structures anyway (leaving Ross' "fine

tuning argument" safe and sound).

But by assigning specified complexity to the fitness

landscape, Dembski has effectively destroyed any hope

of ID ever being falsifiable (at least with respect to

"wonderfully complex, elegant, and integrated"

"biological systems" being "formed by a gradual

Darwinian process").

Whenever natural selection (or, as in this case,

evolutionary algorithms) is observed producing any of

the things that, according to Dembski, was directly

and supernaturally designed, he can just claim that it

was all "hardwired" in the forces of nature.

[...]

WAD> I have omitted many details. I have also omitted

WAD> some complications which to my mind make the

WAD> problem of generating specified complexity via

WAD> evolutionary algorithms even more problematic (in

WAD> nature, for instance, the fitness function will

WAD> not stay fixed but vary over time).

Dembski needs to show why this is "problematic" (as

oppposed to "easy" or "indifferent").

WAD> Some of the details are treated in chapter 6 of

WAD> my recently released Intelligent Design: The

WAD> Bridge Between Science & Theology (InterVarsity).

As already reported, "Intelligent Design" contains

little, if any, new material. It merely repeats

Dembski's assertion that any information "created" by

any un-intelligent processes must be contained in the

process from the start.

WAD> A full treatment will have to await a book I'm

WAD> currently writing (Redesigning Science: Why

WAD> Specified Complexity Is a Reliable Empirical

WAD> Marker of Actual Design).

According to Nelson, this book was renamed to "No Free

Lunch":

From

<http://www.calvin.edu/archive/evolution/200006/0123.html>:

-------------------------------------------------------

From: Paul Nelson (pnelson2@ix.netcom.com)

Date: Mon Jun 19 2000 - 21:22:58 EDT

[...]

Bill Dembski sent me the following note, saying he

had tried to e-mail Wesley but the mail bounced

back as undeliverable. Anyway, here's Bill's

reply to Wesley's question about Bill's new book,

"Redesigning Science":

> It's about two-thirds completed. The working title now

> is _No Free Lunch_. I'm in touch with a publisher.

> I expect it will be out late 2001.

>

> --Bill

[...]

Paul Nelson

Senior Fellow

The Discovery Institute

www.discovery.org/crsc

-------------------------------------------------------

According to Amazon.com, it will be published November

this year:

From

<http://www.amazon.com/exec/obidos/ASIN/0742512975/qid=1002053488/sr=2-1/ref=sr_8_5_1/103-7752700-2613463>:

-------------------------------------------------------

No Free Lunch : Why Specified Complexity Cannot Be

Purchased Without Intelligence

by William A. Dembski

List Price: $35.00

Our Price: $35.00

This item will be published in November 2001. You may

order it now and we will ship it to you when it

arrives.

Hardcover - 336 pages (November 2001)

Rowman & Littlefield; ISBN: 0742512975

[...]

-------------------------------------------------------

It will be interesting to see if *this* book contains

the "in principle refutation" that so far have been

lacking...

WAD> But I want to make these preliminary results

WAD> available because the misconception that one can

WAD> purchase specified complexity on the cheap is

WAD> widespread and ill-conceived.

WAD>

WAD> The only known generator of specified complexity

WAD> that we know is intelligence.

Given Dembski's stringent criteria for what can be

considered to be "specified complexity", it is

questionable if even the actions of intelligent

entities can be considered to be manifestations of

"specified complexity".

WAD> Sans intelligence, a process that yields

WAD> specified complexity merely converts already

WAD> existing specified complexity.

WAD> We are seeing a similar phenomenon with

WAD> inflationary cosmologies, which attempt to wash

WAD> out cosmological fine-tuning but invariably seem

WAD> to smuggle it back in. Smuggling in specified

WAD> complexity is not the same as **generating**

WAD> specified complexity. I challenge the biological

WAD> community to take these results seriously, and

WAD> reevaluate how it understands the generation of

WAD> specified complexity.

[...]

META> Permission is granted to reproduce this e-mail

META> and distribute it without restriction with the

META> inclusion of the following credit line: [This is

META> another posting from the Meta-List . Copyright

META> 1997, 1998, 1999. William Grassie.]

-------------------------------------------------------

=====

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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