Email:<dembski@discovery.org> 

AMERICA'S OBSESSION WITH DESIGN: A RESPONSE TO WOLFHART PANNENBERG
By William A. Dembski 

In 1994 I participated at a C. S. Lewis summer conference at Queens'
College, Cambridge. The event was sponsored by the C. S. Lewis Foundation
(Redlands, California) and hosted by John Polkinghorne. A portion of the
conference was devoted to design, and proponents of design who spoke at
the conference included Phillip Johnson, Stephen Meyer, and Walter
Bradley. After Walter Bradley's presentation Arthur Peacocke got up and
dismissively remarked that "design is an American thing." His point
presumably was that European intellectuals had made their peace with
Darwin and evolution long since and had moved on to better things.
It was therefore with a sense of deja vu that I followed Wolfhart
Pannenberg's spring 2001 tour of American colleges and universities. In
that tour he focused on the relation between science and religion and in
particular on the role of contingency in the evolution of the biophysical
universe. Thomas Oord, in the Templeton Foundation's _Research News_ (May
2001, p. 34) reports that Pannenberg began the conference "Evolution:
Scientific and Theological Views," held in St. Paul, Minnesota with the
remark: "Concerning design, I wonder again and again why the dispute in
this country over the doctrine of evolution is so obsessive." Pannenberg
was especially puzzled why this obsession with design should take root in
America: "If you think of design as a plan to bring about something, then
one should be aware, especially in this country, that this involves
predeterminism. Because Americans are usually so apprehensive concerning
the danger of determinism, I wonder why 'design' is so much in the focus
of the discussion about the theory of evolution."
I want in this essay to try to answer Pannenberg's puzzlement (though
answering Peacocke's dismissiveness is a lost cause). The short answer to
Pannenberg's puzzlement is this: Science itself will not allow the design
problem to go away. It is important to understand that design in its
present resurgence is not a project in natural theology (see my last
article for Metaviews: "Is Intelligent Design a Form of Natural
Theology?"). In particular, design is not wedded to a 17th or 18th century
conception of God as a rational object, single subject, or first cause.
All that design in its present American incarnation is wedded to is that
telic processes operate in nature and that these processes are empirically
detectable and not reducible to blind, unbroken natural laws (what Jacques
Monod referred to as chance and necessity). The ultimate source behind
such processes is not at issue.
On the same page of _Research News_ where Pannenberg puzzles over
America's
obsession with design, Pannenberg comments on the work and person of John
Polkinghorne. The problem with Polkinghorne's contribution to the
science-religion dialogue for Pannenberg is that Polkinghorne "has no
appropriate philosophical education." Since I don't know what Pannenberg's
mathematical education is, I'm in no position to say whether the problem
with Pannenberg's puzzlement over America's obsession with design is that
he "has no appropriate
mathematical education."Nonetheless, to see why design remains a live
issue and why the design problem will not go away for science, it is
important to understand something about the mathematical underpinnings of
design, and specifically how these underpinnings relate to Darwinian
evolution.
But before venturing on to these mathematical underpinnings, I need
to clear up a confusion about design. Pannenberg evinces this confusion
when he remarks that design entails a plan which in turn entails
determinism. This is the clock-work view of design in which a designer
forms a rigid plan that admits no variation and in which the designed
object constructed conforms exactly to that rigid plan. Such designs are
common with human artifacts, but they hardly exhaust the notion of design.
Design as understood by American design theorists is conceived in much
broader teleological terms. Michael Polanyi remarked on this broader
conception of design as follows:
"It is true that the teleology rejected in our day is understood as
an overriding cosmic purpose necessitating all the structures and
occurrences in the universe in order to accomplish itself. This form of
teleology is indeed a form of determinism -- perhaps even a tighter form
of determinism than is provided for by a materialistic, mechanistic
atomism. However, since at least the time of Charles Saunders Peirce and
William James a looser view of teleology has been offered to us -- one
that would make it possible for us to suppose that some sort of
intelligible directional tendencies may be operative in the world without
our having to suppose that they determine all things. Actually it is
possible that even Plato did not suppose that his "Good" forced itself
upon all things. As Whitehead has pointed out, Plato tells us that the
Demiurge, looking toward the Good,"persuades" an essentially free matter
to structure itself, to some extent, in imitation of the Forms. Plato
appeared to Whitehead to have modeled the cosmos on a struggle to achieve
the Good in the somewhat recalcitrant media of space and time and matter,
a struggle well known to all souls with purposes and ends and aims.
Whether or not it is true that Plato did this, certainly Whitehead modeled
his own cosmos very much this way."(1)
America's obsession with design is not identical with America's
obsession with creationism. Creationism cannot be separated from religious
commitments. Design can be considered on its own terms and strictly as a
form of scientific investigation. What's more, design is not properly
regarded as anti-evolutionist. It stands against a certain conception of
evolution, to wit, one in which teleology is removed from having any
scientific significance.
Pannenberg and I are at one that teleology is working itself out
historically in the world (and since we are both Christians, we see that
teleology as deriving ultimately from the Christian Trinitarian God). The
source of Pannenberg's puzzlement over America's obsession with design is
not that American's should regard teleology as real but as having
scientific content. Here is where design becomes interesting: Teleology
plus scientific content equals design.
Of course, to say that teleology plus scientific content equals
design is to raise all sorts of flags, and specifically the worry that
design as it is now being developed is a nun thinking throwback to the
(deterministic) design of the old-time natural theologians. But this is
not the case. Design as my colleagues and I are developing it can
accommodate the rich contingency and freedom of the natural world and
still give scientific content to teleology. To show this, however, will
require a mathematical excursion into evolutionary algorithms and in
particular into the No Free Lunch theorems that were proven five years
ago.
The No Free Lunch theorems are not deep mathematical results that
require brilliant intuitive leaps or fundamentally new concepts. An even
cursory examination of their proofs reveals that the No Free Lunch
theorems are essentially book-keeping results.They keep track of how well
evolutionary algorithms do at optimizing fitness functions over a phase
space. The fundamental claim of these theorems is that averaged across
fitness functions, evolutionary algorithms cannot outperform blind search.
The No Free Lunch theorems underscore the fundamental limits of the
Darwinian mechanism. Up until their proof, it was thought that because the
Darwinian mechanism could account for all of biological complexity,
evolutionary algorithms (i.e., its mathematical underpinnings) must be
universal problem solvers. The No Free Lunch theorems show that
evolutionary algorithms, apart from careful fine-tuning by a programmer,
are anything but universal problem solvers. Consequently, these theorems
undercut the power of the Darwinian mechanism to account for all of
biological complexity. Granted, the No Free Lunch theorems are
book-keeping results. But book-keeping can be very useful. It keeps us
honest about the promissory notes our various enterprises -- science being
one of them -- can and cannot make good. In the case of Darwinism we are
no longer entitled to think that the Darwinian mechanism can offer
biological complexity as a free lunch.
It is a very human impulse to look for magical solutions to
circumvent mathematical impossibilities. The theory of accounting tells us
that Ponzi schemes cannot work. The theory of probability tells us that
games of chance whose expected gain favors not us but the casino can only
lead to our loss in the long run. Nonetheless, Ponzi schemes and casino
gambling continue to be big business. Likewise, in biology, even though
computational theory is clear that evolutionary algorithms cannot generate
complex specified information, by suitably shuffling information around
one often gets the impression that evolutionary algorithms can in fact
generate complex specified information and that complex specified
information is a free lunch after all. Invariably what's involved here is
a shell game in which the shells are adroitly moved so that one loses
track of just which shell contains the elusive pea. The pea here is
complex specified information. The task of the book-keeper is to follow
the information trail so that it is properly accounted for and not
magically smuggled in. Complex specified information is what gives
teleology scientific teeth and turns teleology into design.
As an example of smuggling in complex specified information that is
purported to be generated for free, consider the work of Thomas Schneider.
Schneider heads a laboratory of experimental and computational biology at
the National Cancer Institute. He is well-versed in Shannon's theory of
information, regularly applies it in his research, and devotes
considerable space to it on his website. (2) In the summer of 2000 he
published an article in _Nucleic Acids Research_ titled "Evolution of
Biological Information." (3) In that paper he identified a computational
phase space consisting of all sequences 256 letters in length constructed
from a four-letter alphabet (cf. the four nucleotide bases). The phase
space therefore consisted of 4^256 sequences, or approximately 10^154
sequences. Starting with an evolutionary algorithm acting on a randomly
chosen sequence from the phase space, Schneider then purported to generate
an information-rich sequence corresponding to a finely tuned genetic
control system in which one part of the genome codes for proteins that
precisely bind to another part of the genome. To model genetic control,
Schneider divided his 256-letter computational genomes essentially in
half, treating the first half as what he called a "weight matrix"and the
second half as binding sites. The optimization task of his evolutionary
algorithm was to get the weight matrix to match up suitably with the
binding sites. Here the weight matrix corresponded to translation and
protein folding of natural biological systems, and the binding sites
corresponded to locations on DNA where these proteins would then bind.
The details here are not that important. What is important is the
discrepancy between what Schneider thinks his computer simulation
establishes and what it in fact establishes. Schneider thinks that he has
generated complex specified information for free, or as he puts it, "from
scratch." Early in his article he writes, "The necessary information
should be able to evolve from scratch." (4) Later in the article he claims
to have established precisely that: "The program simulates the process of
evolution of new binding sites from scratch."(5) According to Schneider
the advantage of his simulation over other simulations that attempt to
generate complex specified information (like Richard Dawkins's biomorphs
program and Thomas Ray's Tierra environment) is that Schneider's program
"starts with a completely random genome, and no further intervention is
required."(6) Schneider gives his readers to believe that he has
decisively confirmed the full sufficiency of the Darwinian mechanism to
account for biological information. Accordingly, he claims his model
"addresses the question of how life gains information,... [and] shows
explicitly how this information gain comes about from mutation and
selection, without any other external influence." (7)
But has Schneider in fact successfully answered the charge that the
Darwinian mechanism is inadequate to generate biological information and
in particular complex specified information? In reading Schneider's
article, and more generally when confronting Darwinian scenarios that
purport to generate complex specified information for free, I always go
back to my days as a graduate student in mathematics teaching
undergraduates trigonometry. When it came time to grade their tests, I
always had to watch that they didn't trick me by purporting to establish a
trigonometric equality when in fact they didn't have a clue why one
trigonometric expression was equal to another. What students would do is
write one expression at the top of the page, the other at the bottom of
the page. Then they would manipulate the top expression, transforming it
line by line down the middle of the page. Next they would manipulate the
bottom expression, transforming it line by line up the middle of the page.
In the middle of the page the transformed top and bottom expressions would
happily meet, offering no clue how they were related. My challenge was to
find where the unwarranted leap occurred (i.e., where the transformation
from one expression to the other could no longer be justified).
I find myself in a similar position analyzing Schneider's article and
Darwinian scenarios like his. Schneider claims to have generated complex
specified information for free. The No Free Lunch theorems, however, tell
us this is not possible. Where, then, has he smuggled in complex specified
information? The precise place where he smuggles it in is not hard to find
if one knows what to look for. Here is the crucial paragraph in his
article:
"The organisms [i.e., the computational sequences in phase space] are
subjected to rounds of selection and mutation. First, the number of
mistakes made by each organism in the population is determined. Then the
half of the population making the least mistakes is allowed to replicate
by having their genomes replace ('kill') the ones making more mistakes.
(To preserve diversity, no replacement takes place if they are equal.) At
every generation, each organism is subjected to one random point mutation
in which the original base is obtained one-quarter of the time."(8)
Within this crucial paragraph, the crucial sentence is: "The number
of mistakes made by each organism in the population is determined." Who or
what determines the number of mistakes? Clearly, Schneider had to program
any such determination of number of mistakes into his simulation.
Moreover, the determination of number of mistakes is the key defining
feature of his fitness function, for which optimal fitness corresponds to
minimal number of mistakes.
Readers of Richard Dawkins's _The Blind Watchmaker_ have seen all
this before, to wit, in Richard Dawkins's METHINKS IT IS LIKE A WEASEL
simulation. To be sure, Schneider's simulation is more subtle. But the
parallels are unmistakable. Like Dawkins's simulation, Schneider's
simulation starts with a randomly given "genome" and requires no further
intervention. Unlike Dawkins's simulation, Schneider's does not identify
an explicitly given target sequence. Even so, it identifies target
sequences implicitly through the choice of fitness function. Moreover, by
tying fitness to number of mistakes, Schneider guarantees that the
gradients of his fitness function rise gradually and thus that his
evolutionary algorithm converges in short order to an optimal
computational sequence (optimality being defined in relation to his
fitness function). Although once the algorithm starts running there is no
intervention on the part of the investigator, it is not the case that
Schneider didn't intervene crucially in structuring the fitness function.
He did, and this is where he smuggled in the complex specified information
that he claimed to obtain from scratch.
Schneider's choice of fitness function is the most obvious place
where he smuggles in complex specified information. But there are others.
In the _Nucleic Acids Research_ article we've been discussing, he does not
list the source code for the program underlying his simulation. For that
code he refers readers to the relevant web address. The source code is
revealing and shows that Schneider had to do a lot of fine-tuning to his
evolutionary algorithm to make his simulation come out right. For
instance, in the crucial paragraph from his article that I quoted above,
Schneider remarks parenthetically: "To preserve diversity [of organisms],
no replacement takes place if [the number of mistakes is] equal."
Schneider's Pascal source code reveals why: "SPECIAL RULE: if the bugs
have the same number of mistakes, reproduction (by replacement) does not
take place. This ensures that the quick sort algorithm does not affect who
takes over the population. [1988 October 26] Without this, the population
quickly is taken over and evolution is extremely slow!"(9) Schneider is
here fine-tuning his evolutionary algorithm to obtain the results he
wants. All such fine-tuning amounts to investigator interference smuggling
in complex specified information.

1 Michael Polanyi and Harry Prosch, _Meaning_ (Chicago: University of
Chicago Press, 1975), pp. 162-163.
2 http://www.lecb.ncifcrf.gov/~toms.
3 Thomas D. Schneider, "Evolution of Biological Information,"
_Nucleic Acids Research_ 28(14) (2000): 2794-2799.
4 Ibid., p. 2794.
5 Ibid., p. 2796.
6 Ibid.
7 Ibid., p. 2797.
8 Ibid. p. 2795.
9 http://www.lecb.ncifcrf.gov/~toms/delila/ev.html.

This list is hosted by Metanexus: The Online Forum on Religion and Science
. The views expressed here do not necessarily reflect those of Metanexus
or its sponsors. To comment on this message, go to the browser-based forum
at the bottom of all postings in the magazine section of our web site at
http://www.metanexus.net/index2.asp?fc=/archives/searcharchives.asp?list=16listtype=Magazine.
Metanexus welcomes submissions between 1000 to 3000 words of essays and
book reviews that seek to explore and interpret science and religion in
original and insightful ways for a general educated audience. Previous
columns give a good indication of the topical range and tone for
acceptable essays. Columns may be quoted or republished in full, with
attribution to the author of the column and "Metanexus: The Online Forum
on Religion and Science ". Please send all inquiries and submissions to
Dr. Stacey Ake, Associate Editor of Metanexus at mail to:
ake@metanexus.net. Copyright 1998, 1999, 2000, 2001 by William Grassie.

Oryginal: http://www.metanexus.net/archives/message_fs.asp?ARCHIVEID=3294
------------------------------------------------------------------------------
AMERICA'S OBSESSION WITH DESIGN: A RESPONSE TO WOLFHART
PANNENBERG by WILLIAM DEMBSKI (Part 2/2)
ake@METANEXUS.NET Stacey Ake
Metanexus: VIEWS 2001.06.25 2352 words 

Here we have part two of William Dembski's reply to Wolfhart Pannenberg
via Arthur Peacocke's 1994 remark that "design is an American thing." 

In this part of his essay, Dembski presents several arguments for design,
including the juxtaposition of computer simulations vis-=E0-vis "actual
biological examples". 

But is it fair to set the process of simulation --something which occurs
through time-- over and against a theory such as that of intelligent
design which is looking at biological reality as we find it today? In
other words, could it be the case that intelligent design is merely an
artifact of a kin= d of ascertainment bias in which the result of a
process is used to determine the nature of the process? 

Just a few things to think about. 

--Stacey E. Ake 


From: William A. Dembski Subject: Part 2/2: AMERICA'S OBSESSION WITH
DESIGN: A RESPONSE TO WOLFHART PANNENBERG Email: "William A. Dembski"
<William_Dembski@baylor.edu> 

AMERICA'S OBSESSION WITH DESIGN: A RESPONSE TO WOLFHART PANNENBERG By
William A. Dembski 

In the case of computer simulations, following the information trail and
finding the place where complex specified information was smuggled in is
usually not difficult. I predict it will become more difficult in the
futur= e as this shell game becomes more sophisticated, involving more
shells and quicker movements of the shells. But just as accounts where
profits and losses cannot be squared with receivables contain an error in 
addition or subtraction somewhere, so simulations that claim to generate
complex specified information from scratch contain an unwarranted
insertion of pre-existing complex specified information. With simulations
all that's needed is to follow the information trail and find the point
of insertion. That may be complicated, but the entire trail is surveyable
and will eventually yield to sustained analysis -- it's not as though 
we're missing any crucial piece of the puzzle. 

The same cannot be said for actual biological examples. Consider, for
instance, a proposed counterexample to my claim that evolutionary
algorithm= s cannot generate complex specified information. The challenge
in this instance focuses on carefully controlled experiments with
biopolymers. Here is the challenge as it's been put to me in several
unsolicited emails over the Internet: 

"For selection to produce some innovation that is both complex and
specific would demolish your hypothesis. In fact selection can do just
that. Consider in vitro RNA evolution [N.B.: he actual type of
biopolymer used is unimportant; RNA is the fashion these days]. Using
only a random pool of RNAs (none of them designed), we can select for
RNAs that perform a certain highly specified function. They can be
selected to bind to any molecule of choice with high specificity or to
catalyze a highly specific reaction. This is molecular specified
information, by anyone's definition. We have thus empirically seen that
highly specific information can be generated in a molecule without
designing the molecule. Information theory just has to catch up with what
we know from experiment. At the beginning of a SELEX experiment, for
instance, you have a random pool of RNAs that can't do much at all. At
the end you have a pool of RNAs that can perform a complex specified
function, such as catalyze a specific reaction or bind a specific
molecule. In other words, there is an increase in net complex specified
information through the course of the experiment. The pool of molecules
you get at the end of the experiment were never designed. To the
contrary, the scientist has no clue as to the identity of their sequence
or structure. An extensive effort usually follows a SELEX experiment to 
characterize the evolved RNA. The RNA must be sequenced, and in some cases
it is crystallize= d and the structure is solved. Only then does the
scientist know what was created, and how it performs its complex specific
function." (10)

In no way do SELEX, ribozyme engineering, or similar experimental
techniques circumvent the No Free Lunch theorems. In SELEX experiments
large pools of randomized RNA molecules are formed by intelligent
synthesis and not by chance -- there's no natural route to RNA. These
molecules are then sifted chemically by various means for catalytic
function. What's more, the catalytic function is specified by the 
investigator. Those molecules showing some activity are isolated and
become templates for the next round of selection. And so on, round after
round. At every step in both SELEX and ribozyme (catalytic RNA)
engineering experiments generally, the investigator is carefully
arranging the outcome, even if he or she doesn't know the specific
sequence that will emerge. It is simply irrelevant that the investigator
is ignorant of the identity and structure of the evolved ribozyme and
must determine it after the experiment is over. The investigator first
had to specify a precise catalytic function, next had to specify a
fitness measure gauging degree of catalytic function for a given 
biopolymer, and finally had to run an experiment optimizing the fitness
measure. Only then does the investigator obtain a biopolymer exhibiting
the catalytic function of interest. In all such experiments the
investigator is inserting complex specified information right and left,
most notably in specifying the fitness measure that gauges degree of
catalytic function. Once it's clear what to look for, following the
information trail in such experiments is straightforward.(11) 

I want now to step back and consider why researchers who employ
evolutionary algorithms might be led to think that these algorithms
generate complex specified information as a free lunch. The mathematics
is against complex specified information arising de novo from any
non-telic process. What's more, counterexamples that purport to show how
complex specified information can arise as a free lunch are readily
refuted once one follows the information trail and, as it were, audits
the books. Even so, there is something oddly compelling and almost
magical about the way evolutionary algorithms find solutions to problems
where the solutions are not like anything we have imagined.(12) A
particularly striking example is the "crooked wire genetic antennas" of 
Edward Altshuler and Derek Linden.(13) The problem these researchers
solved with evolutionary (or genetic) algorithms was to find an antenna
that radiates equally well in all directions over a hemisphere situated
above a ground plane of infinite extent. Contrary to expectations, no
wire with a neat symmetric geometric shape solves this problem. Instead,
the best solutions to this problem look like funky zig-zagging tangles.
(14) What's more, evolutionary algorithms find their way through all the
various zig-zagging tangles -- most of which don't work -- to one that
actually does. This is remarkable. Even so, the fitness function that
prescribes optimal antenna performance is well-defined and readily
supplies the complex specified information that an optimal crooked wire
genetic antenna seems to acquire for free. 

Perhaps more striking and certainly better known is the evolutionary
checker playing program of Kumar Chellapilla and David Fogel. As James
Glanz reported in the _New York Times_, "Knowing only the rules of
checkers and a few basics, and otherwise starting from scratch, the
program must teach itself how to play a good game without help from the
outside world including from the programmers."(15) The program employs
evolutionary algorithms, neural networks, and the techniques of
artificial intelligence. In the initial work of Chellapilla and Fogel in
1999, their program attained a level one or two notches below expert.
(16) Since then, "with longer evolutionary trials and the inclusion of a 
preprocessing layer to let the neural network learn that the game is
played on a two-dimensional board, rather than a one-dimensional
32-element vector," the program has in fact attained the level of expert.
(17) The program therefore plays checkers at a level far superior to most
humans. What's remarkable about this program is that it attained such a
high level of play without having to be explicitly programmed with expert 
knowledge like the world champion chess program Deep Blue or the world
champion checker program Chinook. (18) 

But did the evolutionary checker program of Chellapilla and Fogel achieve
its superior play without commensurate input from prior intelligence? If
one looks at how Chellapilla and Fogel actually programmed their
evolutionary algorithm, one finds that they instituted a rating system
(like the one used to rank chess players) that continually tracked how
well a given neural network (i.e., candidate solution) was doing.(19 In
place of a fixed fitness function, Chellapilla and Fogel therefore
defined what might be called a "floating fitness function," or what
Stuart Kauffman calls a coevolving fitness landscape. But the mathematics
of evolutionary algorithms is the same whether the fitness functions are
fixed or floating (see section 5.10 of my forthcoming _No Free Lunch_). 

The important thing to note about these ratings is that they are fine
grained and specify very precisely how well a candidate solution is doing
with respect to other possible solutions. It's not as though there are
only two or three discrete categories for ranking solutions. Instead
there is a whole series of numbers ranging from 0 to 2400 and above in
which higher numbers correspond to superior skill and expert-level play
corresponds to between 2000 and 2199 (master play is ranked 2200 and
above). Consequently, finding an optimal solution here is like the old
Easter egg hunt game in which one is told either "hotter" or "colder"
depending on whether one is getting closer to or farther away from the
hidden prize. There is an incredible amount of complex specified
information packed into a fitness function (whether it's fixed or
floating) that for every pair of elements in a solution space can tell
you which is superior. What's more, any evolutionary algorithm capable of
precisely implementing such a fitness function by preserving only the
superior and weeding out all the inferior i= s making full use of that
information (Chellapilla and Fogel's algorithm did just that; note that
natural selection in biology operates with nowhere near this precision).
Again, there is no free lunch here -- complex specified information has
not been generated for free. 

In closing this essay I want to draw a pair of lessons, both of which I
hope Wolfhart Pannenberg will find congenial. Both design and evolution
have a lesson to learn from each other. The No Free Lunch theorems show
that for evolutionary algorithms to output complex specified information
they had first to receive a prior input of complex specified information.
And since complex specified information is reliably linked to intelligence
(cf. my _The Design Inference_), evolutionary algorithms, insofar as they
output complex specified information, do so on account of a guiding
intelligence. The lesson, then, for evolution is that any intelligence
evolutionary processes display is never autonomous but always derived. On
the other hand= , evolutionary algorithms do produce remarkable solutions
to problems solutions that in many cases we would never have imagined on
our own. Having been given some initial input of complex specified
information, evolutionary algorithms as it were mine that complex
specified information and extract every iota of value from it. The 
lesson, then, for design is that natural causes can synergize with
intelligent causes to produce results far exceeding what intelligent
causes left to their own abstractions might ever accomplish. Too often
design is understood in a deterministic sense in which every aspect of a
designed object has to be pre-ordained by a designing intelligence. 
Evolutionary algorithms underwrite a non-deterministic conception of
design in which design and nature operate in tandem to produce results
that neither could produce by itself (Christian incarnational theology
resonates deeply with this point). 

One final note is in order. Pannenberg is puzzled over America's
obsession with design. He begins what was perhaps the key talk of his
recent American tour with the remark: "Concerning design, I wonder again
and again why the dispute in this country over the doctrine of evolution
is so obsessive." Pannenberg had many interlocutors during his American
tour.Yet no design theorist was invited to serve as an interlocutor for 
Pannenberg during that entire tour. I therefore have a puzzlement of my
own: Why was that? 

10 Adapted from one of many emails like it that I have received. SELEX
refers to "systematic evolution of ligands by exponential enrichment." In
1990 the laboratories of J. W. Szostak (Boston), L. Gold (Boulder), and
G. F. Joyce (La Jolla) independently developed this technique, which
permits the simultaneous screening of more than 10^15 polynucleotides for
different functionalities. See S. Klug and M. Famulok, "All You Wanted to
Know about SELEX," _Molecular Biology Reports_ 20 (1994): 97-107. See
also Gordon Mills and Dean Kenyon, "The RNA World: A Critique," _Origins
& Design_ 17(1) (1996): 9-14. 

11 I'm indebted to Paul Nelson for helping me see how formal mathematical
theory connects to current experimental work with biopolymers. 

12 For a survey of the diverse problems to which evolutionary algorithms
have been applied and for many of which these algorithms have generated
unexpected solutions see Melanie Mitchell, _An Introduction to Genetic
Algorithms_ (Cambridge, Mass.: MIT Press, 1996), pp. 15-16. 

13 Edward E. Altshuler and Derek S. Linden, "Design of Wire Antennas
Using Genetic Algorithms," pp. 211-248 in Electromagnetic Optimization by
Genetic Algorithms, eds. Y. Rahmat-Samii and E. Michielssen (New York:
Wiley, 1999 . I'm indebted to Karl Stephan for pointing me to this
example. See Karl Stephan, "Evolutionary Computing and Intelligent
Design," _Zygon_ (2001): in review. 

14 Altshuler and Linden, "Design of Wire Antennas Using Genetic
Algorithms, fig. 22. 

15 James Glanz, "It's Only Checkers, but the Computer Taught Itself," New
York Times (25 July 2000): on the web at 
http://www.transhumanismus.de/SciTech/0007/Checkers.htm. 

16 Kumar Chellapilla and David B. Fogel, "Co-Evolving Checkers Playing
Programs using Only Win, Lose, or Draw," _SPIE's AeroSense'99:
Applications and Science of Computational Intelligence II_ (Orlando, Fl.:
5-9 April 1999). SPIE is the International Society for Optical
Engineering. 

17 Personal communication from David B. Fogel, 27 February 2001. 

18 Deep Blue's defeat of Gary Kasparov in 1997 is widely known. For an
account of Chinook, see J. Schaeffer, R. Lake, P. Lu, and M. Bryant,
"Chinook: The World Man-Machine Checkers Champion," _AI Magazine_ 17
(1996) 21-29. 

19 Kumar Chellapilla and David B. Fogel, "Evolving Neural Networks to
Play Checkers without Relying on Expert Knowledge," _IEEE Transactions on
Neural [brak dalszego ciagu w oryginale]

This list is hosted by Metanexus: The Online Forum on Religion and
Science <http://www.metanexus.net>. The views expressed here do not
necessarily reflect those of Metanexus or its sponsors. To comment on
this message, go to the browser-based forum at the bottom of all postings
in the magazine section of our web site at 
http://www.metanexus.net/index2.asp?fc=/archives/searcharchives.asp?list=16listtype=Magazine.
Metanexus welcomes submissions betwwen 1000 to 3000 words of essays and
book reviews that seek to explore and interpret science and religion in 
original and insightful ways for a general educated audience. Previous
columns give a good indication of the topical range and tone for
acceptable essays. Columns may be quoted or republished in full, with
attribution to the author of the column and "Metanexus: The Online Forum
on Religion and Science <http://www.metanexus.net>". Please send all
inquiries and submissions to Dr. Stacey Ake, Associate Editor of 
Metanexus at ake@metanexus.net. Copyright 1998, 1999, 2000, 2001 by
William Grassie. 

Oryginal:
http://www.metanexus.net/archives/message_fs.asp?ARCHIVEID=3428