The weasel returns: Truman replies to Curtis
by Royal Truman
Professor Dawkins describes his computer programs, written in Basic and later in Pascal, using words such as ‘mutation’, ‘generation’, ‘selection’, and so on. But he does not inform us just what the sentences actually represent. They might represent genes, proteins, operons or genomes, although from the context and from other publications an expressed gene is most likely.
The parameters and programming details are not based in the remotest on any biological data, or on considerations such as base-pair mutational probability, codon redundancy, population genetics, effect of neutral and destructive mutations, reproductive selectivity coefficients, etc.
A simpler algorithm, which reproduces the guaranteed convergence behaviour, clarifies what Dawkins' algorithm actually shows: that change is only possible towards a pre-selected goal. Once a letter falls into place, Dawkin's program ensures it won't mutate away. This is shown in the two following examples:
Example 1. Provided in: Dawkins, R., The Blind Watchmaker, Penguin Books, London, 1986; p. 48.
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M |
E |
T |
H |
I |
N |
K |
S |
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I |
T |
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I |
S |
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L |
I |
K |
E |
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A |
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W |
E |
A |
S |
E |
L | 10 |
Y |
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Y |
V |
M |
Q |
K |
S |
P |
F | T | X | W | S | H | L | I | K | E | F | V | | H | Q | Y | S | P | Y | 20 | Y | E | T | H | I | N | K | S | P | I | T | X | I | S | H | L | I | K | E | F | A | | W | Q | Y | S | E | Y | 30 | M | E | T | H | I | N | K | S | | I | T | | I | S | S | L | I | K | E | | A | | W | E | F | S | E | Y | 40 | M | E | T | H | I | N | K | S | | I | T | | I | S | B | L | I | K | E | | A | | W | E | A | S | E | S | 50 | M | E | T | H | I | N | K | S | | I | T | | I | S | J | L | I | K | E | | A | | W | E | A | S | E | O | 60 | M | E | T | H | I | N | K | S | | I | T | | I | S | | L | I | K | E | | A | | W | E | A | S | E | P | 64 | M | E | T | H | I | N | K | S | | I | T | | I | S | | L | I | K | E | | A | | W | E | A | S | E | L |
Example 2. Provided in: Dawkins, R., New Scientist, 34, Sept. 25, 1986; p. 34. | M | E | T | H | I | N | K | S | | I | T | | I | S | | L | I | K | E | | A | | W | E | A | S | E | L | #1 | W | D | L | T | M | N | L | T | | D | T | J | B | S | W | I | R | Z | R | E | Z | L | M | Q | C | O | | P | 10 | M | D | L | D | M | N | L | S | | I | T | J | I | S | W | H | R | Z | R | E | Z | | M | E | C | S | | P | 20 | M | E | L | D | I | N | L | S | | I | T | | I | S | W | P | R | K | E | | Z | | W | E | C | S | E | L | 30 | M | E | T | H | I | N | G | S | | I | T | | I | S | W | L | I | K | E | | B | | W | E | C | S | E | L | 40 | M | E | T | H | I | N | K | S | | I | T | | I | S | | L | I | K | E | | I | | W | E | A | S | E | L | 43 | M | E | T | H | I | N | K | S | | I | T | | I | S | | L | I | K | E | | A | | W | E | A | S | E | L |
Truman's data for Dawkins' weasel
 Figure 1. (Click figure to enlarge) Convergence behaviour from Truman’s program straddles that reported by Dawkins (some runs were selected out of 40 generated from Truman's program). |
The data for the three curves in Figure 1 labelled ‘Truman A’, ‘B’, and ‘C’ is in the spreadsheet ‘ImprovementRate’.
 Figure 2. (Click figure to enlarge) Data generated by Dawkins’ program and approximate average of 10,000 runs from Truman’s. On average Truman's curves lie further to the right (they converge a little more slowly). |
The data for Figure 2 has not been supplied as a table. This data is complex and rather long. The raw basis is found in the spreadsheet ‘Original summary 10,000 sorted’ (0.5 MB file). At the bottom of the second column an ‘Average=’ function has been added. Notice that the grand average for 10,000 runs is 102 generations for convergence to the target sentence. The data plotted in Figure 2 (i.e. average number of letters lined up after each generation) is found in the spreadsheet ‘Dawkins vs Truman’. Approximate weighted number of successful letters [as mentioned, using the raw data in the last sheet (Original summary 10,000 sorted)] per generation was used. Notice from the first column that indeed the average number of generations needed for all 28 letters to be lined up is 102.
Weasel words
Refuting a common ploy to persuade people that evolution has been ‘proven by computer’
by Werner Gitt with Carl
Wieland
Oxford professor Richard Dawkins is perhaps evolution’s chief apostle—certainly
one of the most vocal and influential neo-Darwinists in the world. He also aggressively
and unashamedly promotes atheism as a logical consequence of evolution. His book
The Blind Watchmaker has probably resulted in many thousands rejecting
a former profession of Christian faith. It purports to show that all of the apparent
design in the natural world is a consequence of unplanned accumulation, by selection,
of lucky genetic mistakes.1
The awesome engineering design seen throughout the living world is represented by
an unimaginably vast amount of information, stored and transmitted in coded form.
Dawkins realises that the basic challenge for anyone wanting to be (in his words)
an ‘intellectually fulfilled atheist’ is to explain how all this information
arose by natural processes, that is, without a guiding intelligence. However, information
science, the specialty field of one of us (Dr Gitt), makes it perfectly clear that
it is impossible for random processes to generate true information. So how does
Dawkins purport to show otherwise?
One of the most effective of the devices in his book, a demonstration he has repeated
for television audiences, is his alleged computer simulation of evolution by using
the English sentence (from Shakespeare’s Hamlet), ‘Methinks
it is like a weasel’.2
His computer program starts with a random sequence of 28 letters or spaces. It is
then copied repeatedly, representing reproduction. Random copying errors are allowed,
representing mutations. The computer program checks all the ‘daughter’
sentences, and selects that one which most resembles the target sequence, ‘Methinks
it is like a weasel’. This is said to represent natural selection.
Not surprisingly, within a few generations (43 and 64 in the examples shown below),
the target sentence is reached. This is purported to show that real information
can arise by the natural processes of mutation and selection, unaided by intelligence.
There is currently a spate of new books about the Lord Jesus Christ which constantly
present one or the other new, weird and false idea, contrary to the New Testament—for
example, that Jesus was a wicked priest. A Professor at the Heidelberg School of
Theology, Klaus Berger, once remarked, ‘Please buy and read such a book, then
you will realise what degree of gullibility is ascribed to you.’ Similarly,
Dawkins’ ‘weasel’ example makes it clear how much feeble-mindedness
he assumes in his readership.
This sort of computer game can be played by anyone, and will always reach
its goal. Why? Because the whole design involves selecting a target in advance!
The program is fixed, the target is specified—even the number of letters is
given in advance.
It is therefore obvious that no information is generated in Dawkins’ example—on
the contrary, the information (the sentence ‘Methinks it is like a weasel’)
has been predetermined!3
Computers and predetermined results‘Not surprisingly, within a few generations, the target sentence is reached.’ |
Dawkins’ example
Predetermined target sentence: METHINKS IT IS LIKE A WEASEL
First test:
Gen. 01 WDLTMNLT DTJBKWIRZREZLMQCO P
Gen. 02 WDLTMNLT DTJBSWIRZREZIMQLO P
Gen. 10 MDLDMNLS ITJISWHRZREZ MECS P
Gen. 20 MELDINLS IT ISWPRKE Z WECSEL
Gen. 30 METHINGS IT ISWLIKE B WECSEL
Gen. 40 METHINKS IT IS LIKE I WEASEL
Gen. 43 METHINKS IT IS LIKE A WEASEL
Second test:
Gen. 01 Y YVMQKZPFJXWVHGLAWFVCHQYOPY
Gen. 10 Y YVMQKSPFTXWSHLIKEFV HQYSPY
Gen. 20 YETHINKSPITXISHLIKEFA WOYSEY
Gen. 30 METHINKS IT ISSLIKE A WEFSEY
Gen. 40 METHINKS IT ISBLIKE A WEASES
Gen. 50 METHINKS IT ISJLIKE A WEASEO
Gen. 60 METHINKS IT IS LIKE A WEASEP
Gen. 64 METHINKS IT IS LIKE A WEASEL
References and notes
- His later books River out of Eden and Climbing Mount
Improbable continue his atheistic evangelising. See online refutations
of River out of Eden and
Climbing Mount Improbable. Return to text
- For technical details on the reasons why random processes cannot
give rise to information, see Werner Gitt, In
the Beginning was Information . Return to text.
- There are many other serious problems with Dawkins’ ‘demonstration’.
See Walter ReMine,
The Biotic Message, St Paul Science, St Paul, USA for a detailed treatment.
See online review of this book. Return
to text.
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