Dawkins has successfully reduced a combinatorial explosion to a manageable problem…or has he?:
In Richard Dawkins’ book, The Blind Watchmaker, he proposed a famous (and infamous) computer program to demonstrate the power of cumulative selection, known as the “Weasel program.” The program demonstrates that by varying a single letter at a time, it is possible to rapidly evolve a coherent English sentence from a string of gibberish…
Many have latched onto this program to defend (see here, here, and here) and debunk (see here and here) Darwinian evolution. On the other hand, Dawkins claims he only meant the program to show how natural selection can speed up evolution, and nothing further.
I think Dawkins’ program can indeed show something further, which is that natural selection can also make evolution impossible. What’s that again? That’s right: Dawkins’ weasel program shows natural selection prevents evolution from happening…
Multiplying the independent probabilities together, we end up with the probability of 2/27 * 2^-25 of hitting the target phrase, requiring more than 2^25 queries. This puts us right back into the combinatorial explosion Dawkins sought to avoid with piecewise selection. All I did was add a second target.
Eric Holloway, “Dawkins’ Dubious Double Weasel and the Combinatorial Cataclysm” at Mind Matters News
There was always something funny about Dawkins’s Weasel program. Did anyone ever really find the code?
Takehome: Eric Holloway shows that, far from demonstrating evolution, Dawkins’ weasel program shows that natural selection prevents evolution from happening.
The crucial flaw is that the code is aiming toward one predetermined target.
Nature don’t play dat. Nature is an infinite network of feedback loops among living things who are each trying to survive and enjoy life. The shape of an enjoyable ecosystem is NOT a predetermined target. It’s infinitely and smoothly shifting with every season and storm and birth and death and arrival and departure. If each ecosystem was trying to achieve a single predetermined set of conditions, all ecosystems would die FAST because the next storm or season would change both the shape of the best condition and the actors involved in reaching the best condition. The director would still be aiming for Hamlet while the actors and stage were best suited for Gilligan’s Island.
Polistra
No, the crucial flaw is, that Dawkins is a liar. He somehow forgot to mention, that there is DNA proofreading and repair system. So it will never happen the way Dawkins is suggesting. Never.
Simulations of natural selection which involve rewarding progress towards a goal are game simulations – not science simulations.
as to,,,
as to Dawkin’s weasel program, i.e. natural selection, being stymied by the fact that different sentences can mean the same thing, and/or two different sentences can have the same function, it is also worth noting that proteins with different amino acid sequences can have the same shape and/or function.
As well, the following article reveals that, contrary to Darwinian expectations, “RNAs with the same shape could vary very widely in sequence”:
In short, since the sequences of proteins and RNAs, (etc), can vary widely, and yet still produce the same function, then, clearly, the overarching ‘biological form’ of any particular organism is forever beyond ‘mutations to DNA’ as a viable explanation.
Needless to say, this is not a minor problem for Darwinists
Of semi-related note:
Verse:
🙂 “computer program” means that he used an 100% intelligently designed process trying to prove the opposite of intelligent design and purpose :randomness. . It is called : Self-defeater. .. meaning he proved the opposite view than the one he advertized for.
Dawkins by using this method indirectly admitted that mutation requires an background system that CONTROL / DETECT /ADAPT/CHOOSE regarding to a future PURPOSE (to obtain a pre-CHOSEN phrase like :”me thinks it is like a weasel” or “Evolution is a lie” or whatever )
I thought evolution doesn’t “see” in the future, doesn’t pre-program a purpose to be attained , but
Dawkins set the purpose from the beginning(to obtain a phrase) which his type of evolution allegedly doesn’t do and used an intelligent process (computer program) to achieve the programmed goal.
THIS IS EVIDENCE FOR INTELLIGENT DESIGN.
Why would your algorithm tend to evolve any of the two given phrases!!?? From the result you got you obviously specified that ANY combination of the two characters at the same place of the two phrases gets a score from the fitness function. You basically told the algorithm you don’t care about English phrases.
Which is weird, since you also told us, that:
… which I actually interpreted as:
fitness(mutant) = max(fitness(mutant, “it looks like a weasel to me”), fitness(mutant, “methinks it is like a weasel”))
… which would in the end result in one of the two English phrases.
Btw. there’s a simpler way to get a phrase, which no longer resembles an English phrase. Just put “it hooks it e a wkesel easel” as the target phrase instead of “methinks it is like a weasel”. You get a non english phrase in the first generation.
Where are the black knights of Monty Python fame?
At some point someone replicated the code in terms of function.
As Polistra said @ 1, the flaw with the Weasel simulator is that it doesn’t simulate natural selection. It simulates selective breeding. Everything closer to the identified target gets bred, everything else gets culled. Even the most hardened YEC agrees that selective breeding works.
There is already a term for attacking one viewpoint while claiming to attack another; it’s called a straw-man attack; we could use an equally-picturesque term for proving one thing while claiming to prove something else. A donkey in lion’s clothing, perhaps?
Of related note: “The GS (Genetic Selection) Principle states that biological selection must occur at the nucleotide-sequencing molecular-genetic level of 3’5′ phosphodiester bond formation. After-the-fact differential survival and reproduction of already-living phenotypic organisms (ordinary natural selection) does not explain polynucleotide prescription and coding. ”
At the 8:15 minute mark of the following video, Dr John Sanford, with the “Princess and the Pea paradox”, does a very good job of explaining exactly why Natural Selection is grossly inadequate for explaining the coding that must occur at the polynucleotide level.
John Sanford is a genius. Having many decades of experience in genetic area , he understood that must be much more information in cell and that DNA information is not enough even if 100% of DNA is functional (which it is). That leads at the truth that the focus on DNA in biology is the result of an inaccurate understanding about the basics of cell/organism organisation.
DNA is like a book on a shelf in library. Who use the book and informations from book? Who edit some informations or chapers in the book ?Whoever it is (of course is an automatic unconscious process ) must have built into it much more intelligence that the book itself contain. 🙂
PS : John Sanford said that genetic mutation mechanism imagined by evolutionists it’s a fable. The selection is done at the level of 6 billion nucleotides(whole DNA) NOT not at the level of few nucleotides. It’s a train with billions of wagons and if you can’t select one wagon that you like without getting all other bad wagons. Everything we know about “evolution” is wrong.
As to: “Everything we know about “evolution” is wrong.”
“Can You (Darwinists) Tell Me Anything About Evolution That Is True?”
Is the fact that the “Weasel” program is targeted really a flaw? Dawkins himself wrote that was, but surely it is no more than a gross modelling simplification? (It could be argued that the target phrase simply represents a match for what is required to be optimally fit for the environment in which the digital “organism” exists; that’s how it seems to me – in which case EH’s post above does in fact clearly show that the natural selection is more likely to prevent optimal fitness than guide it or speed it up.)
However, isn’t one of the biggest problems with “weasel” that *every* combination of its 28 letters is treated as equally functional? – *any* starting point combination, and *any* subsequent combination which is closer to the target, is considered functional: in other words, *every* combination has exactly the same value of specified complexity — there is no increase in functional complexity in the model, so no *vertical* evolution at all — at best it can only illustrate “horizontal” evolution, and that too only on the basis that it already has a mutation-proof self-replication system in place [in this case, as supplied by the intelligently designed computer program] and ignores the possibility of intermediate lesser “fitness peaks” etc between the starting letters and the final target.
T2 @ 13.
If one is attempting to model a system that, by definition, has no target, it would seem so.
@T2:
EH is very confused. He wrote:
“Okay, phrase added to program. Now there are two phrases, making the program a double weaseler. (…) This puts us right back into the combinatorial explosion Dawkins sought to avoid with piecewise selection. All I did was add a second target.”
Clearly that is not true. Looking at his result, it’s obvious he just added 2^25 target phrases, instead of two. His result after the first run has optimal fitness. Indeed that is how the weasel algorithm ends its run: when it hits optimal fitness.
It’s the “locking mechanism” of Weasel. It preserves non functional changes because they are closer to the target but that requires foreknowledge of the end state.
Thank you for your comments everyone. Lots of good thoughts. I’ll be writing a follow up in the near future. The weasel program, even though old, is very profound. It gets to the heart of the entire debate, and I think it is actually very insightful thought experiment by Dawkins.
While there are lots of criticisms that have been leveled at the weasel, like it not really representing purposeless evolution, I do believe that is besides Dawkins basic point with the thought experiment, so I agree with T2. It is primarily meant to demonstrate that what appears improbable due to the combinatorics involved, can actually be achieved very rapidly given the right circumstances.
On the other hand, T2, I don’t believe the assuming everything is functional is an inherent problem with the model. In fact, it is that assumption, combined with the fact that evolution is not goal directed, which leads to the dilemma I illustrate. AndyClue explains this well with noting the fitness function is indeed equivalent to 2^25 target phrases.
I’ll write a follow up piece to explain this in detail.