Home » Darwinism, Evolution, Intelligent Design » The Original WEASEL(s) — Part II

The Original WEASEL(s) — Part II

In an earlier post (go here), I relayed to UD readers two programs that had been emailed to me by someone named Oxfordensis. After careful scrutiny, my colleagues and I at the Evolutionary Informatics Lab concluded that these are by far the best candidates that we have to date for Dawkins’s original WEASEL program(s) (as I note in the previous UD post, it appears that there were in fact two programs, one described by Dawkins in his book THE BLIND WATCHMAKER, the other appearing in his BBC video about the book). Are these in fact the original WEASELs? When I contacted Richard Dawkins to confirm their authenticity, he replied, in an email dated 9.21.09, “I cannot confirm that either of them is mine. They don’t look familiar to me, but it is a long time ago. I don’t see what more I can say.”

In that email, Dawkins rightly raised the question of these program’s provenance and the fact that UD had issued a reward for them. Yet the reward was so small (a mere book) that this hardly seems sufficient for someone to write these programs as a hoax. The question of provenance is more worrisome, but then again so is the failure of Dawkins to keep copies of the program, especially when they are of such historical interest in ongoing debates over evolution. Are, then, the programs listed in my previous post in fact the originals? Even if this question cannot be answered with iron-clad certainty, we submit that they deserve to be taken as originals. Why? Three reasons:

1. They are written in PASCAL and they compile in the PASCAL compilers available in the mid- to late-80s.

2. These programs were widely circulated at the time. Charles Thaxton informs me, in an email dated 8.27.09, “As for the Dawkins program, I picked up a copy in 1989 at Princeton from a physics grad student after my talk there.” Like Dawkins, he adds, “But Bill, I have no idea where it is.” Presumably, these programs are still out there on people’s computer memory (or floppy disks). So why can’t an anonymous person like Oxfordensis have the originals?

3. Their performance is precisely what we would expect given the historical record that we have of these programs.

This last point has been the main sticking point keeping critics from embracing these programs as the originals. As Wesley Elsberry put it to me in an email dated 9.21.09: “Putting mutation on a per-copy basis rather than per-base would be rather unlike the biology.” And yet, Dawkins does indeed seem to have made this non-biological assumption in programming his WEASELs. In what follows, I draw from my consultation with a programmer colleague:

The objection raised against the code for the two WEASELS is that it makes exactly one mutation in each offspring; instead, so the objection goes, the mutations should have been made per-letter so that each letter has a certain probability of being mutated when the string is copied. The main reason urged for this is that a per-letter mutation scheme is closer to the biological reality. It is, of course, true that the biology would be better modeled by allowing multiple mutations. Nevertheless, there does not appear to be a good reason to claim that Dawkins would necessarily have modeled this particular aspect of biology to that degree of accuracy. Dawkins was attempting to demonstrate the power of cumulative selection. Thus, the difference between one-mutation-per-child and mutation-rate-per-letter may well have been too minor to worry about.

Dawkins’s text does not explicitly state what procedure was used to produce mutations. Some of the statements could be read to imply a mutation-rate-per-letter method, but that’s far from clear. Dawkins leaves many of the precise details of his algorithm unstated. That is all very fine and well because he was writing a popular defense of Darwinism not an algorithm textbook. On balance, the text does very little either way to specify which method was used to produce the mutations.

WEASEL1 does in fact converge to the target phrase in a similar number of generations as the runs shown in TBW. A simulation based on a mutation-rate-per-letter can be made to converge in a similar number of generations by combining a 4% mutation rate with a population size of 200. On the other hand, WEASEL1 has a population size of 100. Clearly, it would be possible to have the simulation converge in any given number of generations by picking an appropriate mutation rate and population size. The question is whether it is more likely for Dawkins to have chosen a population size of 100 or 200. Deciding that question is, arguably, too speculative to be of much use.

WEASEL2 records a similar number of tries as the run shown in the BBC video. WEASEL2’s algorithm does not contain the same tunable parameters as do the other algorithms. There is no population size to pick in order to make it fit with the correct number of tries. As such it would be extremely unlikely for the program to converge in a similar number of generations if it were not the same algorithm.

The best way to determine whether the illustrative runs in TBW were produced by an algorithm that used multiple mutations would be to see whether or not evidence of multiple mutations exists in the showcased runs. One complication in determining this is that the first generation of the first run has only 27 letters instead of the correct 28. Nevertheless, there is another very similar string presented early as part of the non-cumulative selection demonstration. The only difference is that the earlier string was in fact 28 letters long. Presumably, Dawkins reused the random seed to produce the string. Assuming that the earlier string is correct, the string is missing a D. In the second generation that D changes into a T. Yet by generation 10, the letter is back to a D. It seems highly unlikely that the letter would change from D to T to D without any selection pressure to do so. The most probable understanding is that it was a D the whole time and the T is a typo. This isn’t too unreasonable considering the difficulty of copying random strings by hand correctly and given the couple of other obvious typos in the strings.

If we assume the preceding interpretation of the first run, we can see that between generation 1 and 10, 9 letters are changed. This means that exactly one letter was changed per generation. From generation 10 to 20, an additional 10 letters are changed; again, one per generation. The second run gives the same results. In generations 1-10, 9 letters are changed. In generations 10-20, 10 letters are changed. This is what we would expect if each offspring contained exactly one mutation. In every generation, one change ends up being made to the string. On the other hand, it is improbable for this to have occurred if the algorithm employed multiple mutations.

Indeed, for the runs to have resulted from a simulation that included multiple mutations, either all multiple mutations were selected against, or they were hidden by another mutation. However, a multiple mutation has an increased chance of setting a correct letter early in the simulation because most of the letters are incorrect. It is simply too improbable to suggest that they happened not to be selected in either run. Other mutations that hide the first mutations also appeal to improbable events. There is no reason to suppose it is probable that the extra mutations happened to be hidden by other mutations while insuring that those other mutations themselves were not multiple mutations.

Looking at the evidence as a whole, there does not seem to be any significant reason to reject a single-mutation understanding of the simulation. On the other hand, the data fits a single mutation method very well. It does not fit a simulation that includes multiple mutations. We therefore conclude, unless further evidence is presented, that the single-mutation algorithm implemented by WEASEL1 is the one used by Dawkins in TBW.

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11 Responses to The Original WEASEL(s) — Part II

  1. Re prize:

    The prize is a copy of either Stephen Meyer’s new Signature in the Cell or Richard Dawkins’ soon-to-be-out The Greatest Show on Earth. [Winner's choice]

    Should the winner choose the latter, I will ask Dawkins’s publicist to mail the copy.

    Either book could be obtained for less than US$50 in most places, so I doubt either would be much of an incentive to perpetrate a big hoax that would take many hours to prepare.

    Denyse O’Leary
    contest administrator
    PS: Hoaxers: Wait till I offer an Alpha Romeo or a villa in Tuscany.

  2. One objection could be that the 4% mutation rate would be 4 out of 100 getting one mutation or one individual getting 4 mutations- the 99 others are mutation free.(?)

    However 4% is very, very high.

  3. What’s the purpose of finding that algorithm anyway? I’m just curious and like to know.

  4. I don’t really understand the point of disassembling these programs when, as Dembski also says, (1) they 20 years old! (2) they were never intended to accurately model evolution in the first place.

    It would be more interesting of Dembski would crack down on models that actually are used in research.

  5. –Joseph
    in which sense is 4% high, very high. We’re talking about models! In the paper of Marks and Dembski Conservation of Information in Search: Measuring the Cost of Success, the authors introduce an algorithm called Random Mutation, where they use a mutation rate of .005 % , and a population size of 2. Would you call such a size low, very low.
    Fact is that in such algorithms one should use the parameters which work best for your purposes: Dembski’s and Marks’s wanted to be able to apply the approximation they developed in the appendix of their paper, so they used these values (while a generation size of 9 and a mutation rate of .92% would have resulted in much fewer evaluations of the fitness function, ~1600 instead of 55,000 on average)
    As for Dawkins: for population sizes smaller than 1000 – and bigger than 15, a mutation rate of ~ 4% results in the least number of generations an average…

  6. …using an weasel algorithm similar to Random Mutation.

  7. Dr Dembski:

    My thoughts:

    A reasonable conclusion on the balance of evidence to date, especially noting that Weasel did seem to have been circulated in the late 1980′s.

    And, the conclusion is suitably provisional.

    GEM of TKI

    PS: parlar: the Weasel pgms and publicised runs were used to persuade many people that chance variation and natural selection could account step by step for the origin of complex functional information based features of life, i.e that the blind watchmaker thesis is credible. It is important to understand how they actually worked, and that hey in fact begged the question on origin of such information, through rewarding increments in proximity to a built-in target [thus Weasel is NOT "blind"] for NON-FUNCTIONAL “nonsense phrases.” In fact, ironically, weasel illustrates the power of intelligent design.

  8. DiEB,

    What is the program “modeling”?

  9. Actually, I have not read the blid watchmaker. Maybe I should. If the programs are used as proof and not just as illustrations of evolution that could be misleading, depending on exactly how they were used and what claims were made. I have also not studied the programs in detail either but, despite probabilistic inadequacies,they might still capture some aspects of the evolutionary process. What claims were actually made?

    It’s important to understand what a model is (and istn’t). No model is ever perfect, but they can be used to generate hypothesis which then can be rejected or gain support by experimental testing.

    Our understanding of evolutionary processes and the sofistication of the models have moved a long way since 1987.

  10. Parlar:

    Weasel 1986 is fundamentally dis-analogous to any BLIND watchmaker mechanism for evolution, as it is a “target[ed]” search that promotes admittedly non-functional “nonsense phrases” in making increments in simple proximity to a target.

    That is why it is fundamentally “misleading in important ways,” and why it should never have been used.

    GEM of TKI

  11. Okay, now I haver read the wikipedia entry on the weasel program. I don’t understand what the fuzz is all about, though.

    The weasel is a simplistic program meant to illustrate how complex patterns principally can arise by (natural) selection for intermediate steps. The program was surely never meant or claimed to accurately model evolution of genes?

    The demonstration relies on the fact that an optimim exists (which here is the phrase that emerges when the program is run, in the wikipedia example “METHINKS IT IS LIKE A WEASEL”). In real life, of course, many different optima can exist and the paths will depend on effective population sizes (Ne) and selection pressures (S), and chance. Thus an interplay with the environment.

    What is the problem? That evolution occurs in the direction of optima, is that what the ID movement disapproves of?

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