Identifying what the designer does – stealing bikes!?

 

“The reason a bike lock works,” explains Meyer, “is that there are vastly more ways of arranging those numeric characters that will keep the lock closed than there are that will open the lock.”

Most bicycle locks have four dials with ten digits. So for a thief to steal the bike, he would have to guess correctly from among 10,000 possible combinations. No easy task.

But what about DNA? Well, in experiments Axe conducted at Cambridge, he found that for a DNA sequence generating a short protein just 150 amino acids in length, for every 1 workable arrangement of amino acids, there are 10 to the 77th possible unworkable amino acid arrangements. Using the bicycle lock analogy, that’s a lock with 77 dials containing 10 digits.

http://www.evolutionnews.org/2015/10/eric_metaxas_on_1100261.html

I believe this is what Mung has been talking about. I asked Mung:

How many goes do you get? How many bacteria in the earth’s soil?

Mung replies:

Not nearly enough.

I feel this is interesting enough for an OP as it seems to finally touch upon what IDers think the designer actually does that can be investigated scientifically.

For example, if we find in a population a protein that is different to the version in an ancestral population but which still works, the by (their) definition, that is prima facie evidence of the designer at work.

Perhaps we can then take the population with the original protein, enclose it in our most sensitive equipment and attempt to detect the designers actions when it “solves the bike lock” and finds the new protein and somehow makes the required adjustment?

If I were an ID supporter these are exactly the sorts of experiments I’d be proposing, and with money on the table (Templeton) I continue to be surprised at the lack of such endeavours. At the very least they can rule out some levels of possible designer interaction at the macroscopic level.

And Mung, I’d be interested in knowing how many would be enough?

Earlier during his direct testimony, Behe had argued that a computer simulation of evolution he performed with Snoke shows that evolution is not likely to produce certain complex biochemical systems. Under cross examination however, Behe was forced to agree that “the number of prokaryotes in 1 ton of soil are 7 orders of magnitude higher than the population [it would take] to produce the disulfide bond” and that “it’s entirely possible that something that couldn’t be produced in the lab in two years… could be produced over three and half billion years.”

http://www.talkorigins.org/faqs/dover/day12am.html

 

 

409 thoughts on “Identifying what the designer does – stealing bikes!?

  1. Mung,

    Yeah, well, Allan tells me I’m wrong about there not being a generic GA that can solve any problem then begs me to change the subject from problem solving.

    Like this?

    Mung: I think GA’s are problem-specific and need to be designed for the problem attempted to be solved. Am I wrong about that?

    Allan: Yes.
    […]
    I don’t know why you are fixated on problem solving anyway; that’s not why students of evolution write them.

    Yes, you are wrong about all GAs being problem specific. You could write a GA that chose its own problem at runtime, or had the problem provided as parameter. But as I say, they are not always written to solve problems, hence my ‘begging’ you (heh heh) to change the subject.

    You were not being urged to write a GA because people had a particular problem they needed a solution for.

  2. Allan Miller,

    Do you understand this?

    I do understand this but it is not clear the mechanism that plugs them together in order. Their origin is also not clear. When you say evolution what mechanism do you believe can accomplish all this?

  3. petrushka,

    What matters is not the size of sequence space, but whether it can be explored one step at a time.

    You are saying that it does not matter if the sequential space is 4^4 or 4^40000?

  4. colewd:
    Allan Miller,

    I do understand this but it is not clear the mechanism that plugs them together in order.Their origin is also not clear.When you say evolution what mechanism do you believe can accomplish all this?

    Mutation, drift, selection. Models work, you complain. Eh.
    Why are you not complaining about gravity too? Your demands on evolution are equivalent to demanding that evidence for the precise step-wise formation of the Solar System by accretion is provided. How can gravity account for the exquisite balance of those huge planets, moons, asteroids, etc.. Does gravity need to account for all the steps in the process of the formation of all those celestial bodies?

  5. colewd:
    petrushka,

    You are saying that it does not matter if the sequential space is 4^4 or 4^40000?

    Yes, what matters is the “shape” of the sequence space. Still won’t sink?

  6. dazz: Your demands on evolution are equivalent to demanding that evidence for the precise step-wise formation of the Solar System by accretion is provided.

    The nature of the inability of biology to provide a coherent explanation of evolution is best seen when contrasting it with geology.

    – Dr. Marcel P. Schutzenberger

  7. Is it any wonder I speak in terms of search and problem solving?

    An adaptive landscape (or surface) is a topographic map used to represent the degree of adaptation of individuals in a given environment. Individuals that only reproduce with each other are part of the same species, which occupies one or more biological niche. As the fittest individuals of the population have higher chances of surviving and reproducing, the outcome of evolution is a population increasingly fit to its environment.

    Viewed in this manner, evolution is clearly a search and optimization problem solving process (Mayr, 1988). Selection drives phenotypes as close as possible to the optimum of a fitness landscape, given initial conditions and environmental constraints. Evolution thus, allows the discovery of functional solutions to particular problems posed by the environment in which some organisms live (e.g., a more appropriate color for moths living in a sooty area). This is the perspective assumed when using evolutionary biology as a source of inspiration for the development of evolutionary computation. In such a case, an evaluation function will define a fitness landscape for the problem, and finding the best solution to the problem (most adapted population or individual in a given environment) will correspond to the search for a peak (assuming a maximization problem) of this landscape by subjecting individuals to genetic variation (e.g., crossover and mutation) operators.

    – Fundamentals of Natural Computing

    No, no wonder at all.

  8. Continuing:

    Evolution can be viewed as a search process capable of locating solutions to problems offered by an environment. Therefore, it is quite natural to look for an algorithmic description of evolution that can be used for problem solving. … Those iterative (search and optimization) algorithms developed with the inspiration of the biological process of evolution are termed evolutionary algorithms (EAs). They are aimed basically at problem solving

    The basic idea of the field of evolutionary computation…has been to make use of the powerful process of natural evolution as a problem solving paradigm

    – Fundamentals of Natural Computing

  9. Mung, you are approaching the scordova event horizon. I suspect you will never escape.

  10. colewd: I have to give credit for creativity 🙂

    Of course you do. Just as I must decline any undeserved credit: I was not being creative.
    😉

  11. Mung:
    Is it any wonder I speak in terms of search and problem solving?

    No, no wonder at all.

    Well, Dembski, Ewert, and Marks represent evolution as “evolutionary search”. Then they get theorems that are supposed to persuade people that these searches cannot do better than blind search. That would be a huge result, if true. But Tom English and I have argued at PT and here that their theorems do not do the job. Mung seemed to want to argue that evolution “was” a search, in order to be able to use DEM’s result.

    We asked Mung for clarification, and Mung acknowledged that no, Mung had no counterargument to ours. So it can’t be that Mung is trying to establish that evolution is a search for that reason.

    So … for what reason? What follows if evolution is a search? The world wonders, but at this stage the world is getting bored by the issue.

  12. Mung,

    Is it any wonder I speak in terms of search and problem solving?

    Not at all. As has been regularly acknowledged, GAs are utilised to do both search and problem solving.

    What is a wonder is that you continue to speak solely in terms of search and problem solving.

  13. colewd,

    I do understand this

    If you understand this, are you prepared to drop the notion that a novel protein of 300 canonical amino acids must be found by random pick from a space with 20^300 members?

    but it is not clear the mechanism that plugs them together in order.

    Iterated evolution.

    Their origin is also not clear.

    Not relevant to evolution.

    When you say evolution what mechanism do you believe can accomplish all this?

    Recombination (which includes duplication, transposition, inversion).

    I don’t need to spell it out that I am being deliberately vague, because I still don’t think you get the modularity issue. You keep writing variations on v^n, total space size of which the complete sequence of n bits is a member.

  14. A point I have made before: if one went from a library with 4 acids to one with 5, a 100-acid peptide would go from 1 of 10^60 to one of nearly 10^70. Does that make evolution less likely, simply because the space is nearly 10^10 bigger? That seems a ridiculous notion. You have an extra acid, so you have more dimensions to go at, and an option for greater subtlety and range. Keep adding acids, 1 by 1, from 5 to 10, and you make the space 10^30 times bigger than when you started. Should this make evolution stop? How about 10^20?

    Likewise, if one added a ‘tail’ to a protein by mutating a STOP, the number of acids added would depend upon how far one would have to go to reach the next STOP. Adding 1 acid to 100 makes the space just 20 times bigger. But adding 10 acids makes the space 10^13 times bigger. Again, this is unlikely to stop evolution dead in its tracks – such tails are not universally lethal – despite the exponential increase in size of the associated space with increasing length.

    So something about the assumption must be awry.

  15. Mung: The nature of the inability of biology to provide a coherent explanation of evolution is best seen when contrasting it with geology.

    – Dr. Marcel P. Schutzenberger

    What a huge pile of BS. This is what IDiots do, adapted to geology:

    “But natural processes can’t explain those layers in the Grand Canyon! How can blind luck produce such patterns?”

    – [scientific explanations provided]

    “Bu, bu, but you can’t explain how those sediments got there in the first place, gawd mustadunnit!!!1!!1!!one”

  16. I’ve ignored Mung, suggest it’s a path to making this site about something other then Mash Until No Good.

  17. Mung (quoting de Castro): Viewed in this manner, evolution is clearly a search and optimization problem solving process (Mayr, 1988).

    You certainly do have a well refined bullshit detector, Mung, which is to say that you’re remarkably good at finding bullshit that suits your purposes. The passage you quote is just spew by someone rushing through a semblance of a survey.

    Mayr discusses his distinction of teleonomy and teleology in Toward a New Philosophy of Biology (1988). De Castro could not be more wrong in his attribution. He probably lifted the reference from a source he actually had read — David Fogel’s Evolutionary Computation: Toward a New Philosophy of Machine Intelligence would be my guess.

  18. Mung: Is it any wonder I speak in terms of search and problem solving?

    Is it any wonder that you should turn to a computer scientist who refers to “evolutionary biology as a source of inspiration for the development of evolutionary computation” as a source of inspiration for explanations of biological evolution?

  19. Joe Felsenstein,

    If evolution is a search then evolution was designed to be so. If evolution isn’t a search then it is impotent and cannot do what evolutionists need it to do

  20. Tom English: Mayr discusses his distinction of teleonomy and teleology in Toward a New Philosophy of Biology (1988). De Castro could not be more wrong in his attribution.

    To be sure, selection is an optimization process, but one of a very special kind. It is neither teleologically programmed nor controlled by any law, but is strictly opportunistic.

    – Ernst Mayr. Toward A New Philosophy of Biology. p. 105

    You see Tom, I did my homework.

  21. A good way to order our understanding of any living creature is to imagine, fancifully and with something more than poetic license, that it (or, if you prefer, a hypothetical ‘designer’ of the creature) faces a chain of problems or tasks. First we pose the initial problem, then we think of possible solutions that might make sense. Then we look at what the creatures actually do. That often leads us to notice a new problem facing animals of this kind, and the chain continues.

    – Climbing Mount Improbable

    From a given starting point, a path which goes ever upward, never downward, is the path that natural selection would follow. If you ran the model in evolutionary mode, natural selection would follow that path. So it saves computer time if we search systematically for upward paths and for peaks that can be reached from postulated starting points. The important thing is that the rules of the game forbid going downhill. This more systematic search for upward paths is what Nilsson and Pelgar did…

    – Climbing Mount Improbable

    Even Richard Dawkins can’t help himself.

    I focus on problem solving, Allan, because that’s the way the evolutionary process is presented. Not because I want to ignore the existence of some GA that doesn’t solve any problem.

  22. Joe Felsenstein: Mung seemed to want to argue that evolution “was” a search, in order to be able to use DEM’s result.

    We asked Mung for clarification, and Mung acknowledged that no, Mung had no counterargument to ours. So it can’t be that Mung is trying to establish that evolution is a search for that reason.

    I don’t think I’ve ever felt the need to try to apply the DEM paper to biological evolution.

    My interest rather lies in the intersection between EAs/GAs and biological evolution. And this primarily because the topic keeps coming up as somehow being relevant to biological evolution. Say it isn’t so.

    If evolution is not a search, then why is it appropriate to model it as a search? I’m just trying to get folks to face their confusion. 🙂

  23. Mung: If evolution is not a search, then why is it appropriate to model it as a search? I’m just trying to get folks to face their confusion.

    Evolution is or isn’t a search in the same way GAs are or aren’t searches. If instead of obsessing over a god damn word you made the effort to actually understand what’s going on in a GA perhaps your confusion would go away

  24. Mung: This more systematic search for upward paths is what Nilsson and Pelgar did…

    On reading what Nilsson and Pelgar did (PDF), I’m not sure what point you’re making. Their criterion was “select for improvement in optical performance”. It’s a GA, no?

  25. dazz: Evolution is or isn’t a search in the same way GAs are or aren’t searches.

    I’ve seen the light! Hallelujah!

  26. Allan Miller,

    I don’t need to spell it out that I am being deliberately vague, because I still don’t think you get the modularity issue. You keep writing variations on v^n, total space size of which the complete sequence of n bits is a member.

    I am sure you understand the modularity issue better than I do. I know it reduces the potential search space but I am not sure you have internalized how large the search space really is and n can be converted from bits to bytes to account for modularity. I think your point is dead wrong that the origin of modules is irrelevant to evolution unless you claim that they were all available at the origin and that creates its own interesting challenge. Can the modularity issue over come it? I am very skeptical here but certainly open to education.

  27. colewd: unless you claim that they were all available at the origin

    What was present at the origin of life? You make it sound like the first replicator was like a ‘modern’ cell.

  28. OMagain: You make it sound like the first replicator was like a ‘modern’ cell.

    The first replicator was like a ‘modern’ cell.

  29. OMagain:
    I’ve ignored Mung, suggest it’s a path to making this site about something other then Mash Until No Good.

    Hey, I like that!

  30. Ernst Mayr: To be sure, selection is an optimization process, but one of a very special kind. It is neither teleologically programmed nor controlled by any law, but is strictly opportunistic.

    Mung: You see Tom, I did my homework.

    E for effort, F for follow-through.

    Searches that operate by Darwinian selection, for instance, often significantly outperform [have much higher probability of hitting a prespecified target than] blind search. But when they do, it is because they exploit information [about the target] supplied by a fitness function— information that is unavailable to blind search. Searches that have a greater probability of success [in hitting a prespecified target] than blind search do not just magically materialize.

    […]

    The challenge of intelligent design, and of this paper in particular, is to show that when natural systems exhibit intelligence by producing information, they have in fact not created it from scratch but merely shuffled around existing information. Nature is a matrix for expressing already existent information. But the ultimate source of that information resides in an intelligence not reducible to nature. The Law of Conservation of Information, which we explain and justify in this paper, demonstrates that this is the case. Though not denying Darwinian evolution or even limiting its role as an immediate efficient cause in the history of life, this law shows that Darwinian evolution is deeply teleological. Moreover, it shows that the teleology inherent in Darwinian evolution is scientifically ascertainable—it is not merely an article of faith. [Evidently knowledge that targets were prespecified, and furthermore knowledge of what those targets were, does magically materialize poof into existence.]

    Dembski and Marks (2009), “Life’s Conservation Law: Why Darwinian Evolution Cannot Create Biological Information.”

    Joe Felsenstein and I demolished their claims about the Law of Conservation of Information. It basically comes down to this: we can measure active information on whatever event we like, after observing what tends to occur in nature. The CoI theorems are relevant to engineering, not to science, because they predicate that an event is targeted before the “search” process occurs.

  31. Why do I put “search” in scare quotes? Even evolutionary computations do not actually search. I don’t care how many “experts” you quote with boldface added. The meaning of “no free lunch” is that it is that the human, not the processor of data emitted by a black box in response to inputs, that searches.

  32. Mung: My interest rather lies in the intersection between EAs/GAs and biological evolution. And this primarily because the topic keeps coming up as somehow being relevant to biological evolution. Say it isn’t so.

    It does keep coming up. And it annoys the hell out of me. Evolutionary computation is appropriately described as biologically inspired. Biological evolution is not inspired by evolutionary computation.

    If evolution is not a search, then why is it appropriate to model it as a search? I’m just trying to get folks to face their confusion.

    It is not a search. (I hadn’t read this when I left my previous comment.) Computational processes operating on black-box functions are utterly uninformed. They take no input describing a problem, and thus cannot be regarded as solving problems themselves. They are merely data processors, and the form of data processing is merely sampling, fanciness notwithstanding.

    Only the human who knows what the problem is can possibly act informedly. A computational process operating without a description of a problem obviously is not solving a problem. It really is that simple. But some simple things are hard to see. And I assure you, plenty of folks working in evolutionary computation have not seen what it is that they’re actually doing. (People tend to attribute their purposes in using computer programs to the programs themselves. The fact that we are searching for solutions when we initiate evolutionary computations does not mean that the computational processes themselves are searching.) Furthermore, it’s taken me a long time to figure out how to say it simply.

    If you can spare time from munging, and ask straight questions, I’ll give you straight answers (tomorrow).

  33. Tom English: Why do I put “search” in scare quotes? Even evolutionary computations do not actually search.

    I would like to know why you don’t likewise put “selection” in scare quotes.

    ETA: Do you also hold that computers do not actually compute?

  34. Mung: I would like to know why you don’t likewise put “selection” in scare quotes.

    ETA: Do you also hold that computers do not actually compute?

    Natural “selection” fully deserves scare quotes. Darwin used it in analogy to the artificial selection in breeding. He preferred natural preservation, in later years. That doesn’t strike me as much better. Left to my own devices, I go with differential reproduction. When Joe Felsenstein advises me to stick with familiar terms, to avoid confusing the reader more than I already am, I take heed.

    No biologist is trying to con people into believing that natural “selection” really does select. Some Iddites have tried to con people into believing that biological evolution really does search. So when I write “search” in connection with the Movement of Id, the scare quotes have different significance than when I write “selection.”

    It wasn’t terribly long ago that computer was the job description of someone who used a calculator to compute results according to unambiguous instructions given them. The intent was that they function mechanically. Our application of the term now to stored-program data processors is not by analogy. Electronic digital computers originally operated as the humans equipped with calculators ideally would have.

  35. The main thing mung can’t get is that reality doesn’t change because someone chooses their words badly or makes limited analogies or metaphors.

    Analogies and metaphors are intended to communicate, not obfuscate.

  36. petrushka: The main thing mung can’t get is that reality doesn’t change because someone chooses their words badly or makes limited analogies or metaphors.

    When the main mechanism you have for progressing your viewpoint is bolding specific types of words in abstracts or press releases and the posting that as “research” then people choosing words badly is in fact a victory.

  37. colewd,

    I am sure you understand the modularity issue better than I do. I know it reduces the potential search space but I am not sure you have internalized how large the search space really is […]

    Get stuffed – I know exactly how big the space is! v^n. It’s a big number when v and n are large. Get over it. Neither v nor n can assumed to be large every step of the way. And absolute size is not the issue – if a space 10000^10000 had 1% functionality, would we give a damn that it was 10000^10000 in size?

    and n can be converted from bits to bytes to account for modularity.

    That makes no sense. I wish people would get over the computing analogies. I guess I asked for it by saying ‘module’. A module in protein terms is a smaller structure than a full protein. We find the same basic structures all over the place, frequently with substantial sequence similarity, highly suggestive of common origin.

    This is precisely why taking the space of the larger peptide is bogus. B-o-g-u-s. Bogus.

    I think your point is dead wrong that the origin of modules is irrelevant to evolution unless you claim that they were all available at the origin and that creates its own interesting challenge.

    When you are talking about the origin of system X, going back to the origin of the things that went into the origin of system X is a massive goal post shift. When you want to know how a flagellum arose in an organism which had no flagellum, asking ‘where did those things come from’ is an irrelevance. But we are actually talking in much more conceptual terms anyway. Given folded small peptides, you can make folded large peptides, even though folded large peptides may be vanishingly infrequent in the larger space. Who thinks peptides arise by randomly bolting together 300 acids individually and seeing what happens?

    Can the modularity issue over come it?

    The peptide modularity issue does not overcome the origin of protein coding, if that is what you mean. But (in tandem with arguments on v, the alphabet size) it does show, conceptually, how a huge repertoire of proteins of length n can be built from an initially smaller set of shorter peptides, probably with fewer than 20 acids at the start. If v is 4 and n is 30, where does your problem go? .

    I am very skeptical here but certainly open to education.

    Board rules require me to take you at your word …

  38. Mung,

    I focus on problem solving, Allan, because that’s the way the evolutionary process is presented.

    What, always, by everybody? You refuse to consider evolution as anything other than a problem-solving search because some people (most of them not biologists) call it a problem-solving search? Heh heh. Good one. How do we go about eradicating these people from the record so that you are free to consider another perspective?

  39. Problem solving is a metaphor or analogy. You can’t reason backward from an analogy to infer limitations on the actual sysrem.

    Well, you could do it, but it would be stupid or intellectually dishonest.

    Either case, not worthy of continued discussion.

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