Evolution and the NFL theorems

_{Mark

December 30, 2007

Intelligent Design}

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Ronald Meester CLICK HERE FOR THE PAPER Department of Mathematics, VU-University Amsterdam,

“William Dembski (2002) claimed that the NoFreeLunch-theorems from op-
timization theory render Darwinian biological evolution impossible. I
argue that the NFL-theorems should be interpreted not in the sense that the models can be used to draw any conclusion about the real biological evolution (and certainly not about any design inference), but in the sense that it allows us to interpret computer simulations of evolutionary processes. I will argue that we learn very little, if anything at all, about biological evolution from simulations. This position is in stark contrast with certain claims in the literature.”

This paper is wonderful! Will it be published? It vindicates what Prof Dembski has been saying all the time whilst sounding like it does not.

“This does not imply that I defend ID in any way; I would like to emphasise this from the outset.”

I love the main useful quote it is a gem!

“I will argue now that simulations of evolutionary processes only demonstrate good programming skills – not much more. In particular, simulations add very little, if anything at all, to our understanding of “real” evolutionary processes.”

“If one wants to argue that there need not be any design in nature, then it is hardly convincing that one argues by showing how a well-designed algorithm behaves as real life is supposed to do.”

Comments

kairos says,
Moreover, in this sense I observe that both Haggstrom and Meester DID NOT cite what David Wolpert (who did invent NFLT) wrote in his paper on IEEE Transactions on Evolutionary Computation), Dec. 2005: “in the typical coevolutionary scenarios encountered in biology, where there is no champion, the NFL theorems still hold.” What about this?
You're engaged in the logical fallacy known as appeal to authority. Would you care to quote their argument in support of this conclusion? You won't find it. This is something the special-edition editors and reviewers let pass that they should not have. Last time I searched the web for the paper, I hit upon an early version that was quite different from the published version. I have a hunch that the reviewers called for many changes, and the little proclamation did not rise to threshold. Wolpert had previously said the opposite, and had given a good argument here:
[...] neo-Darwinian evolution of ecosystems does not involve a set of genomes all searching the same, fixed fitness function, the situation considered by the NFL theorems. Rather it is a co-evolutionary process. Roughly speaking, as each genome changes from one generation to the next, it modifies the surfaces that the other genomes are searching. And recent results indicate that NFL results do not hold in co-evolution.
Evidently he thought that the coevolutionary "free lunch" results he and Macready were developing would apply to biological evolution. My best guess is that the two lapsed into thinking NFL applied when they determined that their results on coevolution did not. But if you examine the argument, you can see that it is fine without the last sentence. It includes elements of what Behe has said recently, and is similar to what English had said in 1996:
Do the arguments of [NFL] contradict the evidence of remarkable adaptive mechanisms in biota? The question is meaningful only if one regards evolutionary adaptation as function optimization. Unfortunately, that model has not been validated. It is well known that biota are components of complex, dynamical ecosystems. Adaptive forces can change rapidly and nonlinearly, due in part to the fact that evolutionary adaptation is itself ecological change. In terms of function optimization, evaluation of points changes the fitness function.
"Everything new is made old again." And as Park said in GA-List discussion of NFL in 1995, almost all functions are "too big" for physical realization. Thus there were two arguments against the applicability of NFL results to real-world optimization before Wolpert and Macready published their first paper. Wolpert concurred with English later, but mysteriously changed his mind.Semiotic 007_{January 1, 2008
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atom, tribune7 The whole point of mma was to show that while you can do all the kata you want and show off your black belt in super tiger dragon ninja kung fu, unless you can continually test your skills against live resisting opponents, you can't say that you have an effective fighting system.ari-freedom_{January 1, 2008
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I tried to come up with a leprechaun simulation but the results don't correspond to reality. I concluded that this is what one would expect as leprechauns are complex nonlinear systems, sensitive to initial conditions.ari-freedom_{January 1, 2008
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Another thing w/regard to Dave's point about the real world and models. When you apply Dembski's EFs to the real world (i.e. objects of known design), they correlate.tribune7_{January 1, 2008
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Another MMA fan? Yes, I got hooked :-) And PaV, Happy New Year to you. And Happy New Year to you. And Happy New Year to you :-) (OK, there is some problem posting this morn.)tribune7_{January 1, 2008
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Another MMA fan? Yes, I got hooked :-)tribune7_{January 1, 2008
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Happy New Years to all!! Best Wishes for the coming Year. What Haggstrom argues in his June 2006 paper that what NFL theorems really mean is that in the distribution of the set |S|^|V| the points alongside any individual point are ‘independent’ of one another. In particular, that the function (and when we start talking about evolution, this will then become a ‘fitness function’) generated, i.e., f: V?S, has values in |S|^|V| that are independent from one another. This means that as one moves away from any particular point, the value of the function at that point will have no correlation to the points next to the original point. And it is for this reason that any kind of ‘linked’ search is no better than a ‘blind’ search. That’s the mathematics; and I think he is to be lauded for this insight. But what comes next, is not, strictly speaking, mathematics. Haggstrom goes on from this above conclusion to state that what his observation implies is that any ‘fitness’ function found in nature would, essentially, have no landscape since his conclusion demonstrates that ‘fitness’ would, on average, fall off precipitously. [[We normally see pictures of curves rising up from a plane, reaching some maximum, and then returning to the plane. We’re told that this is what the landscape of fitness functions look like. Unfortunately, when real experiments are done, giving real results, this isn’t what we see. What we see are fitness functions that fall off so precipitously that even drawing a line straight up out of the plane is not sufficient to characterize it. ]] So, Haggstrom goes on to say: “We could, if we wanted to, dismiss Dembski’s application as irrelevant on the grounds that no physical or biological mechanism motivating (7) [which is the equation that Haggstrom derives based on ‘independence’] has been proposed.” This sentence ends with footnote #11. This is how the footnote reads: “In the hypothetical scenario that we had strong empirical evidence for the claim that the true fitness landscape looks like a typical specimen from the model (7), then this evidence would in particular (as argued in the next few paragraphs) indicate that an extremely small fraction of genomes at one or a few mutations’ distance from a genome with high fitness would themselves exhibit high fitness. It is hard to envision how the Darwinian algorithm A could possibly work in such a fitness landscape.” So what Haggstrom is saying is this: If, indeed, fitness functions in nature can be characterized by a uniform distribution, then the NFL theorems apply. But this would mean that the fitness functions would exhibit ‘landscapes’ wherein that “an extremely small fraction of genomes at one or a few mutation’s distance from a genome with high fitness would themselves exhibit high fitness.” And, the implication of this is that NS could not function since ‘blind search’ would never find its intended target given the size of the |S|^|V| spaces created by real-world proteins. Now enter Behe’s “The Edge of Evolution”—specifically, the PfCRt protein of P. falciparum, the malarial parasite. In P. falciparum’s life-and-death struggle with Chloroquine scientists have learned that this ‘pump’ protein, PfCRT, begins to ‘leak’ due to two amino acid changes at positions 76 and 220. PfCRT is 424 amino acids long. Well, let’s do some math. [Haggstrom is probably aware of so-called ‘neutral’ mutations. A fair amount of the length of most proteins can tolerate random changes from one a.a. to another. It’s because of this variability, I suppose, that Haggstrom thinks that Dembski’s treatment of NFL can be dismissed “as irrelevant on the grounds that no physical or biological mechanism motivating (7) has been proposed.”] Assuming that 60% of PfCRT is ‘neutral’, that leaves 40% of PfCRT, or, 170 a.a. that are not. We see that PfCRT, in its titanic struggle with Chloroquine, involving more duplications/replications than probably all mammals from the time that mammals began to exist, can only come up with 2 substitutions to ward off the effects of Chloroquine. That is, 2 out of 170 unchanging a.a.s, or 1 out of 85. We know that each a.a. is coded for by 3 nucleotides. We know---not from Dembski or the ID movement, but from scientists themselves---that Universal Probability Bound is 10^-150. [Haggstrom, in his paper, uses spaces that range from 10^1,000 to 10^1,000,000,000. But we need not concern ourselves with that size space.] This is equivalent to 2^500. It is also equivalent to 4^-250. Thus, in PfCRT, we have real-world test of its ‘fitness landscape’. What do we find? That, at most, only 2 a.a. can be substituted. Assuming 60% of the protein is free to mutate, we find that 2 out of 170 stable (therefore, necessary) a.a.s change. That is: 1 in 85. There are four nucleotides. 85 a.a.s represents 255 nucleotides. Thus, in the real-world, there is a 1 in 4^255 chance, or 4^-255, of being able to substitute for needed/conserved a.a.s. This is exactly the kind of ‘fitness landscape’ that Haggstrom suggests we would find if the NFL theorems, and Dembski’s analysis of them, were really true in the real world of nature. So, Haggstrom has done us a favor. His analysis provides us with a look at what ‘fitness landscapes’ would look like given a uniform distribution. Do some of you remember Dr. Oloffson visiting here and arguing we should use the maximum-likelihood approach, but that this wasn’t possible because we didn’t know what the probability distribution was like? Well, now we can answer that we do know what it is like: it’s a uniform distribution. This confirms Dembski’s theoretical work. And it means that ‘blind chance’, working in the natural order, cannot create the PfCRT protein since the possibility of ‘blind chance’ doing that in the natural order exceeds the UPB. Using the Explanatory Filter, that leaves only intelligent agency as an explanation for such 'fitness landscapes'. Q.E.D. “Game. Set. Match.”PaV_{January 1, 2008
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...why his older brother can beat up the entire Gracie family
Another MMA fan? I say Rickson takes it by armbar. ;) Sorry, I'm a huge one and that comment distracted me for a sec. hehe.Atom_{January 1, 2008
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Another point: it seems that the rebuttal to Dembski et al can be summed up with the claim that it is reasonable to think things such as amino acids randomly combine in the proper sequence to form proteins, or more to the point, DNA coding happened by accident. It is not reasonable. It is silly. The counter-arguments to Dembski are resembling the ever shriller reasons a second-grader provides to skeptical classmates as to why his older brother can beat up the entire Gracie family. And for what purpose? To show that there ultimately isn't one? It's a depressing waste of brainpower, not to mention time.tribune7_{January 1, 2008
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semiotic When you say that initial conditions for a model can't be obtained from the real world what you're really saying is the model is bogus. It's not a model or simulation unless there's something in the real world to compare it against. Look up the words "model" and "simulation" for Pete's sake. By definition what you're describing are not models. They're nothing more than woolgathering. Evolution "researchers" do a lot of woolgathering. So much in fact it's not easy to determine what else, if anything, they really do.DaveScot_{January 1, 2008
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What about crop simulations? Simulations in computational fluid dynamics (i.e., with high Reynolds numbers)? Simulations (statistical) of communications networks? Are you saying they don't reflect real world results?tribune7_{January 1, 2008
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#37 PaV
I’ve just finished reading Haggstrom’s June 2006 paper which Meester relies on.
I’ve just finished reading it too.
I would direct your attention to footnote #11. This paper was written before Behe’s new book. As I see it, based on what Haggstrom says in the footnote, and based on the results Behe provides in EoE, it is now, as they say, “Game. Set. Match.”
That's true, but IMHO Haggstrom’s argument is completeky invalid. Evolution cannot be considered as a single fittness function but as a very complicated and dynamic set of billions ones. In this context it'no sense to state that local regularities in the landscape space could be signs that in biology NFLT doesn't actually hold. Indeed, they are more and more signs that some teleological process did produce them, ar Dembski and Marks have convincingly argued in their paper that addresses Haggstrom’s argument. Moreover, in this sense I observe that both Haggstrom and Meester DID NOT cite what David Wolpert (who did invent NFLT) wrote in his paper on IEEE Transactions on Evolutionary Computation), Dec. 2005: “in the typical coevolutionary scenarios encountered in biology, where there is no champion, the NFL theorems still hold.” What about this?kairos_{January 1, 2008
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Off Topic: Maybe a new listing could be done for this: http://www.futurepundit.com/archives/004492.htmlmike1962_{December 31, 2007
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Atom, I got the link from one of DLH's post #4. See: Olle Häggström: Some recent papers (PaV - I Added the link DLH.)PaV_{December 31, 2007
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PaV, Do you have a link to the Haggstrom paper? (I'd like to see the footnote you refer to.) ThanksAtom_{December 31, 2007
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Look at this man's picture! Quick! Someone give him a laxative! GloppyGalapagos Finch_{December 31, 2007
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Semiotic @34 "Unfortunately, other parties inspecting the source code found serious defects that make all of the experimental results dubious. I wish Marks and Dembski would withdraw the paper from consideration." I understood that Dembski & Marks had withdrawn that earlier draft. They then substantially rewrote that paper and posted a second draft at Evolutionary Informatics Lab. See: "Unacknowledged Information Costs in Evolutionary Computing: A Case Study in the Evolution of Nucleotide Binding Sites." William Dembski, could you please confirm/comment.DLH_{December 31, 2007
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Just a reminder of the severe discrimination against ID, and anyone remotely appearing to support ID or even publish results of experiments or modeling that could be construed to be supportive of ID - especially in the USA. Consequently, strongly recommend that anyone starting out in science, or who does not yet have tenure, should prudently use a pseudonym when posting such materials or comments on them, especially at Uncommon Descent or other ID friendly blogs.DLH_{December 31, 2007
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Is this ’simulation-ese’ for Natural Selection?
"bounded arena" AND evolutionSemiotic 007_{December 31, 2007
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DaveScot, Your two approaches to evaluation of models of processes exclude a great deal of what happens in practice. What about crop simulations? Simulations in computational fluid dynamics (i.e., with high Reynolds numbers)? Simulations (statistical) of communications networks? You cannot peg initial or present conditions in these cases. See my comments on evolutionary systems as complex nonlinear systems, sensitive to initial conditions, in #26. Arbitrarily small differences in initial conditions lead to exponential divergence of system trajectories. You cannot measure any physical system with absolute precision, so evaluation of the fit of a simulation model to a natural evolutionary process in the senses you are accustomed to is impossible.
Can you describe how “simulations” or “models” (using the words very loosely) of biological evolution were benchmarked against the real world in order to assess the validity of the so-called model?
See #38. To rephrase what I have emphasized above, property Z is likely to be qualitative, not quantitative. Have you ever seen the simulation of schooling behavior of fish that imbues each fish with just 3 or 4 simple rules? The simulation does not precisely predict what any school actually does, but the features of the simulated school are remarkably like what we see in simulated schools. This does not mean that living fish actually act according to the rules of the simulated fish, but it shows definitively that very simple fish behavior can account for the complex behavior of schools. Obviously my example is not drawn from evolutionary simulation, but I am trying to bridge the gap by referring to a famous simulation you may well have seen, and for which it is easy to understand the value of capturing qualitative behavior of a complex nonlinear system. By the way, there are hurricane simulations that do not predict tracks, but do provide insight into the dynamics of cyclonic storms. I've stood in the middle of a forming cyclonic storm, in a virtual reality hut. The meteorology researchers say that the combination of simulation (based on their limited understanding of hurricanes) with visualization has done a lot to advance their understanding. I believe the precise "track" of evolving life on earth is much less predictable than that of a hurricane, but I do believe that simulations can be used analogously to improve understanding of evolutionary dynamics. Most evolutionary biologists avoid math and computing like the plague, however, and that, in my opinion, is why there has been relatively little work in biologically-relevant simulation of evolution.Semiotic 007_{December 31, 2007
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Semiotic 007: By the way, fitness functions are not essential to evolutionary theory. Competition in a bounded arena is. Is this 'simulation-ese' for Natural Selection?PaV_{December 31, 2007
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36 Davescot Thank you for expressing my thinking a lot better than I could have myselfari-freedom_{December 31, 2007
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The Publications page at Evolutionary Informatics Lab appears to be working again.DLH_{December 31, 2007
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allanius, Hypothesis: Property Z of a natural process is caused by conditions X and Y. Empirical test: Create conditions X and Y in a computational process, and see if the process exhibits property Z. Note: If parameter tweaking is required to obtain Z in the computational process, then that may lead to hypothesis revision. There is nothing whatsoever that precludes implementation of a "virtual laboratory" in software and conducting unbiased experiments within it. The researcher has the laboratory in mind when designing the software, not the outcome of the experiments that will be conducted in the laboratory. The situation is not one whit different from that when a chemist designs a physical lab. (In fact, today's labs often include virtual instrumentation.) By the way, fitness functions are not essential to evolutionary theory. Competition in a bounded arena is. We refer to individuals that survive and reproduce as more fit. That does not force us to include explicit fitness functions in models of evolution. Simulation models of coevolution often have no explicit fitness function. Meester seems unaware of this.Semiotic 007_{December 31, 2007
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I've just finished reading Haggstrom's June 2006 paper which Meester relies on. I would direct your attention to footnote #11. This paper was written before Behe's new book. As I see it, based on what Haggstrom says in the footnote, and based on the results Behe provides in EoE, it is now, as they say, "Game. Set. Match." I won't say any more until others have had time to put this all together. But for right now, let me just say this: Haggstrom's argument against Dembski is NOT mathematical; he's using his notion of 'reality' to dismiss Dembski's arguments. (And, for the most part, it won't 'fly'. )PaV_{December 31, 2007
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semiotic Models and simulations are normally used to predict how things will behave in the real world. As such they are tested in two ways: 1) they are set up with initial conditions obtained from the history of the real world and run backward and forward from that time to see if they agree with what was produced in the real history of the world. 2) they are set up with present conditions in the real world, run forward faster than the real world, and then their predictions are compared to reality. Once the model or simulation has demonstrated a capacity for accurately reproducing real world results then and only then does anyone with a lick of common sense begin using them for practical purposes like predicting hurricane paths and taking costly precautions to limit loss of life and property, or committing a microprocessor design to silicon, or anything else that people in the design world affectionately refer to as "bending metal" to describe the costly things that modeling and simulation make less costly through accurate prediction. The common thread here is that models and simulations, the real ones that have merit to real scientists and real engineers (true Scotsmen notwithstanding) doing things that have meaning and impact in the real world, are benchmarked against reality. Anything else is woolgathering. Can you describe how "simulations" or "models" (using the words very loosely) of biological evolution were benchmarked against the real world in order to assess the validity of the so-called model?DaveScot_{December 31, 2007
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[...] 31, 2007 · No Comments A writer named idnet.com.au over on Uncommon Descent has found what looks like a rather interesting paper by Ronald Meester. I hope to peruse it soon and will [...]No Free Lunch? « Professor Smith’s Weblog_{December 31, 2007
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dgw, Model validity is a matter of degree. Newton's model of mechanics is not categorically invalid because Einstein's model makes more accurate predictions. And model utility involves more than accuracy of its predictions. Engineers get more use from Newtonian mechanics than they do Einsteinian mechanics. I last read an ev paper several years ago, and I cannot say whether Marks and Dembski understand it or not.
1. Is ev a valid simulation of the evolution of biological information?
Even if I had read the paper last hour, I would tell you that I am not a life scientist, and that I cannot assess the biological relevance of the simulation. (That term "biological relevance" is a good one to know.)
2. Do Dembski and Marks successfully refute the claims of this algorithm?
Their paper relies heavily on data collected with their own software for simulation (in the sense of numerical experimentation). Unfortunately, other parties inspecting the source code found serious defects that make all of the experimental results dubious. I wish Marks and Dembski would withdraw the paper from consideration.Semiotic 007_{December 31, 2007
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A man cannot build a house unless he has a house in mind, and a man cannot simulate an evolutionary process unless he has evolutionary processes in mind. To use such simulations to justify Neo-Darwinism, then, is tendentious, since nature has nothing whatsoever in mind; since the divide between intellect and matter is absolute. Now if our favorite Secret Agent Man wants us to believe that this is not the real purpose of such simulations--that they are nothing more than pristine academic experiments in computer engineering--then he has simply proven Meester's point. They tell us a great deal about the ingenuity of the programmers and little or nothing about nature. But how can we resist the temptation to suspect that he's wearing another one of his clever disguises?allanius_{December 31, 2007
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Behe uses Lenski's research as the type of research that supports ID. I bet Lenski does not like that endorsement.jerry_{December 31, 2007
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