Evolution and the NFL theorems

Dr Dembski:

There is a name for it: Grudging acknowledgement disguised as disagreement or even claimed refutation.

Telling.

Happy New Year when it comes.

GEM of TKI

We can only simulate that which we fully understand. And seeing that we don’t know what mutations can cause/ caused which changes there is no way we can simulate biological evolution.

It vindicates what Prof Dembski has been saying all the time whilst sounding like it does not.

Be careful what you ask for. Meester also writes

Computing probabilities in a model is one thing, but for these computations to have any implication, the models had better be very good and accurate, and it is obvious that the various models do not live up to this requirement. In particular, it is quite meaningless to compute the probability that certain aminoacids combine to produce a particular molecule, if there is no reasonable mathematical model around.

(emphasis added)

This would mean that the whole approach of calculating CSI of proteins is flawed too, because those probabilities can’t be calculated either.

Bob

Good read. Dr. Dembski, is there an online work where you answer the critique of Haagstrom directly?

Bored young men begin playing around with an inflated pigskin.

Eventually, solely through random mutations and natural selection, a multi-billion dollar league appears and the ball become a prolate spheroid of leather with a polyurethane bladder

In particular, it is quite meaningless to compute the probability that certain aminoacids combine to produce a particular molecule, if there is no reasonable mathematical model around.

OTOH, if your claim is that amino acids can randomly form into a particular molecule and you fail to provide a reasonable mathematical model you are not practicing science.

And note the word “randomly”.

We can’t model it, we can’t calculate it, but we know it happened. Because we’re theoretical scientists!

Thanks Dr. Dembski. I’ll be looking for it when it comes back up.

Atom

After being a long time lurker, both here and at the thumb, I finally have to make a comment. Why? Two reasons:

1. I’m currently finishing up a Ph.D. in experimental evolution of baculoviruses (anyone ever heard of them here?). My thesis mainly concerns the development and validation of models of viral infection and population genetics - my first publications will be out the coming half year. Nevertheless, I’m interested in the philosophical ramifications of evolutionary biology. And I have a Christian background.

2. I’m Dutch - as is R. Meester - and have read some of his popular articles in Dutch and heard a few debates on ID in which he has participated. But enough about me.

What surprizes me greatly is no one has recognized that Ronald Meester is one of the people who started getting ID in the spotlights here in the Netherlands. Granted, he has always taken a somewhat agnostic position with respect to ‘ID proper’, and even more so to any religious/philosophical implications of ID. But, he has really stuck his neck out in order to get people - in scientific and lay circles - thinking about ID. And he has taken a lot of flak for his stance, from both camps. To qualify his position in his latest paper as ‘grudging acknowledgement disguised as disagreement or even claimed refutation’ is skewed. If anything, Meester is a friend of the ID movement, even if he is not (or perhaps no longer) a part of it. I am by no means an ID supporter myself, but cut the man some slack.

Gloppy, that was deep — and incredibly meaningful Thanks a milion!

Meester has this precisely backwards:

Put in yet other words: we cannot expect a search algorithm to be efficient unless we restrict ourselves to functions f that are distinguishable from the “average” f, and I believe that this last formulation is a concise description of the importance of the NFL-theorems.

It follows from basic results in Kolmogorov complexity that almost all of the functions f are algorithmically random. When f is algorithmically random, almost all search algorithms obtain good solutions rapidly. Intuitively, good solutions are no less common than bad ones, and the disorderly function no more hides the good solutions than it presents them.

I have no idea how good a mathematician Meester is in general, but here he has jumped to a conclusion, and he could have avoided it with a better lit review.

Joseph says:

We can only simulate that which we fully understand.

There’s a reason simulations are often referred to as simulation models. When I was a kid, I assembled various models of airplanes. I quickly figured out that I was not learning much about how the modeled aircraft were actually manufactured. But I could have placed some of those models in a wind tunnel and learned about their aerodynamics. That is, I could have simulated the flight of a modeled aircraft and gained important insights into its performance without any knowledge whatsoever of many details essential to fabrication and operation of the aircraft.

If you think that matters change when the simulation model is computational, you have succumbed to some unfortunate mystification of computation.

That is, I could have simulated the flight of a modeled aircraft

Yes, but what is being attempted to be demonstrated by computer models of evolution? That evolution could have occurred without design. It’s like trying to prove you can’t paint a wall blue by painting a wall blue.

Semiotic 007: “…almost all of the functions f are algorithmically random. When f is algorithmically random, almost all search algorithms obtain good solutions rapidly. Intuitively, good solutions are no less common than bad ones, and the disorderly function no more hides the good solutions than it presents them.”

(f)s are supposed to be fitness functions (of base pair configurations in the genome). These are supposedly algorithmically random. A given fitness function could be for visual acuity. Only a tiny part of the genome could be modified to improve this or to degrade this, and the changes would have to be specific not just any to those particular loci. How could almost all search algorithms find these particular configurations rapidly? Intuitively, good solutions are vastly less common than bad ones.

Semiotic 007: “If you think that matters change when the simulation model is computational, you have succumbed to some unfortunate mystification of computation.”

Use of a physical model to investigate a problem is one thing, a computational model another. Try digitally simulating the flight dynamics of the airplane, but with some errors in the aerodynamic constants for computation of lift, drag, etc. We can only validly simulate what we understand.

Maybe I’m wrong, but it seems to me that Meester’s point in all of this is that what Dembski calls the “displacement problem” is not really a problem at all, and that in nature—the ‘real’ world, not that of computers—efficient fitness functions are (I suppose that it would be more proper to say ‘have been’) found. I don’t buy his argument about the “displacement problem”. IIRC, the “displacement problem” says that any effort to find a proper ‘fitness function’ to aid in a search is itself doomed because of the extensive size of the search space of possible fitness functions. This space is extensive because what is being searched for isn’t sufficiently known.

So it looks like Meester wants to conceded that evolutionary algorithms have nothing to do with evolution because he has shown that Dembki’s “displacement problem” is an illusion and that ‘real’ world has its own way of solving these problems.

Again, I don’t buy Meester’s argument. I think Dr. Dembski can point out this error rather immediately.

I have read most of the literature related to the NFL theorems. This paper is the worst I have ever read. I found myself wondering over and over where Meester hopes to publish it. Why in the world did he think he could waltz into a new area, read at most half of the seminal paper from 10 years ago, perhaps all of a paper from last year, and then tell the world what’s what?

Most people who claim to have read Wolpert and Macready’s 1997 article on “No Free Lunch Theorems for Optimization” never bothered with any sections but the early ones. Meester gives absolutely no sign of knowing that he is reinventing Wolpert and Macready’s notion of “alignment” of algorithm and function. Furthermore, he’s doing a lousy job of it. Wolpert and Macready treat the information geometry of optimization with rigor Meester suggests is not possible. Meester seems to be good at attribution, and he is competent to understand what Wolpert and Macready wrote. I can only conclude he did not read all of the article. In fact, I wonder if he read the article at all, because he has not even gotten straight the first NFL theorem of Wolpert and Macready. His Theorem 1 is implied by Wolpert and Macready’s Theorem 1, but is not logically equivalent to it.

Meester seems oblivious to the fact that there can be NFL for non-uniform distributions of functions. He seems oblivious to the fact, which came up in online discussion of NFL in 1995 and has appeared in the literature since, that, for any fixed encoding scheme, almost all functions have codewords too large for realization in the observable universe. And as I mentioned above, he is dead wrong about the difficulty of optimizing the typical function. This is not a matter of hand-waving like his, but of proof.

Meester does write in plain language, however, which no doubt creates the illusion for many of you here that you’ve linked up with something wonderful. There’s not much in the paper that might make you lose bladder control — and that’s a big part of what’s wrong with it. Prof. dr. Meester is clearly a competent mathematician, and if he had bothered to do math, he probably would have caught most of his errors. What you’ve really linked up with is an “expert” who has ventured into seat-of-the-pants pronouncements on something he knows little about. We all make fools of ourselves sometimes, and Meester has been unlucky enough to have you advertise this lapse of his. I hope, for his sake, he does not draw buddies or incompetents for reviewers.

Another way to look at this is that the materialist devises algorithms to demonstrate or simulate aspects of evolution. Dembski uses the NFLT to call foul on these algorithms and Meester agrees. Isn’t it incumbent on the Darwinist to devise a computer simulation that more accurately represents evolutionary processes? Otherwise Dembski’s NFLT refutation of the algorithms and consequently the life processes they claim to represent stands uncontested.

Pav says,

So it looks like Meester wants to conceded that evolutionary algorithms have nothing to do with evolution because he has shown that Dembki’s “displacement problem” is an illusion and that ‘real’ world has its own way of solving these problems.

You are substituting evolutionary algorithm for evolutionary simulation. There is a long history of referring to evolutionary algorithms as “biologically inspired.” More recently, they are often referred to as biomimetic. There was a time when researchers spoke of their evolutionary algorithms as solving problems through “simulated evolution,” but they rarely intended for their findings to be of any use to life scientists. Put simply, evolutionary algorithms are employed predominantly by people with engineering goals.

Systems like ev and Avida support simulation models. What some folks seem not to get is that every model is a simplification of the modeled entity. If it were entirely faithful, it would be a copy. This holds in mathematical modeling as well as in computational models. There was a lot Newton did not understand about mechanics, but I’d say he did well at modeling. Ev and Avida are meant to abstract from biological evolution certain salient features, and to test hypotheses that systems with those features exhibit certain qualitative behaviors observed in nature. (Both systems yield quantities, but the qualitative behavior is what is important. Meester seems not to understand this.)

As Dr. Dembski frames the displacement problem, the meta-search is for an efficient search algorithm, not for a function. The function is given.

Semiotic 007,

On pp. 194-195, Dembski addresses a fitness function that is right out of Meester’s ex. 2, where the fitness function is ‘carefully adapted’ to the target.

Here’s what Dembski says:

“The collection of all fitness functions has therefore become a new phase space in which we must locate a new target (the new target being a fitness function capable of locating the original target in the original phase space). But this new phase space is far less tratable than the original phase space…….To say that E has generated specified complexity within the original phase space is therefore really to say that E has borrowed specified complexity from a higher-order phase space, namely, the phase space of fitness functions. … We have here a particularly vicious regress. …” (p.195)

I don’t see anything at all in what Meester says/presents that overcomes this problem. It would appear that only in the mind of Meester has this problem been solved, and nowhere else.

Isn’t it incumbent on the Darwinist to devise a computer simulation that more accurately represents evolutionary processes?

Which processes? What does it mean to represent them? How do you propose to measure accuracy?

Evolutionary systems are evidently complex nonlinear systems. We should expect them to be sensitive to initial conditions. What it means to model a complex nonlinear system in any domain is a tricky issue, particularly when there is a stochastic component. Investigators in computational quantum physics are having their problems, as well, even though their point of departure is a set of excellent mathematical models, and even though no one asks them dumb questions like “Why can’t you tell us exactly what the trajectories of all the particles in this system will be?”

As I said above, what we hope for in evolutionary simulations is to observe, over many runs, qualitative aspects of biological evolution. When there is sensitivity to initial conditions, no initialization of a simulation is “correct,” and the only way to study the simulated entity is to run the simulation a number of times and somehow characterize the collection of observed results. There is never any way to predict over the long term the precise trajectory of the evolutionary system for a new set of initial conditions.

My very favorite computational study with biological relevance is by David and Gary Fogel. They demonstrated that evolutionary stable strategies, shown stable through a mathematical argument assuming an infinite population (i.e., for mathematical tractability), are anything but stable with modest-sized populations. In fact, it appears that population proportions often vary chaotically over time. The precise numbers do not matter very much. What’s important is the demonstration that when a simulation model operates under assumptions more realistic than people know how to handle mathematically, “stable” strategies are not stable. That’s a qualitative conclusion.

idnet.com.au - you’ve missed a couple of points:

1. If Meester is right and we can’t calculate the probabilities (FWIW, I’m not as pessimistic), then the Explanatory Filter can’t be used to detect design in biological systems. If Meester is right, then the EIL’s activities with regards to biological evolution are pointless.

2. Meester can and does claim Dembski is wrong, on mathematical grounds (he agrees with Häggström’s arguments).

Meester is saying that Dembski’s original argument is wrong (using Häggström’s critique), but he resurrects the general thrust of the “displacement problem” argument to suggest that because search algorithms in computer simulations are designed, they say nothing about evolution.

Meester’s argument is rhetorical, rather than mathematical, and I can see a couple of approaches to critiquing it. But I expect people working on evolutionary simulations will do a better job than me, so I’ll wait and see what they say.

Bob

PaV,

I can’t recall the context from which you’ve drawn the quote, having spent more time with Dr. Dembski’s worthier Searching Large Spaces: Displacement and the No Free Lunch Regress. My guess is that he is referring to a new set of fitness functions constructed using candidate search algorithms.

It would appear that only in the mind of Meester has this problem been solved, and nowhere else.

To restate what I said above, only algorithmically compressible functions “fit into” the physical universe. There’s nothing new in that observation. You simply haven’t heard about it because your information on NFL comes by way of Dr. Dembski, who has not addressed the possibility that fitness landscapes might have predictable features due to compressibility. Yossi Borenstein and Riccardo Poli of the University of Essex have explored this recently.

Matteo:

We can’t model it, we can’t calculate it, but we know it happened. Because we’re theoretical scientists!

I had the very same thought when I first read this article. The Lenski et.al. study is continually cited as an “explanation” for evolution produces a complex systems, even though not one single example from any biological system is referenced as confirming the study.

Dawkins wrote in The Blind Watchmaker that evolution is such a “neat theory”. I can see why: we don’t know how it works, we can’t model it, we can’t make any predictions from it, but it explains all of biology! Yep, pretty neat!!

I’m currently finishing up a Ph.D. in experimental evolution of baculoviruses (anyone ever heard of them here?).

Are they viruses that infect the baculum?

Bob

semiotic, re #26, I was referring to programs like ev. Ev claims to simulate the evolution of biological information. Dembski and Marks show that Ev uses additional (active) information to accomplish this task. Schneider claims on his blog (http://www-lmmb.ncifcrf.gov/~toms/paper/ev/blog-ev.html) that Dembski and Marks misunderstand his algorithm. Meester agrees with Dembski and Marks, but only because ev is a working a toy problem. So the questions are:

1. Is ev a valid simulation of the evolution of biological information?

2. Do Dembski and Marks successfully refute the claims of this algorithm?

If the answers to the above questions are yes, then materialists need to try again.

Behe uses Lenski’s research as the type of research that supports ID. I bet Lenski does not like that endorsement.

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?

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.

[...] 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 [...]

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’. )

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.

36 Davescot

Thank you for expressing my thinking a lot better than I could have myself

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?

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.

Is this ’simulation-ese’ for Natural Selection?

“bounded arena” AND evolution

PaV,

Do you have a link to the Haggstrom paper? (I’d like to see the footnote you refer to.)

Thanks

Atom, I got the link from one of DLH’s post #4.
See:
Olle Häggström: Some recent papers

(PaV - I Added the link DLH.)

Off Topic:

Maybe a new listing could be done for this:

http://www.futurepundit.com/archives/004492.html

#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?

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?

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.

…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.

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.”

Another MMA fan?

Yes, I got hooked

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.)

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.

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.

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.

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.

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,

IOW, models that fail to replicate the real world ought not be taken seriously

PAV

Your post has been displayed four or five times but, you know, repetita iuvant and this is certainly true when right ideas are the opposite NDE theory would like …

Your hint is certainly true and this does invalidate the Hagg. argument but I have argued that his starting point (7) is not valid.

Happy new year to anybody

DaveScot says,

It’s not a model or simulation unless there’s something in the real world to compare it against.

Are you aware that layouts of manufacturing facilities are commonly chosen on the basis of simulation results? That is, the only layout that is ever realized is one that did not exist when it was simulated.

Also, modeled entities need not be as concrete as you seem to think. Flight is an abstract physical phenomenon, and there are several models of flight. When Michelangelo came up with the notion of a helicopter, he had implicitly formed a model of flight that corresponded to no particular physical object. He put the model to empirical test and validated it. He established that the “principles of flight” were not to be obtained merely by looking a birds.

Evolution is also an abstraction. Avida, for instance, is intended to put “principles of evolution” to test. You may criticize the test, but that is the intent.

Look up the words “model” and “simulation” for Pete’s sake.

Done. Now you take a look at:
model OR modeling “first principles” (758,000 hits)

“chaotic system” OR “dynamical system” sensitivity “initial conditions” (131,000 hits)
“chaotic system” OR “dynamical system” qualitative (69,900 hits)

It is reasonable to use first-principles models to predict the behavior of physical entities that have yet to exist. Any model of a chaotic system will, over the long term, diverge exponentially from the modeled system. But a model may be valuable in capturing qualitative features of the modeled entity. I am not making this stuff up.

Are you saying they don’t reflect real world results?

No, Dave said that we had to know either initial or present conditions to model, and I gave some examples of important simulations in which it is impossible to know those conditions with precision or certainty.

Simulation models can take on many forms, and simulation results can be used in many ways — some valid, and some invalid. If I had only my experience in simulation modeling of crops, manufacturing facilities, and human lifting motions to draw upon, my notion of modeling would be relatively limited. There’s no substitute for reading (many) technical papers.

My favorite quotes from the Meester paper:

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.

and

I do not think it is reasonable to summarise the extremely complex biology (and chemistry, physiscs . . .) that is associated to the process, into a single search algorithm. There are no realistic models of evolution that render this approach reasonable, life is simply too complicated. Computing probabilities in a model is one thing, but for these computations to have any implication, the models had better be very good and accurate, and it is obvious that the various models do not live up to this requirement.

The second is really an indictment of Darwinian theory: No one really knows what’s happening in the evolution of life on Earth, Darwinist bluster notwithstanding.
__________

“What I cannot create, I do not understand.”
– Richard Feynman (1988)
__________

Semiotic 007: “By the way, fitness functions are not essential to evolutionary theory. Competition in a bounded arena is.”

In explaining what he meant by the term “natural selection”, Darwin wrote:

I mean by Nature, only the aggregate action and product of many laws, and by laws the sequence of events as ascertained by us.

Thus, it’s all just supposed to be the outworking of the laws of nature (including stochastic processes).

Competition is defined by Merriam-Webster’s as “active demand by two or more organisms or kinds of organisms for some environmental resource in short supply.” So, technically, there is no competition in Darwinian theory. Competition (”active demand”) implies goal-directedness, but in Darwinian theory, there is no goal. Darwin’s use of the term in the Origin can only be considered metaphorical (just as his use of the term “natural selection” was).

What would be needed to validate Darwinian theory is to abstract from “the sequence of events as ascertained by us” the “many [nonteleological] laws” that yield “endless forms most beautiful and most wonderful.” It’s approaching 150 years since Mr. Darwin wrote his book, and no one has done this yet. Why not? Throwing one’s hand up in the air and say, “It’s all just too complicated,” is to concede defeat. And to make a model with a built-in purpose-driven competition of some sort, and proclaim that this is a model of Darwinian evolution, is bogus. If the only way to get evolution to happen is with purpose-driven rules, then this implies something about evolution in nature.

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.

Thus there were two arguments against the applicability of NFL results to real-world optimization…

Oops — meant to say “biological evolution,” not “real-world optimization.”

No one really knows what’s happening in the evolution of life on Earth, Darwinist bluster notwithstanding.
__________

“What I cannot create, I do not understand.”
– Richard Feynman (1988)

No one knows how to build a bird, but we understand how to fly, and we have learned principles of flight that hold for birds as well as helicopters.

Competition is defined by Merriam-Webster’s… So, technically, there is no competition in Darwinian theory.

You may treat OOS like Darwinist scripture, cite chapter and verse, and then assert that any Darwinist believes as Darwin did in 1859 — fine with me. Darwin ascribed to Lamarckian evolution, you know. The last devout Darwinist died a very long time ago.

Don’t you think it is just a tad silly to pull out a dictionary and parse a 150-year-old scientific text to determine what evolutionary theory really is? That’s the stuff of Biblical exegetics, not life science.

That’s the basic problem. Evolutionary theory is undefined and can’t be tested.

DLH says,

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.

I’ve corresponded with some regents and the provost of Baylor University in support of academic freedom.

If you review my comments on the paper, you’ll see that I’ve said nothing in the “pro-ID / anti-ID” dimension. My efforts to prove mathematical results on search and to help