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

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.dgw_{December 31, 2007
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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? :-) BobBob O'H_{December 31, 2007
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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!!DonaldM_{December 31, 2007
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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.Semiotic 007_{December 30, 2007
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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. BobBob O'H_{December 30, 2007
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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.Semiotic 007_{December 30, 2007
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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.PaV_{December 30, 2007
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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_{December 30, 2007
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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.dgw_{December 30, 2007
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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.Semiotic 007_{December 30, 2007
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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.PaV_{December 30, 2007
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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.magnan_{December 30, 2007
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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.magnan_{December 30, 2007
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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.tribune7_{December 30, 2007
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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.Semiotic 007_{December 30, 2007
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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.Semiotic 007_{December 30, 2007
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Gloppy, that was deep -- and incredibly meaningful :-) Thanks a milion!CN_{December 30, 2007
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From Meester’s own stated position, he cannot possibly claim Dr Dembski is wrong. He can only claim that Dr Dembski may not necessarily be right.
That's an other than not unmeaningless statement. GloppyGalapagos Finch_{December 30, 2007
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Bob O'H at 3. No need to worry. It seems that what Meester says is that we don't know enough to be able to model the probability of varions DNA bases or Amino Acids combining in various ways. To calculate probabilities, we need to know if there are intrinsic factors or laws that make it more likely that certain combinations will occur. From Meester's own stated position, he cannot possibly claim Dr Dembski is wrong. He can only claim that Dr Dembski may not necessarily be right.idnet.com.au_{December 30, 2007
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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. ;-)markzwart_{December 30, 2007
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Thanks Dr. Dembski. I'll be looking for it when it comes back up. AtomAtom_{December 30, 2007
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We can't model it, we can't calculate it, but we know it happened. Because we're theoretical scientists!Matteo_{December 30, 2007
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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".tribune7_{December 30, 2007
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Atom: Unfortunately, the EvoInfo.org publications page is down right now. I've asked Robert Marks to look into that. We have a recently revised response to Haggstrom. Be looking for it there.William Dembski_{December 30, 2007
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“This does not imply that I defend ID in any way; I would like to emphasise this from the outset.” He should write that on a little wallet card and memorize it in the elevator. I expect he will need to keep saying it over and over.O'Leary_{December 30, 2007
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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 bladdertribune7_{December 30, 2007
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Good read. Dr. Dembski, is there an online work where you answer the critique of Haagstrom directly?Atom_{December 30, 2007
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Meester cites the 2007 paper by O. Häggström. For links see: Olle Häggström: Some recent papers See particularly the section: ----------------------- "My debunking of some dishonest use of mathematics in the intelligent design movement: Another look is taken at the model assumptions involved in William Dembski's (2002) use of the NFL theorems from optimization theory to disprove the Darwinian theory of evolution by natural selection, and his argument is shown to lack any relevance whatsoever to evolutionary biology. I have two versions of this paper: * O. Häggström: Intelligent Design and the NFL Theorems: Debunking Dembski. This is the original manuscript, of September 2005. * O. Häggström: Intelligent Design and the NFL Theorems. This is a revised version of March 2006 (with a minor additional revision in June 2006) which has now appeared in Biology and Philosophy 22 (2007), pp 217-230. The most striking feature of this version compared to the original one is the removal of all rhetorics, and a more narrow focus on the mathematics. Shortly upon publication of the latter version, a manuscript entitled "Active information in evolutionary search" by William Dembski and Robert Marks (available via Dembski's homepage) appeared on the web, with a response to my argument. This triggered me to elaborate my point a bit further: * O. Häggström: Uniform distribution is a model assumption. " ----------------------- Dembski & Marks' paper should also be available via the Evolutionary Informatics Lab (apparently being edited.) I recommend keeping discussion of Meester under this blog, and begin a new blog to discuss Häggström's three papers. "As iron sharpens iron, so one man sharpens another" Proverbs 27:17 Let the games continue.DLH_{December 30, 2007
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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. BobBob O'H_{December 30, 2007
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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.Joseph_{December 30, 2007
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