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Gil Has Never Grasped the Nature of a Simulation Model

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Tom English challenged me with this:

I say categorically, as someone who has worked in evolutionary computation for 15 years, that Gil does not understand what he is talking about. This is not to say that he is trying to mislead anyone. It is simply clear that he has never grasped the nature of a simulation model. His comments reflect the sort of concrete thinking I have tried to help many students grow beyond, often without success.

The reason for Tom’s lack of success is that he, and Darwinists in general, try to explain everything with an overly — indeed catastrophically — simplistic model. Here’s what’s involved in a real-world computer simulation:

My mathematical, computational, and engineering specialty is guided-airdrop technology. The results of my computer simulations, and their integration into the mechanics of smart parachutes, are now being used to resupply U.S. forces in Afghanistan. C-130 and C-17 aircraft can now drop payloads from up to 25,000 feet MSL, out of range of enemy small-arms, shoulder-launched missile, and RPG fire, and the payloads autonomously guide themselves to their targets within a CEP (circular error probable) of approximately 26 meters. Did I do all of this highly sophisticated mathematical and software simulation without ever having “grasped the nature of a simulation model”?

One small part of developing this technology involves mathematically and computationally simulating the descent rate of a parachute and its payload at various altitudes. This includes the following: the drag coefficient of the parachute, the chute reference area, the density of the air at various altitudes (not only determined by altitude but lapse rate — the rate at which air temperature changes with altitude), and other subtle considerations, such as the flow-field effects of the payload which changes the drag characteristics of the parachute.

If any mathematical, computational, or real-world assumptions about any of these factors are wrong, or if any unforeseen factors are left out (and what I described above represents a small percentage of what’s involved), the simulation breaks down. We do our best, but we never know for sure until we throw the thing out of an airplane, see where it lands, and tediously analyze the telemetry data recorded by the in-flight computer.

Based on these observations and computer simulations that can be tested in the real world, what confidence can anyone have that biological evolutionary computer simulations have anything to do with reality?

The answer is: none. It’s all fantasy and speculation, masquerading as science.

Comments
Karl responds: Yes. He has developed analytic models, not simulation models
Isn't there a big difference between simulation and analysis? This is what I find misleading - analysis takes a look at an existing system and returns some dataset relevant to that which is examined while a simulation is the system itself. For you to say Dembski's analysis is flawed by Gil's critique of evo-sims seems to be an apples an oranges type of fallacy. Indeed, his isn't a 'model' in the same sense as evo-sims are models, so when you say:
Even the most abstract of evolutionary and artificial-life models is more biologically realistic than Bill Dembski’s analytic models
you aren't even talking about the same thing. Dembski's 'model' applies to large 'spaces' of data returning specific information (or so I simply gather, correct me if I'm wrong). His is abstract in that any given set of data can be analyzed, so when you say Dembski " says nothing about protein folding and genes and chromosomes and predators and plagues and earthquakes and meteors. He models information flow that has never been observed directly. There are no data for his models to fit, and he gives no guidance as to how to validate the models", it appears you conflate the two types of models and, I hate to say it, obfuscate the issue at hand. Why should he say anything about protein folding? You want to add folding to the search space? I'm sure it can be done, but I doubt it will improve the statistical return in favor of random mutation and natural selection. I'm curious, do any evo-sims model anything that has been observed directly?todd
October 2, 2006
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PaV on Tom E.: Tom English: "I have long been frustrated with certain parties’ inability to recognize that neo-Darwinian evolution is an abstract process, not necessarily implemented in biota." PaV: "What in the world is this supposed to mean?" Read Dennett's discussion of algorithmic processes in "Darwin's Dangerous Idea" (p. 50). He argues that, by the same token that the logic of long division can be hosted upon virtually any physical substrate ("The power of the procedure is due to its logical structure, not the causal powers of the materials used in the instantiation, just so long as those casual powers permit the prescribed steps to be followed exactly"), selectionist causation (natural selection in biology) is itself an abstract algorithmic process that is portable in the same sense. The power of the selectionist algorithm may be demonstrated in a variety of substrates. This is why modeling of NS by means of computation is so powerful and relevant. And this is what Tom meant. (BTW, in revisiting Dennett's book for the first time in about ten years, I was amused to see that he chose the words "substrate" and "instantiate" to express this notion. But perhaps I was influenced by my prior reading.)Reciprocating Bill
October 2, 2006
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Oh no - there goes Tow-kee-oh...todd
October 2, 2006
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Tom English writes: "There are people working in computational chemistry (e.g., simulation of protein folding), but proteins are no more the right level of granularity for evolutionary simulation than are transistors the right level for microprocessor simulation. If you truly know anything about microprocessor simulation, you know what I am saying." Your ignorance is showing again. http://www.ac.uma.es/hpca10/tutorials.html
Tutorial 1: Advanced Processor Architectures and Verification Challenges Presenter: Sunil Kakkar, IBM Global Services, Bangalore, India Abstract: Due to the complexity associated with design verification, testing costs for high performance processors are spinning out of control. The verification effort grows exponentially as design complexity grows linearly. It is commonplace for a processor design to contain over 100 million gates today. Considering that a one million gate design requires 5-8 verification engineers, the task of verification is dominating project costs. Server farms of thousands of machines are needed to run design verification tests around the clock. Five years ago design verification consumed 50% of the total effort expended on a chip design. Today this percentage has grown to over 80% and is likely to further dominate costs in the near future. All this opens up new frontiers of challenges in verifying the complex processor architectures of today with all the practical constraints that are placed on the verification team. This tutorial will describe the current state-of-the-art in design verification and suggest some directions that could lead to reducing this burden. About the presenter: Sunil Kakkar has over 20 years of experience in Processor Design, Architecture, Performance and Functional Verification and has successfully led large design verification and performance analysis teams that have ended up handing off fully functional first silicon to the manufacturing and the test teams. Holding a Bachelor's degree from IIT-Kanpur and two Master's degrees from the University Of Illinois, Sunil holds a patent for a specialized microprocessor verification flow technique that he invented at Sony and which led to bug free first silicon. Sunil has also taught at the University of Berkeley program for industry professionals. Sunil has also invented a VDL (Verification Design Language) which when used to specify a digital design at a higher level of abstraction can be used to generate testbenches automatically in any HDL or high level language. Sunil was invited to chair the IEEE Computer Society Conference session on Verification. Sunil has worked for companies like Hewlett Packard and Transmeta and is currently managing the Processor Architecture, Performance and Verification Groups at IBM Global Services in Bangalore. He is the chief technologist for the IBM's e-verification technology initiative in the Asia-Pacific region.
DaveScot
October 2, 2006
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Karl Pfluger I am widely known for making what you call "sweeping statements." You bet I am. They are the only kind that really matter. And what are you known for? Come on, don't be shy. Lead me to your publications, especially the ones that deal with organic evolution. I'll bet you don't have any. A near as I can determine you are just one more garden variety Darwimp. The internet is crawling with them, thousands of them, all mumbling the same mindless drivel with what Grasse called "Olympian authority." It makes me sick to my stomach. "Never in the history of mankind have so many owed so little to so many." after Winston Churchill It is hard to believe isn't it? I love it so! "A past evolution is undeniable, a present evolution undemonstrable."John A. Davison
October 2, 2006
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Tom English write "It does not matter one whit whether the simulations fit biological observations." A damn good thing too because if you tried, they wouldn't.DaveScot
October 2, 2006
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PaV writes Call it what it is: “trial and error.” Because that wouldn't sound very impressive to the casual observer. In point of fact children discover the so-called genetic algorithm quite naturally with no tutelage. You have a problem to solve, you try out a possible solution, if it works perfectly you stop there, if works a little bit you build on it, if it doesn't work at all you try something completely different. There is absolutely nothing new about this problem solving method. It's as old as dirt. The only thing new is using computers to speed up the process but these current artifical life programmers were far from the first ones to use a computer to speed up the process. What they've done is pasted a new name on an old method so they can claim they are the inventors. It's an old story. "Expert systems" (a 1980's fad) are the classic example of putting a new name on an old method in order to claim ownership.DaveScot
October 2, 2006
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Tom English wrote:
I have long been frustrated with certain parties’ inability to recognize that neo-Darwinian evolution is an abstract process, not necessarily implemented in biota.
PaV asks:
What in the world is this supposed to mean?
He means that any scenario in which there is replication, heritable variation, and selection constitutes an example of Darwinian evolution, whether or not it involves living creatures. PaV scoffs at Tom's sunspot models:
Gee, let me see: you use a random (chaotic) model, and lo and behold, you’re able to simulate–what is it again? Oh, yeah, a “chaotic process”. Wonderful. Congratulations.
PaV, stop and think for a minute. Are all chaotic processes identical? If you're able to model the dripping from a faucet, does that mean you're automatically able to model an n-body planetary system? Of course not. To model a particular chaotic process, it is not enough to simply create another chaotic process. You have to come up with a chaotic process whose behavior matches the behavior of the system you're trying to model. The fact that Tom's model was better than any existing model in predicting sunspot activity is a significant achievement. Perhaps you don't understand that Tom is using the word "chaos" in its technical sense. If that's the problem, see the following link: http://www.imho.com/grae/chaos/chaos.html
As to Gil’s proposal, don’t computers, in fact, break down?
Yes, but when a computer breaks down, any simulations running on it will cease to produce meaningful results.
We use our intelligence to solve “hardware” problems; what do cells use?
To the extent they are able, they use their inherited repair mechanisms to undo the damage. To the extent they are unable, they die or cease to function properly.
So why would you consider Dembski’s argument, that is along the lines of an analytical solution, inferior to evolutionary modeling? I don’t get this.
Read Tom's statement, paying careful attention to the word 'if':
And if simulation models suffer the defects you say they do, then Bill Dembski’s abstract models of evolution in Searching Large Spaces and The Conservation of Information are in even bigger trouble.
BC wrote:
[T]he “Folding at Home” project has massive computing power being put into this, and they’re nowhere near the needed computational power.
PaV asks:
And what does this say about the likelihood of random mutation being able to “find” the right solution to protein folding?
Nothing. They are entirely different problems. The Folding @ Home project is trying to explain why proteins fold as they do. Natural selection is merely "trying" to find proteins which enhance fitness. Natural selection has no knowledge of protein folding and makes no attempt to explain it. I wrote:
3. They are not the same system. In an airdrop simulation, one of them (#2) is simulating the other (#1). 4. If you wanted to simulate the effects of hardware or software errors on an airdrop, you would introduce the errors into the model of #1.
PaV asks:
This is exactly what Gil is proposing. don’t you see it?
Not at all. This is Gil's categorical statement:
All computational evolutionary algorithms artificially isolate the effects of random mutation on the underlying machinery: the CPU instruction set, operating system, and algorithmic processes responsible for the replication process. If the blind-watchmaker thesis is correct for biological evolution, all of these artificial constraints must be eliminated. Every aspect of the simulation, both hardware and software, must be subject to random errors.
He is talking about mutating the CPU and the OS, not the model.Karl Pfluger
October 2, 2006
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Tom English writes (re sunspots) "My question for you is how I did that. " With a computer is how you did it. Computers can generate and sort possible solutions to problems at blazingly high speeds. You chose a search method that happened to work well on the data set in question. It's not mysterious. Computers are fast.DaveScot
October 2, 2006
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Tom English writes "I have long been frustrated with certain parties’ inability to recognize that neo-Darwinian evolution is an abstract process, not necessarily implemented in biota." It's not abstract in the case of biota, Tom. You get that much don't you? If a process exists ONLY in the abstract then models of it are more appropriately called fantasies. What else can they be if they have no basis in reality? If the process exists in reality then a model of it can be compared to reality (tested) to see how robust the model is. Avida models a fantasy world. The laws of nature in it are made up out of whole cloth, the creatures that live in it are composed of artificial CPU instructions. Write the down.DaveScot
October 2, 2006
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Tom English writes "Even the most abstract of evolutionary and artificial-life models is more biologically realistic than Bill Dembski’s analytic models." You're joking, right? A digital organism composed of virtual microprocessor instructions, that reproduces by instantaneous magical means, driven to success or failure by laws of fantasy decreed by a programmer/god... ascribing any degree of biologically realistic to that has got to be a joke. If you're serious then you're living in denial because I know you're normally smart enough to not get sucked into such a foolish position. DaveScot
October 2, 2006
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todd wrote:
Avida proved IC can arise without intelligent cause?
I said, "The significance of Avida, in particular, is as an example of how a Darwinian mechanism can produce irreducible complexity."
So then, what caused Avida?
Programmers.
It seems an impossible task to create a program, especially one which purports to ‘prove’ stochastic evolution, and keep the marks of intelligence out of the picture.
Avida doesn't "purport to 'prove' stochastic evolution", and of course Avida was created via intelligence. How is that relevant to whether the process modeled by Avida requires intelligent input? You seem to be making Gil's mistake. The process doing the modeling is not the same as the process being modeled. The fact that the former requires intelligent input in order to operate does not mean that the latter does.
Here you have creators (programmers) defining the ground rules of the Avida universe, which you say produces IC, refuting IC as a hallmark of intelligent cause - yet… you have no universe to produce the IC without intelligent agents defining the terms!
If you're arguing that the universe itself must have an intelligent cause, fine -- that's a legitimate question. But that's emphatically not what leading ID proponents are saying. They claim that IC systems cannot evolve via Darwinian mechanisms, regardless of how the universe itself came into existence.
Is there some way to resolve this paradox and leave your claim standing? I’m eager to hear it!
There was no paradox to begin with.Karl Pfluger
October 2, 2006
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John Davison wrote:
Nylonase is an enzyme, not an organism.
Some organisms produce nylonase, some do not. It takes a mutation to turn the ones that don't into ones that do. How is this not an example of non-conservative natural selection?
Adaptive enzymes have been known for decades and probably have nothing to do with creative evolution.
Probably? For you to make such a sweeping statement as this...
Natural selection is very real. Today, as in the past, it PREVENTS change. That is all it ever did.
...don't you think you should be a bit more certain of the evidence?
Does that help? Probably not.
Nope. Write that down.Karl Pfluger
October 2, 2006
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Tom English says:
Yes, Bill gives a made-up problem of searching for a particular sequence of amino acids. He does not go at all into the complexities of how amino acids specify proteins. And he certainly does not mention that in reality many permutations of an amino acid sequence may represent the same protein, and that numerous proteins are represented by amino acid sequences of length 100.
You are correct that Bill doesn't completely specify all the complexities of biology in that one article. But he certainly does address some of those issues elsewhere. The larger point remains: Dembski sees these complexities as friendly to his arguments, hence he is unlikely to ignore them, if for no other reasons than self-serving ones. On the other hand, they aren't necessarily the friend of your position. That's why I'm a little skeptical when you try to diss their relevance. Of course that's before you then turn around and make the claim that:
Even the most abstract of evolutionary and artificial-life models is more biologically realistic than Bill Dembski’s analytic models.
Feel free to provide some evidence for this claim. Why not take the Avida program that Karl mentions, and show us how it is more biologically relevant than Dembski's analysis. And while you are at it, let us know what "principle" is being proved, and where the "natural selection" is in Avida. I see Karl decided it was just plain "selection" in his description.Roger
October 2, 2006
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Gil wrote:
In my original post about mutating the CPU instruction set, the OS, etc., I was being somewhat sarcastic. Obviously, this would be silly, and I wouldn’t expect anyone to take such an experiment seriously.
Gil would have us believe that after three days of being criticized for confusing the simulator with the simulated, and after writing a new post which evades the issue altogether but attempts to establish his credentials, that he is just now getting around to mentioning that his first post was "sarcasm" which noone should take seriously. Gil, is it really that painful to admit that you were wrong? Even if it was pointed out by (gasp) Darwinists? Gil continues:
My point was that if mutations are genuinely random, we should expect that in a biological system (e.g., a cell) they would interfere with or modify all aspects of a cell’s basic functioning, which would affect the ability of the cell to survive and reproduce.
Yes, mutations are necessary in an evolutionary simulation. That's why they all implement mutations and penalize the organisms having deleterious mutations.
If random mutations killed off a significantly large percentage of cells or made them sterile before they had a chance to reproduce, pass on their genetic information, and for natural selection to work its magic, the rest of the simulation would be rendered invalid.
Why would that render the simulation invalid? That's exactly what natural selection should do if the mutation rate is high enough. Suppose , in an extreme case, that a supernova went off within a few light years of Earth. The radiation would be expected to kill all terrestrial life. How is that contrary to NDE? Of course, the interesting stuff in evolutionary simulations happens when the mutation rate is quite a bit lower, as it is in real life.Karl Pfluger
October 2, 2006
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todd:
Isn’t what Dembski is doing different than Avida or other Evo-sims are doing?
Yes. He has developed analytic models, not simulation models.
It seems to me you are misstating Gil’s point of contention regarding evo-sims.
Where? How? Even the most abstract of evolutionary and artificial-life models is more biologically realistic than Bill Dembski's analytic models. Gil's contention applies even more to ID theory (by that, I mean the math) than it does simulated evolution.Tom English
October 1, 2006
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Tom English - In your sunspot sim, you may not have known anything about sunspots, but I'd bet you had a dataset of sunspot activity. And when you "evolved" your model, you found one that "fit" the data best. You had a prespecified target, and you gave organisms "partial credit" for getting better at predicting via a "fitness function" evaluator. If this was in fact what you were doing (and every evolutionary algorithm I've seen has done something like this), then the simulation is a directed search a la Dawkins's "Weasel" program, which even he himself admits has nothing to do with true, undirected Darwinian evolution, in which organisms don't get partial credit. Is this in fact what you did, or am I mistaken?jimbo
October 1, 2006
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Tom English:
I have long been frustrated with certain parties’ inability to recognize that neo-Darwinian evolution is an abstract process, not necessarily implemented in biota.
What in the world is this supposed to mean?
In 1995, I ran an evolutionary computation on a massively-parallel computer and obtained more than 20 thousand models predicting annual sunspots counts. The series of sunspots counts is chaotic, and has been a challenging benchmark problem in time series prediction for many years. With a combination of models, I obtained far better prediction accuracy than anyone else ever had (“Stacked Generalization and Simulated Evolution,” BioSystems, 39(1), pp. 3-18, 1996).
Gee, let me see: you use a random (chaotic) model, and lo and behold, you're able to simulate--what is it again? Oh, yeah, a "chaotic process". Wonderful. Congratulations.
How could I have front-loaded the evolutionary computation when no physicist or statistician had ever managed to give a good model of the time series?
As my above comment illustrates, what need did you have to "frontload" anything! As to Gil's proposal, don't computers, in fact, break down? Tom, have you had that experience? I just had to replace a power unit on a PC. Hard drives break down. Things break down. As well, if you want to talk about "substrates" that "instantiate" a simulation program, then computers are just like cells, the "substrate" upon which the DNA "program" is "instantiated": and things can go wrong in them. We use our intelligence to solve "hardware" problems; what do cells use? Isn't that really the point that Gil is trying to draw out? Perhaps Gil would like to comment, but cellular (and hence computer) breakdown has its valid context. But, of course, why worry about whether a "substrate" is going to fall apart when no one, to date, has demonstrated a program that has been "instantiated" on a reliable "substrate" that anything resembles outputting true information.
The point I want you and others to get here is that such successes of evolutionary computation demonstrate the efficacy of abstract neo-Darwinian processes.
Sir Fred Hoyle in "The Mathematics of Evolution" likens the evolutionary model to a simple feedback equation that includes a selection term: you vary the system, and if the variance is positive it goes to 100% probability, and if it is negative, then it goes to 0% probability. Why fawn over such a simplistic mechanism and call it "neo-Darwinian abstraction". Call it what it is: "trial and error."
Ah, Gil, but what you seem not to comprehend — this, I think, is a genuine misunderstanding — is that there is no absolute distinction between an analytical model and a simulation model. And if simulation models suffer the defects you say they do, then Bill Dembski’s abstract models of evolution in Searching Large Spaces and The Conservation of Information are in even bigger trouble.
But there's a big difference between an analytical solution and a solution derived from perturbation theory, and the analytical is always preferred. So why would you consider Dembski's argument, that is along the lines of an analytical solution, inferior to evolutionary modeling? I don't get this.
[T]he “Folding at Home” project has massive computing power being put into this, and they’re nowhere near the needed computational power.
And what does this say about the likelihood of random mutation being able to "find" the right solution to protein folding? Karl Pfluger:
3. They are not the same system. In an airdrop simulation, one of them (#2) is simulating the other (#1). 4. If you wanted to simulate the effects of hardware or software errors on an airdrop, you would introduce the errors into the model of #1.
This is exactly what Gil is proposing. don't you see it?PaV
October 1, 2006
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Dr Davison: I'm glad you recognized it for what it was...and it didn't give you gas! Heh. :)todd
October 1, 2006
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StevenA: Re: #56 Ditto that for me. I'm not a math geek like Dembski nor a uber goober programmer geek like Gil - I don't ask for ID for Dummies, just minimal mumbo jumbo and gobbledy gook!todd
October 1, 2006
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Tom: Isn't what Dembski is doing different than Avida or other Evo-sims are doing? It seems to me you are misstating Gil's point of contention regarding evo-sims. Karl: Avida proved IC can arise without intelligent cause? So then, what caused Avida? It seems an impossible task to create a program, especially one which purports to 'prove' stochastic evolution, and keep the marks of intelligence out of the picture. This is the bill of goods 'your side' is selling - it is counter-intuitive, so I hope you understand how laymen such as myself consider such a claim with great skepticism. Here you have creators (programmers) defining the ground rules of the Avida universe, which you say produces IC, refuting IC as a hallmark of intelligent cause - yet... you have no universe to produce the IC without intelligent agents defining the terms! Is there some way to resolve this paradox and leave your claim standing? I'm eager to hear it!todd
October 1, 2006
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Todd Thanks for the compliment. Iconoclasts seek to overthrow established order. That is the purpose of science now as in the past. Thanks again. I love it so! "A past evolution is undeniable, a present evolution undemonstrable." John A. DavisonJohn A. Davison
October 1, 2006
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P.S. I am just an interested layman who dabbles in programming, so please keep your replies as free of jargon as possible.StephenA
October 1, 2006
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I think I had better clarify what my suggestion was for since BC seems to think I'm against ID. I think that it is possible to simulate the effects of RM & NS in a computer model, and my suggested model was an attempt to do that. However, I predict that the effects of RM & NS if accurately simulated (even if only in a rather abstract sense) will not produce new information. I think most ID supporters agree with me here (if you don't, let me know). What I contend is that the simulated models that are supposed to produce new information from mimicing evolution do not in fact accurately simulate RM & NS. If I understand correctly, some here also contend that no simulation is capable of producing new information. They may be right, but I don't know enough to argue that case, so I leave that area alone. I hope it is clear what I am saying, and what I am not saying.StephenA
October 1, 2006
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Dr Davison, You sir, are an iconoclast of the highest order! Got that? Write it down! :)todd
October 1, 2006
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Karl Pfluger Nylonase is an enzyme, not an organism. Adaptive enzymes have been known for decades and probably have nothing to do with creative evolution. Does that help? Probably not. I am not neglected. I am cynically and deliberately being ignored. So also were my sources and they still are. We are ignored because collectively we have exposed and destroyed the biggest hoax in the history of science. Got that? Write that down. Thanks for posting and exposing yourself. I love it so! "A past evolution is undeniable, a present evolution undemonstrable."John A. Davison
October 1, 2006
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Because a computer simulation purports to show something is possible doesn’t mean it’s possible or anywhere near possible in the real world.
Does the above hold for a mathematical model which says that something is impossible (or as good as) as well?steveh
October 1, 2006
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In my original post about mutating the CPU instruction set, the OS, etc., I was being somewhat sarcastic. Obviously, this would be silly, and I wouldn't expect anyone to take such an experiment seriously. My point was that if mutations are genuinely random, we should expect that in a biological system (e.g., a cell) they would interfere with or modify all aspects of a cell's basic functioning, which would affect the ability of the cell to survive and reproduce. If random mutations killed off a significantly large percentage of cells or made them sterile before they had a chance to reproduce, pass on their genetic information, and for natural selection to work its magic, the rest of the simulation would be rendered invalid. My point was just that simple, but apparently I didn't make it clear. My point was also not that genetic algorithms and evolutionary computing are not useful and powerful tools in a wide variety of problem-solving domains. I should probably also have been more explicit about the bottom line of my contention: Ridiculously exaggerated and unsubstantiated claims have been made for the real-world relevance of computer simulations of biological evolution. For example, Avida was touted in a premier international science journal as having refuted Michael Behe's irreducible-complexity challenge to random mutation and natural selection as a viable mechanism to explain away obvious difficulties. Avida did nothing of the sort, but the claims made on its behalf were soaked up uncritically.GilDodgen
October 1, 2006
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Because a computer simulation purports to show something is possible doesn't mean it's possible or anywhere near possible in the real world. That should be noted first off. Secondly- I'd bet that a lot of people here and others in ID, in general, would disagree that avida shows what you claim. Even if it did show this in a computer simulation, again- it's not the real world. On top of that- I'd say a lot of honest IDers would disagree with you and do so honestly. It doesn't make a person dishonest to discount avida as a fantasy.JasonTheGreek
October 1, 2006
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John Davison wrote:
Natural selection is very real. Today, as in the past, it PREVENTS change. That is all it ever did. Don’t take my word for it. You never do.
Ok, John, since you're feeling neglected, I'll take your bait. How do you explain nylonase, given your belief that natural selection is solely conservative?Karl Pfluger
October 1, 2006
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