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FEA, PR, E, Ro, EOS (Or, Why Darwinian Computer Simulations are Less than Worthless)

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FEA = finite element analysis
PR = Poisson’s Ratio
E = Young’s modulus
Ro = mass density
EOS = equation of state

Darwinian computer simulationists have no idea what I’m talking about, but they should.

A thorough understanding of FEA, PR, E, Ro, and EOS is a prerequisite for any computer-simulationist who hopes to have any confidence that his computer simulation will have any validity concerning the real world (and this just concerns transient, dynamic, nonlinear mechanical systems — nothing that even approaches, by countless orders of magnitude, the complexity, sophistication, and functional integration of biological systems).

Even with all of my understanding and years of experience, I would never expect anyone to accept the results of one of my FEA computer simulations without empirical verification. However, with a consistent track record of validated simulations within a highly prescribed domain (which I have) I can at least save much wasted effort pursuing what the simulations suggest will not work.

It is for this reason, and many others, that I consider Darwinism to be not just pseudoscience, but perhaps the quintessential example of junk science since the advent of the scientific method and rational inquiry concerning how things really work in the real world.

Darwinists have no idea what rigorous standards are required in the rest of the legitimate engineering and science world, and how they have been given an illegitimate pass concerning empirical or even rational justification of their claims.

Comments
What I'm not seeing is your argument! Leaving aside Young's modulus, which does seem to be a red herring, you say:
What I’m objecting to is the fact that a program like Avida can be approved for publication in a prestigious international science journal with the claim that it validates the creative power of the Darwinian mechanism (random errors filtered by natural selection) in the history of biological evolution — when such a claim has no empirical or even rational justification.
First of all, what AVIDA shows (or what that paper showed) was two important things: Firstly (already known) that Darwinian mechanisms can create novel solutions to problems (in this case, algorithms that perform logic functions). Secondly, the authors showed that that even when functions were Irreducibly Complex, they still evolved AVIDA is a model of Darwinian evolution (theoretical) that empirically (see the results) demonstrates the creative power of the Darwinian algorithm, and the fallacy of the Irreducible Complex counter-argument to it. It doesn't, obviously, show that life evolved by Darwinian mechanisms, but it does show that the IC argument that it can't have done is fallacious.Elizabeth Liddle
January 19, 2012
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This is the closest I've seen on UD to "neener neener" or I'm like rubber you're like glue. I'm still wondering what the connection between tensile strength and elastic and viscous moduli and Darwinian evolution is. Please, demonstrate how to treat Young’s modulus in the change of allele frequencies in a population.DrREC
January 18, 2012
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But the universal creative power of the Darwinian mechanism in biology has neither empirical nor rational justification, Liz. I’m not sure what you are not seeing here.GilDodgen
January 18, 2012
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Guys, Variable solution space and infinite solution space are two different things, which is especially important in this thread. As to sloppy thinking, I read what is written. If it is not clearly written, it is not my fault.Eugene S
January 18, 2012
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Good thinking, Champignon. Thanks. Does that make more sense now, kf and Eugene?Elizabeth Liddle
January 18, 2012
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Hi Elizabeth, I think I see the source of Eugene's and KF's confusion. When SCheesman mentioned a finite solution space, you commented:
Not entirely sure about “finite”. In biology, and in some EAs, the evolving population becomes part of the environment, so solution-space itself is constantly changing (because so is the problem space).
Your point was that the solution space could grow without bound as the population evolved, but KF and Eugene seem to think that you were claiming that the search could completely cover an infinite solution space, which would of course require infinite resources. Sloppy thinking on their part, since that would amount to an exhaustive search, when the whole point of using an EA is to avoid an exhaustive search.champignon
January 18, 2012
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But it does have both empirical and rational justification, Gil. I'm not sure what you are not seeing here.Elizabeth Liddle
January 18, 2012
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That is thermally.Eugene S
January 18, 2012
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I did not "conflate dynamic with finite". I made a fairly simple and tangential point. I did not "appeal to infinite resources" as kf alleges. I have no idea how you and kf are interpreting my posts. We seem to be reading different languages. I'm at a loss.Elizabeth Liddle
January 18, 2012
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In my opinion, an engineer must be aware of lots of things. And by the way, when modelling a real optical system, I do believe one has to have an understanding of mechanical properties of lenses. Many mechanical systems must also be therally insulated, therefore color considerations may also be relevant.Eugene S
January 18, 2012
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It is shocking, Dr Liddle. You conflated dynamic with finite, which I pointed to. I think I understood the context well enough. How else should I or anyone else interpret your posts 8.2 and 11.1 which go one after the other?Eugene S
January 18, 2012
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Gil, I think your post was quite clear, and also quite clearly wrong, which is why commenters have lodged so many objections against its content. Perhaps you could address those, including the one I raised in the very first comment on this thread:
Gil,
A thorough understanding of FEA, PR, E, Ro, and EOS is a prerequisite for any computer-simulationist who hopes to have any confidence that his computer simulation will have any validity concerning the real world…
Seriously? You think that someone modeling an optical system needs to worry about Young’s modulus? Do you take color into account when you’re simulating a mechanical system? Of course not.
champignon
January 17, 2012
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It appears that the theme of my post has been misinterpreted by some. This is perhaps my fault for not being sufficiently explicit. It is not my assertion that genetic algorithms are worthless. In fact, they can be a very useful computational tool for finding approximate solutions to difficult problems that defy brute-force search (e.g., the traveling salesman problem). What I'm objecting to is the fact that a program like Avida can be approved for publication in a prestigious international science journal with the claim that it validates the creative power of the Darwinian mechanism (random errors filtered by natural selection) in the history of biological evolution -- when such a claim has no empirical or even rational justification. There is no way such an unjustified extrapolation would be accepted or even taken seriously in the computer-simulation world in which I operate.GilDodgen
January 17, 2012
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I doubt very much that NS or RV or their combination of any sort are capable of driving anything anywhere.
Sorry to butt in, but against the intuition underlying this doubt I would offer the mechanism underlying both NS and drift. The essence is sampling. Each generation loses some of the variation in the prior population. This must be so because organisms produce variable numbers of offspring. Overrepresentation for some means underrepresentation for others. It is readily demonstrable mathematically. Successive generations iteratively lose more and more variation. The inevitable result of this is loss of ALL variation. So in that sense, NS and drift drive populations towards fixed points, at which points evolution would stop. But what acts against this tendency is the input of new variants through mutation. Mutations get fixed by exactly the same process as described. Any neutral sequence has probability 1/N (total population size) of being the sole sequence at its locus following fixation. Each fixation removes all trace of the ancestral allele. The picture is more complicated for a selective differential, and the existence of thousands of loci, but the underlying process is the same. With this mutation-fixation process in operation, at the background of all generations, populations will wander away from any point in the space, even if selection is acting on some loci to keep them at their current location. Selection acts differently at each locus, and is conditioned by what is going on at every other locus, as well as environmentally. If selection shifts or diminishes, an anchored locus will shift. Having wandered, a sequence is probabilistically almost certain not to return to that same place. The searchability of the space is a different matter, conditioned by the rules by which 'well-formed' strings are evaluated, but the role of the population sampling process is highly significant, and the result - a play-off between addition of and loss of variation - generates a memoryless random walk, effectively 'driving' the population everywhere but its current location.Chas D
January 17, 2012
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Hello vjtorley Yes, I noticed your post covered some common ground. Lots of interesting facts and points.SCheesman
January 17, 2012
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Interesting. If you cannot simulate biology, how do you design? Can you point to any technology more advanced than pottery making that does not involve simulations and model building in the design process?Petrushka
January 17, 2012
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Ah. Thanks. But that was not "an appeal to infinite resources" kf. Did you not read the context? It was merely a comment that in a search algorithm, the solution space may not be static. Nothing "shocking" there, Eugene.Elizabeth Liddle
January 17, 2012
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There we go. #8.2 by Dr Liddle. I responded to it earlier on in #8.2.4. Anyone can compare posts #8.2 and #11.1. Shocking.Eugene S
January 17, 2012
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Strictly speaking, O(N^2) was an upper bound estimate proposed by Chaitin. If it had been the lower bound, then yes, we would have waved good-bye to Darwinian evolution under the time constraints of 5 bln years. However, Chaitin acknowledges that O(N^2) is rather a lot. I don't personally think him a Darwinian mathematician. In what I saw him write he just had nothing against it. On the other hand, I also saw pointers to materialist criticisms of some of his (for want of a better word) not-terribly-materialistic conclusions from his own algorithmic complexity theory. Anyway, it is my personal impression. But whatever his position, I hold him in high esteem as a true scientist.Eugene S
January 17, 2012
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Hi Elizabeth, SCheesman and Petrushka, You might like to have a look at my latest post, at https://uncommondescent.com/intelligent-design/game-on-a-bioinformatician-confronts-intelligent-design/ as it overlaps in content with some of the points you've been discussing. Petrushka, you maintain that natural selection is more powerful than directed evolution, and you compare this insight to Adam Smith's point about the robustness of market economies vs command economies. I'm dubious of the analogy between choices made by agents and supposedly random events (mutations in neo-Darwinian evolution). You might like to have a look at this earlier post of mine: https://uncommondescent.com/intelligent-design/at-last-a-darwinist-mathematician-tells-the-truth-about-evolution/ In this post, Darwinian mathematician Gregory Chaitin acknowledges that as far as we know, Intelligent Design is much faster than Darwinian evolution (order N vs. order N squared), although the latter is far, far more rapid than exhaustive search (order 2 to the N). Of these three kinds of evolution, Intelligent Design is the only one guaranteed to get the job done on time. I also see no reason why the direction of Intelligent Design would need to be one-dimensional. Indeed, I would expect it to be multi-dimensional.vjtorley
January 17, 2012
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Should of course read "the set of parameter combinations".Eugene S
January 17, 2012
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Petrushka, The question is what drives search towards increased utility. Nature does not care about utility, does it? I think evolutionist models suffer from making evolution anthropomorphic. Without agency nothing will choose anything. Algorithms are formal. Physical reality is not. I doubt very much that NS or RV or their combination of any sort are capable of driving anything anywhere. Function, because it is a formal concept, testifies to choice contingency. There is a conceptual chasm between spontaneous redundant low-info regularity at the physical level and systems exhibiting control, formalism and meaning.Eugene S
January 17, 2012
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OK please reference ONE Darwinist simulation that correctly simulates biological evolution. That is what Gil is referring to- oh wait you think they are bogus also, just as Gil does. Strange...Joe
January 17, 2012
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Finite and dynamic are two different concepts. The number of parameter combinations is a Cartesian product of n relevant sets of parameters. E.g. for X={x1,x2} and Y={y1,y2}, the Cartesian product X o Y = {(x1,y1), (x2,y1), (x1,y2), (x2,y2)}. Consequently, on finite domains, it is finite.Eugene S
January 17, 2012
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Thank-you, Elizabeth. Once you can split an issue into parts, it’s much easier to talk about it by focussing on each in turn. I have ideas, counters, explanations (and often agreements) with many of your points, but this thread really isn’t the place for it. In fact each of those points, I’m sure, could be split into several threads much more productively. My main hope was to get over the “that’s not even relevent” objection!SCheesman
January 17, 2012
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OK, how about the light bulb? Minimum components: enclosure, vaccuum pump, filament, electrical supply. Add to that configuration information and construction order (not necessarily the same things). This is not necessarily an "irreducibly complex" recipe, but removing, say the vacuum requirement shortens the life of the filament so much, short of adding additional control systems on the power supply to prevent this by dropping the voltage, it's questionable that you have a functional bulb. Note that configuration and construction are as important as the parts list. Given all the parts, a light bulb does not assemble itself.SCheesman
January 17, 2012
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For record: Onlookers, kindly search for the phrase and context of: "Not entirely sure about “finite” . . . " above. KFkairosfocus
January 16, 2012
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GAs are not necessarily simulations, kf. They are used to produce actual products. And yes, they often do start from "arbitrary initial configs". For instance in the learning algorithms I write, I usually start off with the parameters set to give a result that is no better than guesswork, and it learns from there. When used to "simulate" something, the test of the model is a test of the output of the model against actual data. If the model output is a good match for the output of the process being modeled, we can conclude that the model is a good one.Elizabeth Liddle
January 16, 2012
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GV: By definition, a sim is not reality, though its ops may mimic it and give rise to correct outputs. Op amps are wonderful! As prof N, long ago taught us, for implication-bases systems, ex falso quodlibet. False premises do not guarantee true conclusions. Models have to be validated and cross checked against experienced reality. In effect, they are useful theories that may be empirically reliable in a domain or case, but do not fool yourself that they are reality. Reality is real, that is why we need direct empirical test if we can get it. Which is exactly where the key breakdowns are. GAs etc may model what happens within islands of function, as discussed above, but they don't put us there from arbitrary initial configs, without a lot of intelligent input. GEM of TKIkairosfocus
January 16, 2012
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Who appealed to infinite resources?Elizabeth Liddle
January 16, 2012
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