Evo-Info 3: Evolution is not search

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Introduction to Evolutionary Informatics, by Robert J. Marks II, the “Charles Darwin of Intelligent Design”; William A. Dembski, the “Isaac Newton of Information Theory”; and Winston Ewert, the “Charles Ingram of Active Information.” World Scientific, 332 pages.
Classification: Engineering mathematics. Engineering analysis. (TA347)
Subjects: Evolutionary computation. Information technology–Mathematics.

Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem. What I hope to convey in this post, primarily by means of graphics, is that fine-tuning a model of evolution, and thereby obtaining an evolutionary process in which a maximally fit individual emerges rapidly, is nothing like informing evolution to search for the best solution to a problem. We consider, specifically, a simulation model presented by Christian apologist David Glass in a paper challenging evolutionary gradualism à la Dawkins. The behavior on exhibit below is qualitatively similar to that of various biological models of evolution.

Animation 1. Parental populations in the first 2000 generations of a run of the Glass model, with parameters (mutation rate .005, population size 500) tuned to speed the first occurrence of maximum fitness (1857 generations, on average), are shown in orange. Offspring are generated in pairs by recombination and mutation of heritable traits of randomly mated parents. The fitness of an individual in the parental population is, loosely, the number of pairs of offspring it is expected to leave. In each generation, the parental population is replaced by surviving offspring. Which of the offspring die is arbitrary. When the model is modified to begin with a maximally fit population, the long-term regime of the resulting process (blue) is the same as for the original process. Rather than seek out maximum fitness, the two evolutionary processes settle into statistical equilibrium.

Figure 1. The two bar charts, orange (Glass model) and blue (modified Glass model), are the mean frequencies of fitnesses in the parental populations of the 998,000 generations following the 2,000 shown in Animation 1. The mean frequency distributions approximate the equilibrium distribution to which the evolutionary processes converge. In both cases, the mean and standard deviation of the fitnesses are 39.5 and 2.84, respectively, and the average frequency of fitness 50 is 0.0034. Maximum fitness occurs in only 1 of 295 generations, on average.

I should explain immediately that an individual organism is characterized by 50 heritable traits. For each trait, there are several variants. Some variants contribute 1 to the average number offspring pairs left by individuals possessing them, and other variants contribute 0. The expected number of offspring pairs, or fitness, for an individual in the parental population is roughly the sum of the 0-1 contributions of its 50 traits. That is, fitness ranges from 0 to 50. It is irrelevant to the model what the traits and their variants actually are. In other words, there is no target type of organism specified independently of the evolutionary process. Note the circularity in saying that evolution searches for heritable traits that contribute to the propensity to leave offspring, whatever those traits might be.

The two evolutionary processes displayed above are identical, apart from their initial populations, and are statistically equivalent over the long term. Thus a general account of what occurs in one of them must apply to both of them. Surely you are not going to tell me that a search for the “target” of maximum fitness, when placed smack dab on the target, rushes away from the target, and subsequently finds it once in a blue moon. Hopefully you will allow that the occurrence of maximum fitness in an evolutionary process is an event of interest to us, not an event that evolution seeks to produce. Again, fitness is not the purpose of evolution, but instead the propensity of a type of organism to leave offspring. So why is it that, when the population is initially full of maximally fit individuals, the population does not stay that way indefinitely? In each generation, the parental population is replaced with surviving offspring, some of which are different in type (heritable traits) from their parents. The variety in offspring is due to recombination and mutation of parental traits. Even as the failure of parents to leave perfect copies of themselves contributes to the decrease of fitness in the blue process, it contributes also to the increase of fitness in the orange process.

Both of the evolutionary processes in Animation 1 settle into statistical equilibrium. That is, the effects of factors like differential reproduction and mutation on the frequencies of fitnesses in the population gradually come into balance. As the number of generations goes to infinity, the average frequencies of fitnesses cease to change (see “Wright, Fisher, and the Weasel,” by Joe Felsenstein). More precisely, the evolutionary processes converge to an equilibrium distribution, shown in Figure 1. This does not mean that the processes enter a state in which the frequencies of fitnesses in the population stay the same from one generation to the next. The equilibrium distribution is the underlying change­less­ness in a ceaselessly changing population. It is what your eyes would make of the flicker if I were to increase the frame rate of the animation, and show you a million generations in a minute.

Animation 2. As the mutation rate increases, the equilibrium distribution shifts from right to left, which is to say that the long-term mean fitness of the parental population decreases. The variance of the fitnesses (spread of the equilibrium distribution) increases until the mean reaches an intermediate value, and then decreases. Note that the fine-tuned mutation rate .005 ≈ 10–2.3 in Figure 1.

Let’s forget about the blue process now, and consider how the orange (randomly initialized) process settles into statistical equilibrium, moving from left to right in Animation 1. The mutation rate determines

  1. the location and the spread of the equilibrium distribution, and also
  2. the speed of convergence to the equilibrium distribution.

Animation 2 makes the first point clear. In visual terms, an effect of increasing the mutation rate is to move equilibrium distribution from right to left, placing it closer to the distribution of the initial population. The second point is intuitive: the closer the equilibrium distribution is to the frequency distribution of the initial population, the faster the evolutionary process “gets there.” Not only does the evolutionary process have “less far to go” to reach equilibrium, when the mutation rate is higher, but the frequency distribution of fitnesses changes faster. Animation 3 allows you to see the differences in rate of convergence to the equilibrium distribution for evolutionary processes with different mutation rates.

Animation 3. Shown are runs of the Glass model with mutation rate we have focused upon, .005, doubled and halved. That is, uʹ = 2 ⨉ .005 = .01 for the blue process, and uʹ = 1/2 ⨉ .005 = .0025 for the orange process.

An increase in mutation rate speeds convergence to the equilibrium distribution, and reduces the mean frequency of maximum fitness.

I have selected a mutation rate that strikes an optimal balance between the time it takes for the evolutionary process to settle into equilibrium, and the time it takes for maximum fitness to occur when the process is at (or near) equilibrium. With the mutation rate set to .005, the average wait for the first occurrence of maximum fitness, in 1001 runs of the Glass model, is 1857 generations. Over the long term, maximum fitness occurs in about 1 of 295 generations. Although it’s not entirely accurate, it’s not too terribly wrong to think in terms of waiting an average of 1562 generations for the evolutionary process to reach equilibrium, and then waiting an average of 295 generations for a maximally fit individual to emerge. Increasing the mutation rate will decrease the first wait, but the decrease will be more than offset by an increase in the second wait.

Figure 2. Regarding Glass’s algorithm (“Parameter Dependence in Cumulative Selection,” Section 3) as a problem solver, the optimal mutation rate is inversely related to the squared string length (compare to his Figure 3). We focus on the case of string length (number of heritable traits) L = 50, population size N = 500, and mutation rate uʹ = .005, with scaled mutation rate uʹ L2 = 12.5 ≈ 23.64. The actual rate of mutation, commonly denoted u, is 26/27 times the rate uʹ reported by Glass. Note that each point on a curve corresponds to an evolutionary process. Setting the parameters does not inform the evolutionary search, as Marks et al. would have you believe, but instead defines an evolutionary process.

Figure 2 provides another perspective on the point at which changes in the two waiting times balance. In each curve, going from left to right, the mutation rate is increasing, the mean fitness at equilibrium is decreasing, and the speed of convergence to the equilibrium distribution is increasing. The middle curve (L = 50) in the middle pane (N = 500) corresponds to Animation 2. As we slide down the curve from the left, the equilibrium distribution in the animation moves to the left. The knee of the curve is the point where the increase in speed of convergence no longer offsets the increase in expected wait for maximum fitness to occur when the process is near equilibrium. The equilibrium distribution at that point is the one shown in Figure 1. Continuing along the curve, we now climb steeply. And it’s easy to see why, looking again at Figure 1. A small shift of the equilibrium distribution to the left, corresponding to a slight increase in mutation rate, greatly reduces the (already low) incidence of maximum fitness. This brings us to an important question, which I’m going to punt into the comments section: why would a biologist care about the expected wait for the first appearance of a type of organism that appears rarely?

You will not make sense of what you’ve seen if you cling to the misconception that evolution searches for the “target” of maximally fit organisms, and that I must have informed the search where to look. What I actually did, by fine-tuning the parameters of the Glass model, was to determine the location and the shape of the equilibrium distribution. For the mutation rate that I selected, the long-term average fitness of the population is only 79 percent of the maximum. So I did not inform the evolutionary process to seek out individuals of maximum fitness. I selected a process that settles far away from the maximum, but not too far away to suit my purpose, which is to observe maximum fitness rapidly. If my objective were to observe maximum fitness often, then I would reduce the mutation rate, and expect to wait longer for the evolutionary process to settle into equilibrium. In any case, my purpose for selecting a process is not the purpose of the process itself. All that the evolutionary process “does” is to settle into statistical equilibrium.

Sanity check of some claims in the book

Unfortunately, the most important thing to know about the Glass model is something that cannot be expressed in pictures: fitness has nothing to do with an objective specified independently of the evolutionary process. Which variants of traits contribute 1 to fitness, and which contribute 0, is irrelevant. The fact of the matter is that I ignore traits entirely in my implementation of the model, and keep track of 1s and 0s instead. Yet I have replicated Glass’s results. You cannot argue that I’ve informed the computer to search for a solution to a given problem when the solution simply does not exist within my program.

Let’s quickly test some assertions by Marks et al. (emphasis added by me) against the reality of the Glass model.

There have been numerous models proposed for Darwinian evolution. […] We show repeatedly that the proposed models all require inclusion of significant knowledge about the problem being solved. If a goal of a model is specified in advance, that’s not Darwinian evolution: it’s intelligent design. So ironically, these models of evolution purported to demonstrate Darwinian evolution necessitate an intelligent designer.

Chapter 1, “Introduction”

[T]he fundamentals of evolutionary models offered by Darwinists and those used by engineers and computer scientists are the same. There is always a teleological goal imposed by an omnipotent programmer, a fitness associated with the goal, a source of active information …, and stochastic updates.

Chapter 6, “Analysis of Some Biologically Motivated Evolutionary Models”

Evolution is often modeled by as [sic] a search process. Mutation, survival of the fittest and repopulation are the components of evolutionary search. Evolutionary search computer programs used by computer scientists for design are typically teleological — they have a goal in mind. This is a significant departure from the off-heard [sic] claim that Darwinian evolution has no goal in mind.

Chapter 3, “Design Search in Evolution and the Requirement of Intelligence”

My implementation of the Glass model tracks only fitnesses, not associated traits, so there cannot be a goal or problem specified independently of the evolutionary process.

Evolutionary models to date point strongly to the necessity of design. Indeed, all current models of evolution require information from an external designer in order to work. All current evolutionary models simply do not work without tapping into an external information source.

Preface to Introduction to Evolutionary Informatics

The sources of information in the fundamental Darwinian evolutionary model include (1) a large population of agents, (2) beneficial mutation, (3) survival of the fittest and (4) initialization.

Chapter 5, “Conservation of Information in Computer Search”

The enumerated items are attributes of an evolutionary process. Change the attributes, and you do not inform the process to search, but instead define a different process. Fitness is the probabilistic propensity of a type of organism to leave offspring, not search guidance coming from an “external information source.” The components of evolution in the Glass model are differential reproduction of individuals as a consequence of their differences in heritable traits, variety in the heritable traits of offspring resulting from recombination and mutation of parental traits, and a greater number of offspring than available resources permit to survive and reproduce. That, and nothing you will find in Introduction to Evolutionary Informatics, is a fundamental Darwinian account.

1,439 thoughts on “Evo-Info 3: Evolution is not search”

  1. DNA_Jock: You may not have noticed, but there are 50 comments on each page. So the formula
    (comment-page number -1) x 50 + (number on page) yields a unique post number that identifies any comment in a thread.

    I actually considered that. But it didn’t work out. But I’m sure you thought of everything.

    The first comment in a thread is number 1, this comment is number 549.

    For me, your comment is the 44th comment on the 11th page. Let’s see if you’re bright enough to figure out why. I bet phoodoo gets it before you do.

    You are getting more and more like phoodoo, btw.

    And you’re getting more like keiths.

  3. DNA_Jock: If out of, say. 210,000 games, we (like Bert) think that there has only been one Perfect Game, then we would estimate that P(Perfect Game) is about 1/210,000 (with a rather wide margin of error, btw). Ernie provides more complete data, showing that the correct numbers are 23/210,100.

    And Bert was an idiot, and your analogy is completely gratuitous. It doesn’t reflect anything real that has actually gone on, which is why I originally ignored it.

    DNA_Jock: Holy shit! Our best estimate has gone up dramatically.

    How dramatically? And how significant? And what if Bert had thought there had only been 20 perfect games? Oh, and yes, when you cherry pick your numbers, you can get the results you want.

    DNA_Jock:
    Sure, Mung, they are still rare, just not as unlikely as Bert originally thought.

    And what does this have to do with anything, DNA_Jock? Because no one ever said just how rare eyes are (1 in 120,000 for example). No one. And no one ever argued that eyes are rare, just not as rare as Bert thought.

    You came to the party late and it shows.

  4. Mung: Who ever claimed there was a contradiction?

    You did, when you wrote

    I guess Perfect Games are more likely than you originally thought.

    No, they are still rare. ”
    [emphasis added]

    re the numbering: I guess you’ll just have to un-ignore to get the point. Which you really should anyway…

  5. DNA_Jock: AIUI, this was Rumraket’s point that you mis-construed. So when you find “Increasingly likely relative to our previous, less well-informed, estimate.” to be “funny” and “not to be taken seriously”, you are displaying your ostrich-like inability to comprehend.

    What previous, less well-informed estimate? There wasn’t one. So there’s nothing to compare our new estimate to. And there’s no new estimate either. So your analogy misses the point. Badly. And your judging that Rumraket is right and I am wrong is misguided. At best.

  6. DAN_Jock, you agreed that perfect games are still rare.

    Sure, Mung, they are still rare…

    I agree. What contradiction?

  7. Mung: And Bert was an idiot, and your analogy is completely gratuitous.

    Not as much of an idiot as you appear to be here. Bert was working off incomplete information. Ernie, with more information, had a more well-informed estimate.
    If we believe that event X has occurred Y times, out of Z opportunities that we know of, then our best estimate for the probability of X = Y/Z.
    If we gather more data, and discover that event X has actually occurred 3X times, over 1.5Z opportunities, then our estimate is revised upwards to 2 Y/Z.
    As you correctly noted, every time a game is not a Perfect Game, our estimate of P(Perfect Game) goes down, and every time a game is an Perfect Game, it goes up.

    DNA_Jock: Holy shit! Our best estimate has gone up dramatically.

    How dramatically? And how significant? And what if Bert had thought there had only been 20 perfect games? Oh, and yes, when you cherry pick your numbers, you can get the results you want.

    22.98905 -fold
    1.15 -fold
    It’s an illustration of a concept, Mung.

    You came to the party late and it shows.

    Well, I’m not drunk yet. So there is that…

    Is there a concept here that you are having difficulty with?
    I’ve had a tendency to overestimate you lately. I need to keep reminding myself of that whole dimensions-of-a-fitness-landscape fiasco.

  8. Oh dear.
    Mung, you disputed my statement:
    “I guess Perfect Games are more likely than you originally thought.”
    with the response
    “No, they are still rare.”

    I think that these two statements are mutually consistent, being both true, ‘n’all.
    In normal English usage, your “No” carries the implication that “they are still rare” is NOT consistent with “they are less likely than you originally thought.”
    If you merely wished to change the topic of conversation, you could write “But”, or “Yes, but” or “Maybe, but”.
    This is getting weird.

  9. DNA_Jock: If we believe that event X has occurred Y times, out of Z opportunities that we know of, then our best estimate for the probability of X = Y/Z.
    If we gather more data, and discover that event X has actually occurred 3X times, over 1.5Z opportunities, then our estimate is revised upwards to 2 Y/Z.

    There’s nothing controversial about that. And it puzzles the hell out of me why you think convincing me of it has anything to do with the disagreement with Rumraket.

    My claim all along is that we don’t know the probabilities because we lack essential information. I even asked him to agree with me that we don’t know the probabilities. He declined and went on as if they were irrelevant.

    There is no X occurred Y times out of Z opportunities to give us our starting point.

    There was no gathering of more data to to discover that X has actually occurred 3X times over 1.5Z opportunities. We simply do not have that data.

    So there is no way to compare the two and revise our estimates, because 1) we never had an initial estimate in the first place, and 2) we don’t have any new figures either.

    So to argue that eyes are not improbable, or that eyes are easy to evolve, and then to claim that probability theory supports those claims, well, I admit I don’t see how.

    And no one has given me any actual numbers to work with. For example, if we say eyes have evolved 40 times. How many times did eyes not evolve? Out of how many opportunities? Aren’t those the numbers we need to know?

    If you can find out where put any number to any of this I’d see like to see it. I certainly never made any claims like that IDiot Bert.

    Bert: “A perfect game is incredibly unlikely — it has only ever happened once”

    Except Bert had only been to one baseball game ever and it was a perfect game.

    Bert “A perfect game is highly likely, it’s happened every time I’ve seen a game!”

    That’s the evolutionist version, lol.

  10. This thread is a keeper.

    On the one hand, we have Mung’s meltdown, including this gem of bafflement:

    DNA_Jock:

    TAYQ: see posts 509, 499, 480 (Bert and Ernie), 470, and 449 in this thread.

    Mung:

    I don’t know which posts these three digit numbers refer to. Are you using some sort of reader that displays those numbers to you?

    Meanwhile, we have phoodoo overturning the entire field of statistics with the “discovery” that random sampling can tell you nothing about the sampled population. Or, as he puts it in this classic example of phoodooese:

    If there are 10 hundred, trillion, billion gazillion balls in a bag, and you only pull out 90, you have taken out so few, that its the same as only taken out 1-only worse.

    And, by the way, this is the guy who’s figured out how to support himself in retirement through roulette. Just hasn’t gotten around to doing it yet.

  11. DNA_Jock, to Mung:

    I’ve had a tendency to overestimate you lately. I need to keep reminding myself of that whole dimensions-of-a-fitness-landscape fiasco.

    For those who missed it, Jock is referring to Mung’s comical misunderstanding of fitness landscapes in this thread.

    The highlight was Mung’s declaration that a two-dimensional fitness landscape is impossible, because fitness is represented by height, which is always the third dimension:

    keiths:

    In a two-dimensional landscape, height still represents fitness, but horizontal motion is limited to one dimension — a line, rather than a plane.

    Mung:

    So in a two-dimensional landscape there three dimensions?

    Left, Right. Up. Down.

    Define your terms. Horizontal. Plane. Motion. Landscape.

    In a two dimensional landscape there is no height. In a two dimensional landscape there is no landscape.

    There is no plane, in your two-dimensional landscape. Hah. Unbelievable.

    keiths:

    Seriously, Mung?

    Right-left: first dimension.
    Up-Down: second dimension.

    Two dimensions in a two-dimensional landscape.

    And:

    Mung, how many dimensions in a vertical plane?

    Out of curiosity, what is your educational background?

  12. phoodoo,

    If there are 10 hundred, trillion, billion gazillion balls in a bag, and you only pull out 90, you have taken out so few, that its the same as only taken out 1-only worse.

    Here’s why that’s completely wrong:

    1. The more balls you randomly sample, the less likely it is that the sample will be unrepresentative of the entire population.

    2. The factor by which that likelihood decreases, with each additional ball sampled, depends on the percentages of balls of each color remaining in the bag, not on their absolute numbers. If you are drawing randomly from a bag that contains 55% white balls and 45% black balls, it makes no difference whether there are a) 55,000 white balls and 45,000 black balls in the bag, or b) 55,000,000 white balls and 45,000,000 black balls, or c) 55 gazillion white balls and 45 gazillion black balls. In each case there is a 55% chance of drawing a white ball and a 45% chance of drawing a black ball.

    3. When the population size is large relative to the sample size, then sampling without replacement is approximated by sampling with replacement. And by #2, the likelihoods depend on percentages, not on absolute numbers. When you are sampling with replacement, the percentages do not change at all. When you are sampling without replacement, they do change, but only negligibly when the population size is large relative to the sample size.

    Do you see your mistake?

  13. My own exposure to roulette is limited to precisely two spins. A mate and I acquired a free meal voucher for a casino restaurant. We turned it into a free meal apiece by betting the price of a meal on red. The reasoning was that if we won, free meals all round, if we lost, still got a free meal while the other guy essentially paid for the ‘free’ sandwiches they kept bringing round. They gave us another meal voucher, so we operated our ‘system’ again. Did it twice, won twice. I am thinking of writing a book … just need some padding. I may already have revealed too much.

  14. But what if there were 10 hundred, trillion, billion gazillion slots in the wheel?

    Did you think of that, Mr. Smarty Pants?

    </phoodoo>

  15. phoodoo a net winner on games of chance? Sal Cordova claims the same. Eeeenteresting (draws deck from inside pocket, chews on cheroot, motions to empty chair).

  17. Mung:

    There is no X occurred Y times out of Z opportunities to give us our starting point.

    Thanks to the bias in our sampling procedure, our estimates of the denominator are going to be rather vague, I agree. But here’s the weird thing – if we gather additional data, wherein the numerator increases by a greater factor than the (vaguely defined) denominator could possibly increase, then our best estimate of the quotient will be revised upwards. The only way that your argument would make any sense, would be if you were arguing that the denominator could have changed by a greater factor than the numerator — but you have not supported that position. Instead you seem to have been focused on the purportedly incalculable nature of the denominator. That is not actually relevant to the direction in which the quotient is moving.
    This ground has been covered before, recently. One does not need to know the absolute probability of an event, or even have a way of calculating it, in order to be able to describe situations in which the probability has increased or decreased.

    My claim all along is that we don’t know the probabilities because we lack essential information.

    Very interesting. I did not realize that you were not an IDist.

  18. keiths,

    … a wheel so big it exerts its own gravitational force; a ball so small it sticks to anything it touches …

  19. DNA_Jock,

    One does not need to know the absolute probability of an event, or even have a way of calculating it, in order to be able to describe situations in which the probability has increased or decreased.

    Yep. Mung once attempted to make significant quantities of hay from an assertion of mine that, life having arisen once, the chance of it happening again is reduced (due, of course, to competition and predation by the existing evolved forms). The argument being that I didn’t know what the probability was, so how could I say that?

  21. Allan Miller: phoodoo a net winner on games of chance? Sal Cordova claims the same. Eeeenteresting (draws deck from inside pocket, chews on cheroot, motions to empty chair).

    Big difference between roulette and blackjack. And it just happens to be the difference between sampling with replacement (roulette) and sampling without replacement (blackjack). There are in fact card counters who win at blackjack. The casinos forbid the practice. But there’s no law against it. So Latter-Day Hebrew Warriors Carrying Get-Out-of-Hell-Free Cards Stamped John 3:16 think it’s morally fine to take money from the evil casinos. (Of course, this is nothing compared to voting to entrust Donald Trump with the nuclear codes.) The documentary Holy Rollers, in which Sal is credited, is worth watching.

  22. keiths: 1. The more balls you randomly sample, the less likely it is that the sample will be unrepresentative of the entire population.

    Is each draw an independent event?

  24. phoodoo,

    Is each draw an independent event?

    Yes, if you return each ball to the bag after drawing it. No, otherwise. But the difference is so small as to be negligible.

    To see this, suppose your population size is 100,000,000 and your sample size is 10. What are the odds of getting an unrepresentative sample in which the balls are all black?

    If you sample with replacement, the probability of a black ball remains exactly the same each time you draw: 0.45. The probability of getting an all-black sample is therefore 0.45 raised to the tenth power.

    If you sample without replacement, then the probability changes ever so slightly with each draw. The probability of drawing ten black balls becomes

    45,000,000/100,000,000 * 44,999,999/100,000,000 * 44,999,998/100,000,000 * 44,999,997/100,000,000 … * 44,999,991/100,000,000 .

    Do the math.

  25. keiths: The probability of getting an all-black sample is therefore 0.45 raised to the tenth power.

    First off, what bag are you talking about, you don’t know what percentage inside the bag are black, so you also don’t know what the probability is. But let’s go on for fun shall we?

    So if the first two pulled are black (forget the population decreasing, since it could be an infinite bag) , does the next pull become less likely to be black? And then what about the one after that, does it become even less likely to be black? Does the probability that the next draw will be black go down each time I draw one black?

    Furthermore, it is irrelevant if you say it becomes less likely, since less likely is just that, its less likely. So what? Maybe something less likely happened, how would you know? Maybe all you have learned is that something unlikely happened. Get it?

  28. phoodoo,

    We are talking about this goofy statement of yours:

    If there are 10 hundred, trillion, billion gazillion balls in a bag, and you only pull out 90, you have taken out so few, that its the same as only taken out 1-only worse.

    You got it completely wrong. Do you see that now?

  29. DNA_Jock: Very interesting. I did not realize that you were not an IDist.

    When I don’t know how to calculate the probabilities I say so. If only evolutionists were as honest. I really do love the irony. the problem with ID is it never calculates the probabilities.

    Pot. Kettle. Black.

  30. DNA_Jock: He probably thought you too were indulging in the Gambler’s Fallacy.

    Are you suggesting that I accused Rumraket of the Gambler’s Fallacy? Are you even paying attention?

  32. Mung,

    When I don’t know how to calculate the probabilities I say so. If only evolutionists were as honest.

    Unguided evolution fits the evidence of the objective nested hierarchy trillions of times better than ID. Which do you think is more likely to be true?

    ID is for chumps.

  33. DNA_Jock: Thanks to the bias in our sampling procedure, our estimates of the denominator are going to be rather vague, I agree. But here’s the weird thing – if we gather additional data, wherein the numerator increases by a greater factor than the (vaguely defined) denominator could possibly increase, then our best estimate of the quotient will be revised upwards.

    Could you say something about eyes? How is this relevant to eyes?

    Let’s see if we can’t dumb this down enough that even I can get it.

    What is the denominator?

  35. Mung,

    The truth hurts. But you haven’t answered my question:

    Unguided evolution fits the evidence of the objective nested hierarchy trillions of times better than ID. Which do you think is more likely to be true?

  36. DNA_Jock: One does not need to know the absolute probability of an event, or even have a way of calculating it, in order to be able to describe situations in which the probability has increased or decreased.

    Do you think that’s what I have been saying? What if I say that probabilities are expressed as fractions? Are you saying that they are not? Or that they need not be?

    DNA_Jock: One does not need to know the absolute probability of an event, or even have a way of calculating it, in order to be able to describe situations in which the probability has increased or decreased.

    ok. Keep in mind it’s me you’re talking to. So dumb it down. So what do we need to know to decide whether a probability has increased or decreased.

    I draw 10 green balls from a bag. On the eleventh draw, I draw a red ball. The probability that the bag had only green balls is what? Zero? Is that what you mean?

    What was the probability it had only green balls before we started? No one knows. So did the probability increase or decrease? Obviously, this is all quite far over my head. Can you spare a ladder?

  38. DNA_Jock: Instead you seem to have been focused on the purportedly incalculable nature of the denominator. That is not actually relevant to the direction in which the quotient is moving.

    No, I’ve been focusing on the absence of relative frequencies. That has to be obvious from what I have written in my previous posts.

    How do we decide which direction the quotient is moving? We have to compare it to something, right? So what are we comparing it to?

  40. Mung,

    When I don’t know how to calculate the probabilities I say so. If only evolutionists were as honest. I really do love the irony.

    And now, hopefully you will complete this thought by revealing an evolutionist – flesh and blood, not straw – who says they do know. I could certainly give you many Creationist examples.

  41. Allan Miller: And now, hopefully you will complete this thought by revealing an evolutionist – flesh and blood, not straw – who says they do know.

    As I’ve said in a couple recent posts in this thread, I invited Rumraket to join with me in agreeing that we do not know the probabilities and cannot calculate them. He declined to do so. From that, I believe I am warranted in making an inference that he thinks he does know. Of course, he could just be obstinate and unwilling to admit to what he does not know. Another disturbing trait common among evolutionists here.

  43. Mung,

    Of course he doesn’t know. There is no way he is going to even attempt to persuade you that he does. Yet you infer that he claims to know but chooses not to tell you? Methinks thou dost infer too much.

  44. Mung: Are you suggesting that I accused Rumraket of the Gambler’s Fallacy? Are you even paying attention?

    The reverse Gambler’s Fallacy, yes, as in “Doing it once actually makes doing it twice even more probable!”. Although you did then get it the ‘right ‘ way round when sharing a giggle with your bff, viz: “Hey phoodoo, I have never won the lotto. I am due brother!” And yes.
    You have since modified your argument to be less obviously wrong. Bully for you.

  45. Mung: Do you think that’s what I have been saying? What if I say that probabilities are expressed as fractions? Are you saying that they are not? Or that they need not be?

    A probability is a number between 0 and 1 inclusive. If it’s a rational number, then it may be expressed as a fraction.

    DNA_Jock: One does not need to know the absolute probability of an event, or even have a way of calculating it, in order to be able to describe situations in which the probability has increased or decreased.

    Do you agree with this statement or not?

    ok. Keep in mind it’s me you’re talking to. So dumb it down. So what do we need to know to decide whether a probability has increased or decreased.

    Already answered, Mung. In the comment that you were replying to, even.

    I draw 10 green balls from a bag. On the eleventh draw, I draw a red ball. The probability that the bag had only green balls is what? Zero? Is that what you mean?

    No.

  46. Mung: Could you say something about eyes? How is this relevant to eyes?

    Let’s see if we can’t dumb this down enough that even I can get it.

    What is the denominator?

    See comment 567509

    e4trvth oops!

  47. DNA_Jock: You have since modified your argument to be less obviously wrong. Bully for you.

    Yes. Rumraket made it clear that he didn’t say what it looked like he said. So I dropped that line. Yes. Bully for me.

  48. DNA_Jock: Do you agree with this statement or not?

    Well, I confess I don’t know what absolute probability means. To me, all probability is relative. Do you mean we don’t need to know the exact probability? To which my answer is of course I agree. We’re not talking about perfect knowledge.

  49. DNA_Jock to Allan, re Mung “He probably thought you too were indulging in the Gambler’s Fallacy.”
    Mung, ever quick to take offence: “Are you suggesting that I accused Rumraket of the Gambler’s Fallacy? Are you even paying attention?”
    DNA_Jock: “Yes: and here are the quotes: [quotes] You have since modified your argument to be less obviously wrong. Bully for you.”
    Mung “Yes. Rumraket made it clear that he didn’t say what it looked like he said. So I dropped that line. Yes. Bully for me.”
    That’s quite the admission.

  50. Mung,

    What was the probability it had only green balls before we started? No one knows. So did the probability increase or decrease? Obviously, this is all quite far over my head.

    Obviously. You are still utterly baffled by the concept of epistemic probability.

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