Evo-Info 3: Evolution is not search

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,  = 2 ⨉ .005 = .01 for the blue process, and  = 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  = .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 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. Mung,

    I think your reply to Allan is best when you just said this:

    You see, phoodoo, if you draw one black ball, the frequency is 1. If you draw two black balls, the frequency is 2×1. If you draw three black balls, the frequency is 3×1.

    That is the entire claim of the evolutionist. They draw the ball from the bag, it is black, so now the balls inside the bag must be black. If they draw another ball, and it is green, it must be 50% black and 50% green. They have nothing else to base their conclusions on than this.

    You will never know when a rare event happened, you will never know when a rare event happened twice, and you will never know if ANY of your conclusions are valid, other than if there was one black ball drawn, the bag contained some black balls (or maybe only one).

    It has nothing to do with probability and statistics, it is simply what you see, and that is what you conclude. If we haven’t seen an alien, there are no aliens, and there is zero percent chance there are aliens. Until we see one.

  2. Mung,

    I guess at some point we can forgive Allan for having been away and upon returning in jumping right in without knowing the context. But after seeing this over and over again it is starting to become a tad tiresome.

    I’ve not claimed that eyes are impossible without design. Nor has phoodoo. Nor has anyone else in this thread or the eye thread.

    It is not necessarily the case that every point I make is directly related to something you personally have said. The argument I mentioned is a very common one. If you are not making it, there is no need to bristle then, is there? I didn’t refer to you, I referred to a hypothetical Creationist. It’s not all about you, you know.

    I’ve also not made an initial assessment that eyes are vanishingly improbable. In fact i have explicitly denied making any initial assessment. Of course, if you’re not really participating in this thread you probably missed that.

    In fact, it is evolutionists who say the eye is improbable:

    Natural selection is not just a scientifically respectable alternative to divine creation. It is the only alternative that can explain the evolution of a complex organ like the eye. The reason that the choice is so stark – God or natural selection – is that structures that can do what the eye does are extremely low-probability arrangements of matter.

    This is just stupid. You find one evolutionist who makes the rather bizarre statement that eyes are low-probability arrangements of matter (as if they are just picked from an infinity of random permutations of atoms). This, you seem to argue, proves that it is just evolutionists, and not Creationists, who make this point. Bollocks. I could give you dozens of references to this very argument in Creationist literature. I can’t be bothered to do so, because I know damn well you know I could.

    As a final point, I don’t think information can be information without being being relative to something else.

    Huh?

  3. phoodoo,

    I think your reply to Allan is best when you just said this:

    And then quote something that was a response to someone else entirely, on a separate issue – misrepresenting their point to boot.

  4. I’ll come in again.

    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.

    There. Since the word “absolute” seemed to be causing you confusion, we can drop it.
    Do you, Mung, agree with the quoted statement?

  5. One big confusion in all this, as I see it, is that some see every statement made about probability as being a statement about evolution. And therefore to be resisted at all costs. Hence the strange spectacle of Mung flip-flopping between stating he disagrees with phoodoo one minute, while they clutch each other with mirth the next.

  6. phoodoo:
    Right

    Then it is more than likely it is certain.

    And that is just what Mung was explaining.

    That once a blue ball has been picked the probability of at least one blue ball is 1?

    And what’s the frequency Kenneth?

    Not Kenneth but 1, if you discovered Aliens twice, 2.

  7. phoodoo: I hate aliens, so I am so relieved, the frequency is zero and the probability is zero.

    The frequency of the discovery is 0, the probability is unknown but has been estimated by the Drake Equation .

    Maybe, but I doubt it, DNA and keiths and Rum and Joe will start to see the problem.

    I think they all understand the source of the problem

  8. Allan Miller: One big confusion in all this, as I see it, is that some see every statement made about probability as being a statement about evolution. And therefore to be resisted at all costs.

    LoL. I am all for an evolution that is quantifiable. As long as it remains primarily storytelling, well, then not so much.

  9. DNA_Jock: Do you, Mung, agree with the quoted statement?

    I think I already answered that question.

    Mung: 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.

    That would be a yes.

    Would you care to explain the relevance to the current discussion? Do you agree that at least so far, we don’t have an initial estimate?

  10. DNA_Jock: I really haven’t studied eye evolution in any detail. But it seems to me that eye evolution is plausible, at least — Darwin’s gradualism is supported by what we have learnt since Origin was written. If you want to argue that eye evolution is implausible, then the ball is in your court, mate.

    We covered the plausibility argument a long time back. Where were you then?

    The question now though is not whether or not the evolution of the eye is implausible, it’s whether it is probable.

    If someone not named Mung says that the evolution of the eye is not improbable, would that ball be in my court too? If someone not named Mung says that it’s easy to evolve an eye, would that ball be in my court too?

    I can just see myself standing there laughing with their balls in my hand.

  11. phoodoo: It has nothing to do with probability and statistics…

    I disagree with that.

    …it is simply what you see…

    I agree with that.

    But in agreeing with that, I disagree with this:

    They draw the ball from the bag, it is black, so now the balls inside the bag must be black. If they draw another ball, and it is green, it must be 50% black and 50% green.

    I don’t think it’s about what is left in the bag. It’s only about what has been observed.

  12. The question:
    Do you agree with this statement?

    One does not need to know the 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.
    [Emphasis added for lulz]

    Mung’s response:

    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.

    Fairly obviously, that’s not what I meant.
    I’ll come in again.
    Do you, Mung, agree with the statement:

    One does not need to be able to calculate the probability of an event in order to be able to describe situations in which the probability has increased or decreased.

    ?

  13. Mung,

    Yea, well, until you haven’t pulled something out of the bag, I suppose there is no reason to believe anything is left in the bag.

    Since we don’t know what is in the bag, there can not be any statistics, perhaps if the first ball you pull out is black, that was the only thing inside. In fact, that is all there is, until its not.

  14. DNA_Jock: Fairly obviously, that’s not what I meant.

    You seem to be playing word games. Perhaps if we keep tossing this coin we’ll manage to converge.

    One does not need to be able to estimate the probability of an event in order to be able to describe situations in which the probability has increased or decreased.

    I disagree. If you are going to claim that some probability has changed, you need to know what it is that you’re talking about that has allegedly changed. Else you won’t know by which direction it changed.

    If you’re asking whether the values upon which you based your estimate have to be precise or accurate, to that I would say no. They could take on a range of values.

    Are we getting closer?

  15. phoodoo: Yea, well, until you haven’t pulled something out of the bag, I suppose there is no reason to believe anything is left in the bag.

    Other than shaking the bag?

    Since we don’t know what is in the bag, there can not be any statistics, perhaps if the first ball you pull out is black, that was the only thing inside. In fact, that is all there is, until its not.

    Reach back in and you will know for sure

  16. Allan Miller: One big confusion in all this, as I see it, is that some see every statement made about probability as being a statement about evolution. And therefore to be resisted at all costs.

    Yeah, like Mung disagreeing with the statement

    One does not need to be able to calculate estimate the probability of an event in order to be able to describe situations in which the probability has increased or decreased.

    Epic.

  17. Mung: You seem to be playing word games. Perhaps if we keep tossing this coin we’ll manage to converge.

    Now that is funny

  18. DNA_Jock: Epic.

    You asked for an answer. I gave you one. I have no idea what you think it means to calculate a probability (or not calculate one). That you weren’t being exactly clear has already been established.

    calculate To make an estimate of; evaluate

    If that’s the definition you’re using then my answer ought to be definitive. You have a problem with that?

  19. Absolutely hilarious. DNA_Jock asked me to take a stance and I did. Maybe that’s such an improbable event that he finds it absolutely Epic!

    🙂

  20. Mung: Absolutely hilarious. DNA_Jock asked me to take a stance and I did. Maybe that’s such an improbable event that he finds it absolutely Epic!

    Well, since you brought the subject up, I’ve always considered you as more of a “Hey, what’s the deal with Aquaman?” kinda comic.
    Here goes.
    I present you with a well-shuffled deck of cards, and ask you “What’s the probability that you draw a heart from the deck?”

  21. DNA_Jock: I present you with a well-shuffled deck of cards, and ask you “What’s the probability that you draw a heart from the deck?”

    52 cards, 25% of which are hearts with a gun held to my head?

    52 / 13 = 1 / 4 = 0.25

    Hang on, let me check my calculations …

  22. But you’re apparently making the same mistake that I made, because this doesn’t have anything to do with prior probabilities. We’re talking the frequentist interpretation of probability.

    So I draw a card, and it is what it is, and now I have information about the card that I drew. Right Allan?

  23. DNA_Jock: Who said it was a regular deck of cards?

    No one. Which is why I specified precisely the conditions upon which my calculation was based.

    52 cards, 25% of which are hearts with a gun held to my head?

    See that question mark at the end? Did you really think you were going to fool me?

    Epic!

  24. DNA_Jock:
    Who said it was a regular deck of cards?

    You mean maybe its a magic deck of cards? Maybe they can be all hearts one moment, and then suddenly change to all spades? Oh, you really caught Mung now, congratulations!

  25. Excellent! So we have established that you understand the situation, both the meaning of the phrase “draw a heart”, and the realization that, knowing DNA_Jock, it is NOT a regular deck.

    I’ll come in again.

    I present you with a well-shuffled deck of cards, and ask you “What’s the probability that you draw a heart from the deck?”

  26. DNA_Jock,

    Can I try, this looks like fun? Like compositional groundhogs day.

    52 cards, 25% of which are hearts with a gun held to my head?

    52 / 13 = 1 / 4 = 0.25

    Hang on, let me check my calculations …

  27. Christ. Neither of them can do simple arithmetic — it’s so confusing keeping the numerator and denominator straight — but they still believe that they are the ones who’ve spotted fundamental flaws at the heart of standard evolutionary and statistical reasoning.

    Typical Mung. Typical phoodoo. Typical ID.

  28. Mung,

    But you’re apparently making the same mistake that I made, because this doesn’t have anything to do with prior probabilities. We’re talking the frequentist interpretation of probability.

    We’re talking about epistemic probabilities and how they are updated in light of new information. And since you are baffled by the concept of epistemic probability — even idiotically dismissing it as ‘made up’ — you are floundering.

  29. keiths,

    Its not 25% chance? Wow, the mystery deepens!

    Plus we still have to one day figure out the point of DNA’s question. That’s really going to be hard.

  30. phoodoo: Oh, you really caught Mung now, congratulations!

    And Allan wonders why you and I clutch each other with mirth.

    Damn, I didn’t notice that DNA_Jock didn’t tell me anything about the deck other than that it was a deck of cards that was well-shuffled. I never saw that response coming! I am just DUMB! DoH.

  31. keiths: even idiotically dismissing it as ‘made up’

    Heh. I guess there’s those that receive their knowledge from golden plates in a hat and then there’s just the rest of us schleps making it up as we go along. Somehow, we get by.

  32. DNA_Jock:

    One does not need to be able to estimate the probability of an event in order to be able to describe situations in which the probability has increased or decreased.

    Mung:

    I disagree. If you are going to claim that some probability has changed, you need to know what it is that you’re talking about that has allegedly changed.

    He does. He’s talking about a probability, and he knows that he’s talking about that probability. To know that you’re talking about a probability does not require you to know the numerical value of that probability, either exactly or approximately

    Jesus, Mung. Isn’t that obvious?

  33. DNA_Jock: I present you with a well-shuffled deck of cards, and ask you “What’s the probability that you draw a heart from the deck?”

    About the same as drawing an eye from the deck. Or a liver. [ETA: probability of drawing a kidney being twice that of drawing a heart or liver.]

    ok, I don’t mind playing along. It’s the same. 0.25. That’s my initial guesstimate.

    Can I ask a question now? What does this have to do with frequentist probability? Just a hint is all i ask.

  34. keiths: We’re talking about epistemic probabilities and how they are updated in light of new information.

    Ever desperate to change the subject. And here i thought you found my struggles immensely amusing. Rumraket never once appealed to “epistemic probabilities” to justify his claims. Not once.

  35. Mung:

    Rumraket never once appealed to “epistemic probabilities” to justify his claims. Not once.

    Rumraket never used the phrase “epistemic probability”. Therefore, by Mung Logic, we can’t possibly be talking about epistemic probabilities.

    Derp.

  36. keiths: Jesus, Mung. Isn’t that obvious?

    No. How do I know he has a probability in mind? I’m not a mind reader, like you. And people can have all sorts of things in mind that are not a probability. Isn’t that so?

    So when you say he has a probability in mind, what do you mean, exactly? That he’s got a spot reserved, a hypothetical perhaps one day probability? Does that potential probability ever become an actual probability?

    Perhaps an analogy to help me out. I do love me a good analogy.

    He’s talking about a temperature, and he knows that he’s talking about that temperature. To know that you’re talking about a temperature does not require you to know the numerical value of that temperature, either exactly or approximately.

    Something like that?

  37. keiths: Rumraket never used the phrase “epistemic probability”. Therefore, by Mung Logic, we can’t possibly be talking about epistemic probabilities.

    LoL! No, it’s quite clear that you think you and DNA_Jock are talking about “epistemic probabilities.” It’s not at all clear that is what Rumraket was talking about.

    As soon as you even try to put together a logical argument we see how you can’t even do it successfully for even the simplest of arguments. Nice job!

  38. Mung: LoL! No, it’s quite clear that you think you and DNA_Jock are talking about “epistemic probabilities.” It’s not at all clear that is what Rumraket was talking about.

    What Rumraket wrote:

    You might have some ideas about how unlikely some things are, given some set of assumptions you have about how the world works. But if evidence keeps contradicting what your assumptions lead you to conclude about how unlikely those things are, perhaps it is time to reevaluate your assumptions?

    Not at all clear to Mung, perhaps, reinforcing yet again Allan Miller’s assessment of Mung’s mindset.

  39. So I haven’t followed this discussion these last few days, anything interesting have transpired? If anyone wants clarification on something I’ve said (or they just want to know my view on something) please just ask.

    I get that back when I agreed with the statement that with evolution eyes are likely, and without they’re not, the reasons for this might not have been initially clear. I did elaborate on why, though.

    If anyone is still unsure as to what the basis for me agreeing with the statement is, please just ask. Don’t presume I don’t have any reasons for making such a claim.

  40. Mung: ok, I don’t mind playing along. It’s the same. 0.25. That’s my initial guesstimate.

    Can I ask a question now? What does this have to do with frequentist probability? Just a hint is all i ask.

    A frequentist would note that, if we repeated this experiment a million times, the proportion of hearts observed would converge on the actual probability. Not really relevant though.
    You are going with 0.25. Interesting.
    Interesting, too, that it has become a “guesstimate”. Why have we moved from “calculate” to “estimate” to “guesstimate”? Sounds like word-gaming to me. Frankly, I don’t believe you.
    I tell you what: let’s meet IRL. I’ll give you odds of 3 to 1, and we play this game a hundred times. You put up $100,000, and I pay $4000 each time you draw a heart. That’s a thousand bucks a go.
    It’s a fair game, according to you.
    Methinks the penny has dropped, finally.

  41. phoodoo: Mung,

    I think your reply to Allan is best when you just said this:

    You see, phoodoo, if you draw one black ball, the frequency is 1. If you draw two black balls, the frequency is 2×1. If you draw three black balls, the frequency is 3×1.

    That is the entire claim of the evolutionist. They draw the ball from the bag, it is black, so now the balls inside the bag must be black. If they draw another ball, and it is green, it must be 50% black and 50% green. They have nothing else to base their conclusions on than this.

    This is too simplistic phoodoo. Admittedly the references to the frequentist interpreation of probability used has been simplistic, so I can see why you’d think we are proposing to draw a conclusion from a sample size of 1. But we aren’t actually doing that, and wouldn’t do that in a situation where we are completely ignorant about the contents of the bag (and the size of the bag and so on).

    In statistics you want what is called statistical significance. That means that the number of samples you draw must constitute a fraction of the total sample space that is large enough that you can have some reasonable confidence that the samples you took are representative of the sample space.

    Counterintuitively (and IIRC) sample sizes can be as low as a few hundred, for a total sample space of hundreds of millions, and you can still be 95% confident that your few hundred samples are representative. These are just elementary statistical facts. But they do come with some confounding assumptions, which if they are violated, undermine your confidence in the result.

    One of those assumptions is that the contents of the bag are pretty well mixed. In other words, if it’s full of black and white balls, all the black balls are properly mixed with the white balls. If the bag contains 350 million well-mixed black and white balls, drawing a few hundred from the bag is enough to get well into the 90’s % confidence interval.

    If all the black balls are at the bottom of the bag, and you assume they’re well mixed, drawing a range of whites will of course lead you to the wrong conclusion. Noone here is under any illusions about these elementary axioms of statistics. You should stop arguing as if people you disagree with about evolution, also disagree about the basics of statistical reasoning.

  42. “Epistemic probability” sounds to me like “boiling hot.” Or “epistemic temperatures.” What’s wrong with my temperature analogy Boy Wonder?

    What’s not like like about it?

  43. Mung,

    How do I know he has a probability in mind?

    Um, the fact that he said so.

    I’m not a mind reader, like you.

    You’re not even a reader.

    Derp.

  44. keiths: I hope you’ll take Jock up on his bet so you can learn about epistemic probability the hard way.

    You geniuses just don’t get it, do you.

    DNA_Jock would only make that wager if he already knows what the odds are. So he is in possession of information that he claims we don’t have, or need. If he played that game in the old west he’d get shot.

    Why don’t you two jokers get back to teaching me something I don’t already know. What probability did he have in mind?

    DNA_Jock: Methinks the penny has dropped, finally.

    Not likely.

  45. DNA_Jock: It’s a fair game, according to you.

    I never said anything about it being a game or about it being either fair or unfair. I just love how you have to put words in my mouth.

    Let’s play this out, from the beginning;

    DNA_Jock. I have this deck of cards, it’s well shuffled.
    Mung: Intruiging.
    DNA_Jock. Let’s play a game.
    Mung: ok, sure
    DNA_Jock: You take a card from the deck, any card.
    Mung: ok
    DNA_Jock: Not yet dummy!
    Mung: why not?
    DNA_Jock: I haven’t finished with the rules,
    Mung: Do proceed.
    DNA_Jock: You pay me a dollar for every card you pull.
    Mung: No. That’s silly.
    DNA_Jock: Wait, you can win money. Let me tell you the rest.
    Mung: ok, now I’m interested.
    DNA_Jock: For every heart you pull from the deck I’ll pay you 1,000 dollars.
    Mung. This isn’t gambling is it?
    DNA_Jock: Certainly not!
    Mung: Let me see that deck.
    DNA_Jock. Nope. That’s not in the rules.
    Mung: Well, then. Thanks but no thanks. My momma didn’t raise no fools.

  46. DNA_Jock would only make that wager if he already knows what the odds are.

    By Jove, I think you might be on the verge of getting it, finally!

    Jock knows something you don’t, so your epistemic probabilities differ.

    When you start playing the game and losing money hand over fist to Jock, you will — unless you are even stupider than we’d realized — revise your epistemic probabilities.

  47. Mung:

    My momma didn’t raise no fools.

    You might want to read this thread and reevaluate.

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