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
- the location and the spread of the equilibrium distribution, and also
- 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.
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.
The Series
Evo-Info review: Do not buy the book until…
Evo-Info 1: Engineering analysis construed as metaphysics
Evo-Info 2: Teaser for algorithmic specified complexity
Evo-Info sidebar: Conservation of performance in search
Evo-Info 3: Evolution is not search
Evo-Info 4: Non-conservation of algorithmic specified complexity
Evo-Info 4 addendum
Do not ask for whom will purportedly reproduce.
Ask for whom has reproduced.
And if that made sense, so would going to a casino and betting without first trying to calculate the odds. I wish I were running a casino with phoodoo among my clientele!
That’s a lousy example Joe, lol. Might as well have commented on people who play the lottery without having calculated the odds.
Analogy alert: Which happened first, matter or gravity?
Organism and evolution are simultaneous. The moment that the earliest precursor to life as we know it (self-replicating protein?) occurred, it was part of a fitness landscape. Life and evolution are the same thing. Environmental pressures have no meaning unless there is something which they are acting on.
I can just envision the environment trying our different size beak molds, planning to use some on the nearby finches.
Your agent-bias is showing.
If there is variation in the characteristics of beaks in a population of finches, some will be better suited to the environment while others will be worse suited. Which ones will likely have higher reproductive success?
That is up there with dumbest posts ever made on this forum. And that’s saying a lot.
The fact that something unexpected can happen, doesn’t mean that concepts based on expectations are meaningless. I expect the bus will arrive at it’s usual intervals, and it almost always does. Some times it unexpectedly doesn’t come, or is rather late, but it’s not like the concept of a schedule is rendered meaningless just because the bus on some very rare occasions doesn’t come (on time).
Why do I have to sit here and explain something so unbelievably basic?
That’s a variation of one of my favorite Fred Singer arguments against the smoking-cancer link. Unless you can predict exactly which smokers will get cancer and when, you can’t draw a causal link between smoking and lung cancer:-)
Rumraket,
Arguments about the concept of fitness being meaningless are obviously intended to apply to all uses of fitness evolutionary biology. But strangely, once you start explaining that the concept is meaningful, there often a sudden move of goalposts and the person specifies that the issue is the evolution of important and innovative complex structures, and that that is “macroevolution” and that the argument is only about that. No, it was stated as applying to everything.
Admittedly this hasn’t (yet!) happened in this thread.
You’re the one who spoke of the environment “pressuring” something. Does it “pressure” beak sizes without actually imposing any force on beaks?
I used a verb and, given your bias, you chose to interpret it as me giving intentionality to the environment. If I said that ‘the ocean swallowed the sinking ship’ would you assume that I meant the ocean had agent intentionality?
Put more blandly, in any population with variation in characteristics, some individuals will be better suited to thrive (or at least survive and reproduce) under a given set of environmental conditions. Do you agree or disagree with this statement?
I disagree, of course.
It’s a common failing of evolutionists that they often use words in a way that they don’t really mean what the evolutionist says. Then they blame the reader.
Notice how in rephrasing your earlier statement the environment isn’t “pressuring” anything. not only that, the statement is utterly untestable.
Under some set of environmental conditions which shall remain unspecified, stuff might happen, and some organisms might leave more offspring than others. Sure, maybe.
Under drought conditions, finches with longer beaks will leave more offspring than finches with shorter beaks. Maybe, maybe not.
That was the purpose of rephrasing it. I removed the active verb to avoid any unintended implication of agent-intentionality in my question.
Which part precisely do you disagree with? Do you disagree that populations can have variation of traits, or do you disagree that some individuals can be better-suited to thrive under a given set of environmental conditions?
Individuals can be different.
Some individuals can leave more offspring than others.
Drop the nonsense about thriving, which is a value judgment unless you’re going to redefine that term. And drop the silliness about some unspecified set of environmental conditions unless you can enumerate the conditions and connect them to the “thriving.”
But please allow me point out how you changed the subject. Your original statement was that “some individuals will be better suited to thrive.”
Maybe they will be, maybe they won’t be.
You wouldn’t agree with a stronger version of those two statements?
– Individuals will differ in various traits within a population.
– Individuals with traits more beneficial to survival and reproduction in a given set of environmental conditions will tend to leave more offspring than others.
Evolution. Problem. Solution. Best possible. Not that it’s a problem solving search or optimization algorithm. It’s both!
Incoherent blathering is not an argument.
We could, in principle, get somewhere by reaching a mutual understanding of what’s going on here. I say “in principle” because I don’t believe you’ll ever say, “OK, Marks, Dembski, and Ewert blew it. They have not addressed with their math what they actually needed to address.”
Let me ask first, do you understand that ID is not hanging in the balance? You’ve got guys making a variety of claims, and trying to make it sound as though they agree with one another. They don’t agree with one another, and you’re better off letting go of Dembski’s fabulously dishonest “make up a story about a law of conservation of information you know simply HAS to be true, and then scramble (over and over and over) to produce the goods” branch of ID than with it. You need to bury that stillborn brainchild that Dembski delivered 20 years ago, and move on.
In the OPs you’ve made since I posted this one, you seem to want Marks, Dembski, and Ewert to show that design occurs only by design. I’d say that’s essentially the story that tell in the book. No matter how much sense that makes to you intuitively, the conservation of information math at the center of the book has nothing to do with it. Their measure of so-called “active information” is not a measure of design. It’s simply a measure of relative improbability (expressed on a logarithmic scale — BFD).
Back to your attempt to enlist Simpson. The crucial question is whether the “problem” is given independently of the evolutionary process that putatively solves it, or is chosen to suit the process. If you select as the “problem” that evolution “solves” something that tends to occur in the process, then the conservation of information math does not apply. My first attempt at animation, back in November 2015 (see “The Law of Conservation of Information Is Defunct“), illustrated the reason why. Ewert’s unresponsive response was utterly pathetic. The grownups have known since then, if not before, that they screwed up. But they were already committed to the Big Book, and talked all the bigger for all the realization that they hadn’t accomplished what they’d intended with their math. (If you cannot tell that Winston Ewert is, on the scale of Ph.D. engineers and computer scientists, about as dopey as they come, then I probably shouldn’t bother with you at all. By the way, his former colleague George Montañez is a very bright guy, if yet to recover fully from the bad advising he suffered at Baylor.)
The conservation of information theorem is a special case of something that is quite simple, and is taught to advanced undergraduates at some schools: if you randomly select a problem solver from among a set of alternatives, then it probably does not perform much better than expected (i.e., much better than a randomly selected solver does on average). The measure of performance, however, cannot be set to favor whatever problem solver it is that you happen to draw.
Are modelers claiming to have been given a problem, and to have randomly selected an evolutionary process from among a set of alternative processes? Of course they are not. That’s nothing but an ancient “everything happens totally by chance” parody of naturalism. And you do not get to pin it on the modelers. They’ve said no such thing. There’s no reason for them to have said such a thing.
Marks, Dembski, and Ewert tell a story disconnected from their math, and count on readers to trust them when they say, “Oh, you don’t have to read the sections marked with asterisks.” Yes, you do have to read them, and work hard at grasping the significance of them, because the authors are no help at all. I’ll be generalizing and simplifying their results in Evo-Info 4. There will still be some mathematical concepts and squiggly marks, so phoodoo is bound to shit a brick. But Mung, if he tries, will stand a good chance of following it.
The witty professor Felsenstein.
What is interesting is that it doesnt matter one ‘wit’ whether the White-Crowned Sparrow or the Golden-Crowned Sparrow ever exist at all. Only that ‘birds’ exist. that is what goes way over your head, time after time.
You (pl) seem to be under the impression that White-Crowned or Golden-Crowned actually has some weighty evolutionary explanation to be expounded on for years to come. Sure good for the money flow, I know. But not much else. What have you learned from the golden-crowned sparrow other than its a nice bird to look at?
Species are a figment of your imagination. Own up to it.
Is your nickname the Mooch?
Are you a front stabber?
Why should I care, do mathematicians propose that math must adhere to some reality?
I don’t mind parlor tricks.
Here’s how the book ends:
(The harder they fall, the bigger they talk.)
It’s fine by me if your response to the “evolutionary informatics” branch of ID is “What should I care?”
Tom English,
So you acknowledge that mathematics has no connection to reality?
I’m not Tom. But I am a mathematician, so I’ll comment.
Mathematics is not about reality.
However, mathematics is done by mathematicians and mathematicians are real people. So it goes too far to say that mathematics has no connection to reality.
Unless they’re Marks, Dembski, and Ewerts’s “parlor tricks”.
In that case, there is no reason to assume any math is true.
True with respect to reality or true within its system?
We don’t assume. We prove it.
You possibly have confused ideas about “true”.
You possibly have confused ideas about prove. If the math is created by humans, then you proving that what you created is true is impossible.
If I created the idea of God, then I say I have proved it according to the methods I created, does that make it so?
I’ll take that as confirming that you have confused ideas about “true”.
I take that as YOU have confused ideas about true. Or else I take that that if someone says they proved God exists, it must be true.
Wow, Neils is a believer, congratulations!
phoodoo: Do you have a different attitude toward mathematical models when they are used by Marks, Dembski, and Ewert in Introduction to Evolutionary Informatics?
Well, you have already said that you do.
You and Tom make me laugh. You seem to think that ID theory rests on whether or not one believes one small science paper. Tom is so afraid of this paper he is dedicating half his life trying to discredit it. There are real signs of hysteria there. People should be worried about him.
So math may or may not pertain to anything real. One of the mathematician here has already stated that it is something completely invented by man, and thus we can never have certainty that it pertains to anything. So we can have no more certainty of Ewert, Dembski, and Mark’s math than we can of any other. We also can’t use math to refute them.
Do you think that is scoring some big win for you? Congratulations. But the problem for you is that you have no justification for saying that math is either right or wrong.
I think the hysteria the two of you have for other people’s written papers is much more telling than any math questions.
phoodoo,
Well, it seems to. There’s very little else out there in the way of formal scientific support for Intelligent Design. If it does not rest upon that small paper what does it rest on?
(Emphasis added.)
Did you miss a word, perchance?
By the way, Introduction to Evolutionary Informatics is a book, not a paper. It contains much of the rhetoric of Dembski’s No Free Lunch: Why Specified Complexity Cannot Be Purchased without Intelligence, but attaches it to material drawn from various papers defining active information (not specified complexity aka complex specified information) in various ways. In all honesty, I expected the authors to adopt the latest of the meanings that they have assigned to the term active information over the years, and to redo some of their published studies to produce a coherent whole. They have chosen instead to conceal their inconsistency by expressing themselves vaguely, and to claim that they’re doing it only to make their work accessible to general readers.
The reason that Joe and I focused on one of their papers is that Joe had ignored it in his first response to Dembski’s talk at the University of Chicago, and Dembski had given him grief for doing so. However, Dembski never explained why the omission was egregious. He had listed three major publications in his talk, but not bothered to mention that active information was defined differently in all three. So, while Joe has acknowledged that he should have studied the third paper, and went above and beyond in rectifying his error — I was a witness to that — Dembski has never acknowledged that he has drifted from one thing to another, year after year after year. He has contradicted himself so many times that he would be a liability, were he to serve as an expert witness in a judicial test of public-school instruction in ID. The ID movement is definitely better off without him.
Not completely.
Die ganzen Zahlen hat der liebe Gott gemacht, alles andere ist Menschenwerk. — Leopold Kronecker
However, we now treat the Devil’s own invention, sets, as more fundamental than the integers. The counting numbers 0, 1, 2, … are defined as sets:
\begin{align*}
0 &\equiv \{\} \\
1 &\equiv 0 \cup \{0\} = \{0\} = \{\:\{\}\:\} \\
2 &\equiv 1 \cup \{1\} = \{0, 1\} = \{\:\{\},\: \{\{\}\}\:\} \\
3 &\equiv 2 \cup \{2\} = \{0, 1, 2\} = \{\:\{\},\: \{\{\}\},\: \{\{\}, \{\{\}\}\} \:\} \\
&\phantom{:}\vdots
\end{align*}
I expect you to be enraged, not engaged, and certainly not enlightened, by this little something I’ve shown sophomores early in a first course in discrete mathematics.
Seriously (for just a moment), you ought to recognize that there’s a big difference between people who are fascinated by such stuff and those who are not. Why you, a generally intelligent person who evinces zero aptitude for math, would not discern the big difference, and would not suspect that he makes an ass of himself when he shoots off his mouth about math, is a mystery to me.
Searchless in Seattle.
Evolution is a search, and everyone knows it. for example:
Tom, to phoodoo:
I suspect that’s because your characterization of phoodoo as “a generally intelligent person” is inaccurate, to put it mildly.
I’d like the record to show that, when it comes to math, my gob remains firmly shut. Like King Crimson drummer Bill Bruford, credited on the drum-less track ‘Trio’ with ‘admirable restraint’, I feel I have made some of my most significant contributions thus.
Well Allan, at least you appear to know enough math to know that if you’re dealt a straight flush at poker that it doesn’t make it more likely that you’ll be dealt another straight flush.
Mung,
Indeed. However, if we are assessing a probability when we don’t know the size of the deck or the possible face values, two straight flushes may indicate we were wrong in thinking one unlikely. It’s a good example of poor analogy.
I see I spoke too highly of you too soon. Ah well. It is quite remarkable, the efforts people will go to in order to hang on to their favorite creation myth.
If we had a deck consisting of only five cards, and we dealt Rumraket a poker hand that gave him a straight flush. Shuffled the deck, dealt again, and lo and behold another straight flush. How many in a row would it take him before he figured out that being dealt one straight flush after another didn’t increases his chance of getting yet another one in the slightest. What’s the over and under?
Now increase the number of cards in the deck by one. Give it the same value and suit as one of the existing cards in the deck. Did that improve his odds of being dealt a straight flush? Only in evo-fantasy-land.
Poor confused Mung has learned nothing from his own repeated failures, nor from phoodoo’s.
Mung, here’s a hint: this topic is above your pay grade.
I see that you fail to distinguish frequentist from prior probabilities.
Could you please explain what aspect of evolution this thought-experiment is supposed to analogize?
Pretty much my response, so I of course think it’s pretty damned good.
Why don’t we talk about Marks, Dembski, and Ewert instead of Rumraket and Allan? I’ve just looked again at Chapter 4. It’s quite remarkable, the lengths that creationists-turned-IDists will go to in order to hang on to uniform probability.
Do you believe that complete absence of knowledge of a process with a finite number of outcomes compels you to regard the outcomes as equiprobable? Hint: If you know nothing, then how do you justify assigning probabilities to outcomes?
I didn’t dream up that last question. Anyone who does a bit of reading in the philosophy of probability will come across it. One of the funny things about following Dembski and Marks closely over the past decade is that, doing the study necessary to properly evaluate their claims, I’ve learned that they have not done nearly as much study as I have. In all honesty, Mung, I have gone from the presumption that they knew more than I did about the subject matter — especially probability and information — to the realization that they’re pretending to be experts on subjects they know poorly.
The line of thinking where you can derive other numbers from zero is parallel to that creatio ex nihilo makes sense. Christians like it this way. I personally prefer ex nihilo nihil fit.
Either way, none of this is from the Devil. Why do you think it is?
Exactly my response. I made an honest effort to figure out what he might be driving at, and came up with nothing.