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
Only true believers are lunatics. oh. wait.
The Losos book argues otherwise. Did I already say that?
That would certainly explain why Joe writes of solutions to problems in a section on maximizing fitness or fitness optimization in his book on population genetics.
The evidence is pretty clear that you have in fact so argued. You even have models.
Do you still say you don’t think you have? Or has your memory been refreshed.
Good one Mung! Selective memory…depending on what suits the evolutionary paradigm better…
II.8 Selection and Fitness : Multiple Alleles
STABILITY AND MEAN FITNESS.
And yet despite all this dissembling and fakery no alternative seems capable of displacing it. One can only conclude that the alternatives are supported by even more fakery then you claim evolution is. Hardly a comfort to you however.
Joe says Mung quote-mined Joe. That’s all there is to it! If anybody is in a position to state that it’s Joe.
Mung, you probably consider what you are doing to be “work” that supports your position (whatever that actually). What can you point to as your most significant victory?
About a month ago, Joe worked out, mathematically, the mean fitness of the population for the Glass model (modified to begin with a uniformly random population) as a function of time. I did some numerical checks. We’re supposed to assemble the results into a post, if ever I manage to finish Evo-Info 4. I want to do an animation similar to Animation 1, but with the frame at time t showing the average population in generation t for a whole bunch of runs. You won’t see the frequency distributions of fitnesses sloshing around, as in Animation 1. The orange distribution will move from left to right, with the movement slowing, the closer the mean fitness comes to the mean fitness at equilibrium. Eventually, the difference of the distribution in the animation from that in Figure 1 will be indiscernible.
Put simply, what I’ve shown you in orange is somewhat similar to what you’ve highlighted in Joe’s text. However, for the modified Glass model (blue), which begins with a maximally fit population, the mean population fitness never increases. What you see in orange is no more fitness maximization than what you see in blue is fitness minimization. Essentially the same thing is occurring in both cases, but with different initial conditions. The effects of several factors on the frequency distribution of fitnesses gradually “balance out.” I considered using the term evolutionary equilibration in the OP. Come to think of it, an earlier title was “Evolutionary Equilibration Is Not Search.”
Mung,
There is absolutely no need for a designer to put anything into or onto an organism for the delight of Man. (Christ, what a gorgeous spliceosome!).
All He would need to do is make Man find everything rilly beautiful, like a student on ‘shrooms.
Some people struggle to get into the mind of a good designer.
keiths,
It’s the arguing against the selection of (rather, against) disadvantageous traits (by one, at least) that I’m boggling at.
Mung,
What do you think about the ptarmigan and mountain hare, then? White in winter, brownish in summer (and the kind of convergent feature that completely destroys common descent, chortle!).
Adaptive, designed, or … something else?
I think (though it’s hard to tell, because all he is doing is excerpting sections of text and bolding them) that Mung may be confusing absolute, relative, population mean and individual fitnesses.
Still, I’m not sure why, on the one hand, he’s getting all moist over words like ‘optimise’ and ‘maximise’ (and frequently seeing the one but reading the other) while at the same time appearing to deny adaptive evolution, or at least, to fight tooth and nail against any example presented.
I see Mung has been trumpeting to the world that I have actually written that (in some cases) mean population fitness is maximized.
We’ve been over all that. Mung quoted a passage from my online book. It was supposed to show that I said that fitness is always increasing in evolution. The statement was about one case, which covers a single haploid locus or asexual clonal inheritance, in an infinite population with constant relative fitnesses. I pointed out that if you read the whole paragraph, which means the next two sentences too, you find me saying that this holds only in one other case.
In the next comment, I’ll try to generalize a bit more about increase of fitness in population genetics models. But for the moment all I need to point out is that the passages from my book that Mung is dramatically unearthing are all from those two models. Which I already said, in the full paragraph, show increase of average fitness.
So do mean fitnesses always increase in population genetics models? No. they don’t. In Chapter II of my book, I am introducing natural selection, using models with infinite populations. I discuss absolute fitness and relative fitness, then show the math for models with relative fitnesses constant through time. For one-locus models (and also clonal reproduction models) mean fitness does not decrease, so one ends up at a local maximum of the fitnesses.
But as soon as we let relative fitnesses vary through time, or be dependent on gene frequency, or be dependent on population density, there is no guarantee that mean fitness cannot decrease. I present the models for those cases in the rest of Chapter II.
And that is all for infinite single populations, with no mutation, no migration, no genetic drift. In Chapter III I cover mutation, and models of balance between mutation and selection. Mutation keeps the population from getting all the way to optimum fitness. I even discuss “mutational load” and its negative effect on fitness. In Chapter IV, migration. An excursion to the theory of inbreeding in Chapter V sets us up for the effects of genetic drift in chapters VI and VII. Genetic drift can cause a population to wander down off a peak in the fitness surface (though selection acts as if trying to pull it back up).
Later chapter VIII covers what happens with multiple loci that can recombine. There it can be shown that mean fitness need not increase.
So have I gone around saying that mean fitnesses always increase? Or that they always reach the highest possible fitness? Or that the population comes to consist on only the most fit genotypes? Of course not. The quotes ripped from context are generalizations about particular cases, not general rules.
The OP (remember that?) behaves as we expect from population genetics. Will explain in my next comment.
Tom English’s very good and effective examples in the OP (the original post of this thread) show a case suggested by David Glass. It has selection, mutation, and genetic drift. Selection is effective, as it often is. Remember that this is a Weasel-like model where mutation from higher to lower fitness is 26 times more frequent than mutation from lower to higher fitness. So if we just let mutation occur and have no selection, the distribution will stay quite far over near the left end of the axis.
Selection has a very dramatic effect when present. But mutation keeps it from getting all the way to the right-hand end of the axis. Fitness does not reach its optimum value. In fact if we start with a distribution near the right-hand end, it evolves downward in average fitness, owing to mutation.
All this is a very clear example, showing that fitness has a big effect, but in the presence of mutation and of genetic drift, does not inevitably lead to an optimum phenotype. And Tom’s distributions can be closely predicted using population genetics theory.
I don’t deny “adaptive evolution.” Whatever one may mean by “adaptive evolution” in that context. And I don’t argue against purported examples because I deny adaptive evolution. Man, I wish you guys could grasp subtle distinctions, lol.
What I argue against is story-telling. I don’t want my science books to read like fairy tales. What I argue against is assuming one’s conclusions. What I argue against is substituting imagination for science.
It’s not good enough to simply imagine why a white color might be adaptive. I can imagine all sorts of things might be adaptive, but that don’t make it so. Do polar bears have black noses? Must be an adaptive reason for it. Now if I can only think up a reason why a black nose might be adaptive.
Seals also have black noses. So black noses are obviously adaptive in the arctic. A black nose on a polar bear might cause a seal to be confused for just a moment as to whether it’s really not just another seal and that tiny bit could give a polar bear with a black nose a slight reproductive advantage over bears without a black nose. And that’s why polar bears have black noses.
It’s imagination and story-telling. But if that’s what evolutionary theory consists of, then so be it.
This is a straw man Joe. Can we focus on the models in which mean fitness does always increase (at least until it stops increasing)?
And why are you still writing about maximizing fitnesses when the point of the OP is that evolution doesn’t do that? You shouldn’t even be talking about it if it doesn’t ever happen.
And I never claimed that you were saying that mean fitnesses always increase.
And I never claimed that you were saying that they always reach the highest possible fitness.
And I never claimed that you were saying that the population always comes to consist on only the most fit genotypes.
These are manufactured controversies. And if you you can find where I did ever say such a thing I will apologize and retract it and buy you a nice drink. [Contingent on my not being able to demonstrate you actually saying one of those things, of course.]
Do you see the word always in there Joe? Anyone?
It’s the particular cases that matter, can we focus on that?
Mung,
OK in religious texts though?
We’re not talking of their noses.
I don’t know how one can look at the white colouration of many high-latitude and high-altitude organisms – in particular, the change in white from winter to summer in ptarmigan, stoat, mountain hare and even Arctic hare in certain latitudes, and not conclude that there is a very likely set of instances of adaptation associated with that colouration. It should not be necessary to present you with the statistics on relative mortality before you accepted it (and, having some experience in this area, statistics on relative mortality would not be enough either. And we’d have to repeat the exercise for every feature of every organism. A fun project for someone).
If your alternative is Design (you’re against story-telling?), you’d better make it a good one. I’ll pull up a chair.
Mung,
Evolution by selection – specifically that a feature is present because, historically, greater mortality or reduced fecundity was experienced by non-carriers than carriers of that trait. As I’m sure you know.
And you admit that in some cases this is true. Do we agree on that?
This is false. This is keiths-level mind-reading in play. Can we stick with what I actually wrote rather than what people might imagine what I might have written? Is that too much to ask?
I demonstrated exactly what I claimed I could demonstrate. That you had in fact argued that evolution maximizes fitness. All that matters is that one case in which you did so argue. And you admit to that.
How big an advance did you get for Felsenstein on Trial?
You don’t find me making up design stories, at least not in any serious way. I leave the story-telling to the experts. 🙂
Have you simply failed to notice the constant goading by OMagain trying to get me to make up design stories and my steadfast refusal to do so?
He accused me of quote-mining him. I ought to be given the right to cross-examine. I’m betting he’s more of a gentleman than keiths.
Obviously! Religious texts are not intended to be science texts. They may contain, for example, stories that demonstrate morals. What is the moral of the stories that evolutionists tell? That there is no God, no purpose, etc. etc.?
I think correlation is not causation. You know, I have a book around here somewhere, lol.
Winter World: The Ingenuity of Animal Survival
Not directly related in that these aren’t the same species of bears, and they don’t hunt in the way polar bears do, but black bears like fishing in streams. Among black bears, there’s a relatively rare mutation that makes them white, and these white bears are called Kermode bears.
“Scientists have found that black bears are not as effective at catching fish as white bears, as the white bears are less visible from the perspective of the fish. At night, the two colours of bears have similar success rates at catching fish, such as salmon, but during the day, the white bears are 30 percent more effective.”
30% more fish is a lot for such a simple, single change.
It is of course hard to estimate how much that fish catching increase translates into increased reproductive success. But if the frequency of the white allele in the population is known, and it confers as little as 1% increased reproductive success on average, together with the effective population size (polar bears are around 30.000?), it’s probability of fixation can be calculated. The ancestral sub-population that split off from brown bears, and from which polar bears evolved might have been even smaller still as they became geographically isolated.
I believe Joe has shown before that it takes very little in terms of increased reproductive success for the fixation of an allele to be virtually guaranteed under certain conditions.
Those starving polar bears hunting in the arctic sea ice that I linked some videos to earlier, at their already low chance of successful hunting (an already low rate of 1 in 20 attempts) can mean the difference between starving to death, and surviving long enough to make it through another season and get to mate.
Mung,
If I were you, I’d worry…
Tom and Joe may have some quantitative, experimental evidence from cohort studies, proving their fitness claims are more than just mathematical illusions…that their observations that are not reality itself, and their mathematical expressions of their observations are not reality itself….
They might have been withholding it until now..though we already know it has nothing to do with non-random mutations via quantum coherence evidence kind of thing… 😉
You’re being asked to explain the correlation, not handwave in the direction of a book you haven’t even read.
LOL
Mung, if a mutation is deleterious, that means the allele without the mutation is beneficial compared to the deleterious one.
Not exclusively, no.
But, it’s pretty obvious that not all human behaviors are adaptive. A pretty big difference.
We have a lot of basic adaptive instincts, but we also have reason, and our adaptive behaviors can misfire in maladaptive ways.
No, it actually doesn’t. It is entirely plausible given what we know about the potential effectiveness of natural selection.
What do you even mean by that? Your question makes zero logical sense in the context of our discussion.
You speak in vague generalities, and you handwave in the direction of mere logical concievabilities. Pretty much all the time.
Mung,
So the parallel standard we need to adopt is simply to say ‘Evolution’? Or, more parallel still, ‘Not Design?
Anyway, you haven’t really answered about the stoats, ptarmigans and hares. Are those not reasonable examples of adaptation? If not, why not, and what would you expect to be provided to make them so?
Mung,
Mung has a book. Absolutely no answer.
Allan: OK [stamps boots, shakes out hat, chips icicles off beard, chucks sack of empty paint spray cans to floor] I have returned from an intensive 20 year study in Arctic conditions. I have discovered that white varieties have 1000 births for every 999 non-white. By mathematical analysis, one can show that this is a significant selection coefficient, enough to lead to selective fixation in the general case.
Mung, smirking: “correlation is not causation”.
Exit, pursued by a bear.
Allan:
Mung has lots of books. Storing knowledge externally allows him to keep his head empty.
J-Mac seems to be into that. But what about gravitational coherence or electrostatic coherence? The gravitational effect of, say, my typing on this keyboard go all the way out to the end of the universe. That could be the basis of non-random mutations couldn’t it? Just as plausibly as quantum entangling. Gravitational entangling.
Mung has books seemingly entirely for the purpose of seaching for things said in one book, which in isolation or maliciously interpreted, can me bade to appear as if in contradiction to something said in another book.
He doesn’t have them to read them and understand them, or to think about about it because the contents are interesting or intellectually stimulating. To him, they’re just tools to be (ab)used for trolling and rhetorical purposes.
Some times he doesn’t even have the books. He has links to them on amazon, so he can handwave in the direction of their titles and backside blurps.
So in one or two cases in my book it can be established that mean fitness of the population continually increases. Therefore I am supposed to have argued that “evolution” maximizes fitness. Generally?
In the case of helium balloons, when released in the air they fall up. I admit and confess that this is true. Therefore I have argued that things fall up?
Oh, okay. Cool.
Then have fun arguing with phoodoo, because he explicitly denies adaptive evolution, of all kinds. There is no such thing as fitness, no such thing as relative reproductive success, and alleles have no meaingful or measurable contribution to the survival and reproductive opportunities of an organism.
So, since we can’t seem to explain it to him, can you? Can you explain adaptive evolution to phoodoo so he can get it?
That really seems to be one of Mungs chief methods of argumentation. While arguing some contentious issue, in so far as you state a general rule, and Mung can find an exception, then Mung will produce the contradictory quote and say something along the lines of “X does Y, until it doesn’t.”
And then he’ll pretend he has significantly undermined the general case. As if it is now 50/50, or worse, we can’t even say anything about the relative contributions, or frequencies of the different options. In so far as there is more than one option, Mung gives up and figures one couldn’t possibly make substantive advances in understanding.
So if you say natural selection is a fitness maximizer, Mung will implicitly take that to mean that you have said natural selection always and exclusively succeeds in finding the highest possible fitness. He won’t say that he took you to mean that, instead he’ll go with that understanding in mind, seeking for a quote to contradict what he implicitly took you to mean, and then pretend he’s now utterly undermined the general case with his newfound quote.
He will then produce the quote and say “Evolution maximizes fitness, until it doesn’t” and declare victory. And he’ll think he’s shown you‘ve said something stupid.
You will see this and then point out to him that you never claimed natural selection always succeeds at maximizing fitness, but because Mung didn’t explicitly assert that you did, he will act all indignant that you would accuse him of such when he so clearly never did.
That’s Mung. That’s basically all he ever does. On all subjects ever.
They use the orchard model in the ID/YEC/Too embarrassed to say tent. Everybody faces forwards and like horses their ideological blinkers don’t allow them to perceive their neighbours position is inconsistent with theirs. There’s no cross-pollination. As long as they all look forwards at the altar of Darwin all is well. As long as their pea shooters are all aimed at the same place they are all friends.
Otherwise they’d notice their positions have even less in common with each other then their common enemy and suddenly it’s the schisms and the real holy wars!
Mung,
“Design” will remain merely inconsistencies in evolutionary biology then, won’t it?
Joe G used resort to the “but what if a meteor hit the most fit gazelle” trope all the time. It’s amusing to see phoodoo and even Mung finally resorting to it too. It seems that at the bottom of the barrel of ID/YEC there is convergent evolution of arguments ;P
Perhaps worth mentioning that, in a population which is not growing, mean fitness cannot be greater than 1. Only in a growing population can it exceed that value. Can we reasonably expect that, in a finite world, all populations would be growing?
So, one might express it as a jibe: “evolution increases fitness except where it doesn’t”. Well, yeah. And?
It’s easy to engage with him. I’ve had him on ignore for some time now, and I’ll still reply to people quoting him or occasionally have a read and reply directly but I’m mostly Mung free these days. And so it’s amusing to see him say that I’m constantly trying to get him to make up design stories. Hardly. You might have to define “constantly” Mung.
Mung,
Here’s something someone quoted you saying that I wanted to comment on. I don’t care what you reply, as I’ve got you on ignore.
Presumably the fact that you concentrate most of your efforts against evolutionary biology and not the tropes regurgitated by the likes of J-Mac and phoodoo means that you feel it has some legitimacy not shared by the “positions” of the YEC/ID crowd. Otherwise you’d not be wasting your time, right? No point in disconfirming the unconfirmed, right?
But it’s amusing to see that they think you are on their side, sometimes. Bless them.
Archers hit bullseyes until they don’t. So nobody can win and nobody can be better than another. I guess.
That’s the Mung-Phoodoo theorem of competition and conflict. It only some times happens, so it can’t possibly mean anything.
It’s another exemplification of the black-or-white thinking of creationists. This thing about gradations, about nuance, about small changes in relative frequencies over time. It is entirely alien and incomprehensible to them.
If it’s not 100% vs 0%, then it is intellectually impenetrable and mysterious. And nobody could ever make sense of it. Or find systematic biases or patterns of change. Nope, can’t happen. It’s all or nothing.