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
Mung,
If, in a single generation, frequencies change, then it is inevitable that the next generation will be distorted by that change in frequency. So, you are affecting offspring numbers. The allele that experiences excess death gets into fewer offspring.
Of course there is more than one way of measuring it. Have I ever said otherwise? I have been defending this one – though it happens to equate to Losos’s, as far as I can see. You are mistaking the ‘offspring count’ idea for a view of fitness that concerns itself solely with fecundity.
Futuyma covers this; look it up.
FFS Allan.
Read the Losos quote again. They were counting existence. Some died and no longer existed. Some did not die and had continued to exist. That’s how they were able to find them and count them and measure them, etc. Because they still existed.
The fact of their continued existence, the fact that they survived, meant they had adapted and were evolutionarily “fit.”
phoodoo,
Do you think that an allele that makes its bearers lame will spread through a population?
Unless a hurricane comes along and wipes them all out. Read the Losos book. And they didn’t know that the changes they observed had any genetic basis. Read the Losos book.
There is no such thing as The Ideal Evolutionary Theory.
Is that the totality of its morphology?
You didn’t mention how smart or dumb the bearer was. Or how hairless.
Mung,
Doesn’t matter; ‘exists’ is not a measure of fitness. Do all existent individuals have the same fitness?
Is this a trick question? What is the initial frequency? Drift alone could cause it to spread through a population. Is it linked to some other trait or traits?
phoodoo,
Sigh. Yes, it’s an organism made solely of feet.
Unless those are linked characters, it doesn’t matter. Recombination separates out alleles from their present genetic background through the generations.
Mung,
No. I’m trying to see if phoodoo recognises any role for natural selection at all
1
Unlikely, though possible. I chose something likely to be detrimental though.
Try with and without, if you think it makes a significant difference. Whichever way you go, do you think all alleles have that characteristic?
If you are using survival as the metric of evolutionary fitness, as Losos did, I’d say the answer is yes.
Mung,
So you think we should apply that as a principle? Evolution can’t happen because there’s always something?
In which case they would not cause evolution.
The view of fitness I have defended here is not the whole of evolutionary theory, nor have I tried to portray it as such. Your approach to this particular view of fitness is to say ‘but there are others’. Great help, thanks.
What if the bearer of the lameness was really smart? Then he would reproduce because mates would be attracted to his clever wit. Evolution would keep selecting for really smart lame people. They would become more lame with each generation and also more smart. Eventually they would become brilliant, but unable to look for food.
Then evolution would start to chose those that are less lame, instead of choosing smart ones. It would keep choosing ones that walk better over the smart ones, because the smart ones are no longer preferred because food is hard to get if you can’t walk even if you are brilliant. So we need to be faster to catch food. And faster. And faster! Don’t choose for smart, forget that.
But gosh all that fur is making the fast ones too hot. We need to choose those with less fur, even if they are a little slower. And even less fur would be even better! And less fur! But now we are stupid. We better start choosing for smart again! Who cares how fast you can run.
But wouldn’t it be good to have huge fist for fighting? Yea, but they slow you down when you are running. I don’t give a shit, I want to fight, choose for fighting. Dammit, what happened to smart-can we please stay focused.
Argh! I’m podophobic! Halp!
Mung,
So they are all equally fit? That sounds like a particularly deterministic model of selection, there.
phoodoo,
Yet another lengthy phoodoo rant I didn’t bother reading past the first paragraph. Unless the characters you froth up are linked, they are irrelevant. Recombination removes traits from any given genetic background, over the generations.
Allan Miller,
How is recombination going to help us determine which path towards greatness we are on? If we are on the path to great running, but take a turn onto the path for smart, and then another turn onto the path for less furry, can we just jump back to the middle of the path for running by recombination? How is that going to work?
If we keep changing paths, each characteristic is still going to stay as good or maybe even get better? Why? So its a package deal then?
If so, then we better forget about talking about alleles.
Thank God for recombination. We couldn’t survive without it!
That’s right. Individuals are unimportant in evolutionary theory. They are just robot machines for cranking out more alleles.
This has to be one of the best books I’ve ever come across:
Gene Avatars: The Neo-Darwinian Theory of Evolution
So the individual was the target of selection. But now the individual is not the target of selection. The individual was the focus of evolution. But now the individual is not the focus of evolution.
And people wonder why I think evolutionary theory is incoherent.
Tom, will you be posting your code anywhere? GitHub?
Allan Miller,
Go back and think about how to build a car Allan. Lets say first you select all of the parts that are the strongest, because we don’t want our new car design to break easily. So we choose heavy over lighter parts. Big over small. Thick over thin. So, we have narrowed it down to huge slabs of tungsten.
Ok, so the car next needs to be fast. Wait, that’s a problem, tungsten is heavy as hell. Too bad, we already selected tungsten, can’t go back now. Just make a big engine, huge. Really huge. Now it can be faster.
Well, we tried the big engine and its still not fast enough, because the big slabs of tungsten are not very aerodynamic. “Look, we have already selected big heavy pieces of tungsten, if you wanted smaller pieces, you should have told me earlier, I already bought these big pieces. We are committed.” “Well, then cut them smaller.” “But then they won’t be very strong, tungsten is very brittle if you cut it small.”
“Look, I don’t care about strong anymore, I need it faster, cut it!”
Ok, now we need it to be fuel efficient. How can it be fuel efficient, we have got this huge engine? A smaller engine would be much better for fuel efficiency. But we have already selected the biggest engine possible, how can I start all over, I have all these huge pistons, and push rods, and a gigantic transmission? Besides tungsten is too heavy to be fuel efficient. “Well, then why didn’t we start with carbon fiber, that’s light right?” “But you said you wanted the strongest.”
“No, what I meant was I wanted the cheapest, that will sell best.” “In that case, I would recommend used broomsticks. And forget the engine, that is not the cheapest way. Broomsticks, very cheap!”
“Well, how safe is it going to be?” “Very very dangerous. If you don’t die from getting run over, the fire afterwards will surely kill you.” Oh great.
“Well, look, how long is this car going to last anyway?” “Couple of weeks I guess, as long as you don’t go over any bumps, then might not make it a day.”
“But I want a car that is durable!” “Well, in that case, concrete would be good. But we have already wasted all this money and time on tungsten, and we threw it away for the broomsticks, so no way to get concrete now, its already built. ”
“Recombination!”
And this little kids, is the story of how the eagle can fly. Time for bed now…
Until tomorrow.
Then whatever exists then is the most fit.
If the evolutionary process always, or almost always, produces the same outcome (e.g., an equilibrium distribution) then it is, by definition, teleological.
We can ask, what directs it to that end, rather than some other. Does your model answer that?
Excellent suggestion! Joe, where’s your model?
Alan Fox, Neil Rickert, and now you. There’s an open thread on the book here at TSZ where anyone who doubts can provide actual evidence that I have misrepresented Losos. So far no one has, and the thread’s been up for almost three weeks.
I think I figured out how Allan wants to measure fitness. What you do is you take each individual trait, and you give it a score.
Like say curly hair you get a 1, straight hair a 3, red hair a -1, bald gene .5, freckles gets -2, tongue curling 0, pointed ears -4, dimple chin 1.3, big lungs 2.1, ring finger longer than index finger 1.6, cancer gene .96…etc
But then you ask, well why does straight hair get a 3? Well, don’t you know how many people in China have straight hair, of course its a higher fitness!
And a cancer gene is higher than curling your tongue? Well, more people have it! Simple. Very simple. Just count. Don’t judge.
No. It’s the story of how creationists insist on misunderstanding evolution.
“But Allan, how do you know that the reason there are more people in China with straight hair is because of fitness?”
‘Well, there are more of them right? That’s how you know!”
Its not a tautology, its just obvious! The winner wins, what’s the problem?
Mung finds a quote from me, in my online text Theoretical Evolutionary Genetics, that has me mindlessly arguing that the most fit genotype always takes over the population. It’s from the section in Chapter II where I discuss fitness maximization. Yes, it did say that. But it said more. It was from a discussion of what happens when we have a haploid locus (with constant relative fitnesses, in an infinitely large population). Or else an infinite population of clonally-reproducing genotypes. After the algebra, in which mean fitness always increases, I say that, and then start to talk about more complex cases.
So here’s a fuller quote, so you can see whether I was that mindless:
I recommend that whole section on selection and mean fitness to get a fuller view. You’ll find that section, II.7. Pages 90-96 are the most relevant.
Mung:
You’re generally a keyword highlighter, not a cherry picker. Your omission of the context that Joe Felsenstein provided is not at all cool. Hopefully you will tell us that you did not understand the context, and thus did not know that including it was essential.
ETA: Theoretical Evolutionary Genetics [link to Joe’s textbook]
phoodoo,
Oh, for goodness’ sake. For one, it’s not a question of how I want to do it, but what fitness typically means in evolutionary biology. For another thing: no. No, you don’t ‘give things a score’. That is not in any way measuring the mean fitness of carriers vs that of non-carriers, when fitness is understood to be the mean number of complete organismal cycles accruing to the individuals in the two groups.
Once again, the thing that would have got this post in Guano is hanging in the air, unsaid. Between the square brackets: […].
phoodoo,
I don’t.
It is not at all safe to conclude simply by counting ‘existence’ that the most frequent allele is the one with the greatest selection coefficient, because there are other causes of change in allele frequency – not least what you refer to as ‘luck’. That’s why one does not just use ‘exists’ as a measure of fitness, pace Mung. [eta – and of course as Rumraket says, there may have been a change in the selective landscape, acting against a common allele or (same thing) in favour of one presently rare.]
Nonetheless, if you want to insist that the only factor is ‘luck’, you deny selection even at the microevolutionary level. So, no antibiotic resistance, no differential in the relative susceptibility of bald and hairy varieties to cold, no adaptations to predation, etc. No link between any allele and its effect on its bearers’ survival/reproduction whatsoever. That is a minority position, even for a Creationist.
Mung,
I don’t wonder why you think it incoherent. You scurry around looking for quotes that appear to contradict statements by others – or even that appear to contradict the author themselves – then play the poor old confused soul that needs to be told exactly what to think, and finds the literature lacking in that regard. “How can I understand evolutionary theory if there’s no Catechism?” Boo and hoo.
This doesn’t make much sense to me. It seems to me you can’t really estimate fitness without considering the factor of time. There could be two competing alleles in the population, and one could be having higher reproductive success than another, even though it (presently) exists at a lower frequency.
You don’t just look at the frequency of alleles at a single instance of time, and then declare that the one with the highest momentary frequency is the most fit. It might have been the most fit in the past, to get to such a high frequency. But the mean reproductive success of carriers could actually be lower than other competing alleles, so it could in principle be “on the way out” so to speak.
Sorry Tom – another derail!
Blue
Sure, all of them.
They also all start suffering from muscle weakness at the age of four. Most are unable to walk by the age of 12. By the age of 40, most of them are dead.
So to repeat: do DMD patients, on average, have fewer children than healthy subjects? You can pick the hair colour of the latter yourself. I like green.
The thread has veered in all possible directions again, but this is precisely what I meant. See, I wasn’t talking about adaptive or neutral evolution. I was continuing the “bad mutation” topic that Allan started, because that seemed like a sensible direction for finding common ground to me. That means we are talking purifying selection now.
Corneel,
I did try to restart that direction with a discussion of a mutation that rendered its bearers lame, but that effort too crashed and burned, because the relation of allele spread to recombination and linkage seems not to have been internalised. The response is always a ‘but what if …’ followed by several paragraphs of excitable drivel.
The fact one can dream up exceptional scenarios – hurricanes, fixation by drift, linkage effects – seems to depend, for its force in dismantling fitness, upon there being nothing but exceptional scenarios!
Not quite right, but you are definitely on to something! Consider this: How many people carry the dystrophin mutation? How many carry the mutations to CFTR that predispose to cystic fibrosis? How many have disease mutations in the Huntingtin gene? They are all rare! As to the “cancer gene”; individual heritable oncogenic mutations are indeed uncommon. The ones that are associated with severe and early-onset types of cancer are generally rare.
Fact: Bad mutations are rare! Why is that?
What’s a bad mutation? You mean deleterious mutations?
Haha, quite right. Only characters in soap operas tend to lead lives that interesting. Still one would think that it should be possible to convince *anyone* that there are some diseases that are so devastating that patients are unlikely to start a happy little family.
Yes
Not at all! Why are the populations in the simulations not reaching maximum attainable fitness, if not because of deleterious mutations?
Corneel,
Indeed – it’s just the labyrinthine burrows one goes down in pursuit of that rabbit!
I should mention that the view I am touting is not my own.
I know Wikipedia is not the pinnacle of trustworthiness, but I had nothing to do with that piece, and it differs little from the treatments on Mung’s groaning shelves.
I’d be interested if phoodoo and/or Mung could suggests edits to extend the concept to add ‘exists’ as a measure of fitness, because it isn’t there at the moment.
Moved two posts to guano
Criticize the post, not the person.
Corneel,
Is straight hair a fit or not fit? How about a widows peak, is that fit or not fit? What about a curved thumb as opposed to a straight thumb, which is more fit?
Is a unibrow less fit than two distinct eyebrows?
Because in your discussion about DMD I didn’t see you mention which of these genotypes the DMD individuals had. Is it not important? Do we look at every allele type and analysis its fitness? Why not? Why only choose DMD? maybe most of the DMD individuals also have unibrows, and so THIS is why they mate less. What shape ears do they have, could that be a factor in how many offspring they reproduce?
Why play favorites?
So then, is straight hair a sign of fitness?
Because the effect of having Duchenne Muscle Dystrophy is so devastating that it dwarfs the effect of any other genetic variant. That is why I chose this example.
You can safely ignore unibrows, trust me.
So … when Creationists argue that ‘all mutations are bad’, what are they doing that differs from the ‘neo-Classical’ view of an allele’s effects affecting its spread?
Yes, that’s all well and good if we want to use common sense as a measure of fitness.
But common sense isn’t part of Allan’s definition of fitness. If it was, then we might be able to rightly claim that Cristiano Ronaldo if of course more fit than Stephen Hawking. But Allan’s definition forces us to say the opposite-Hawking is much more fit.
So whose view do we take, what thinking people know about fitness, or do we go with what Allan says all evolutionary biologist understand?