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
(I’ve quoted context that Mung did not.)
It’s not my thinking. It’s an explanation of how we model search in the NFL analytic framework, expressed at high enough a level that it covers also the analytic framework of Dembski, Ewert, and Marks (2013), “A General Theory of Information Cost Incurred by Successful Search.” DEM give five components, but you can lump them into two. (Mind you, Dembski has simplified when it has suited his purposes, as in his University of Chicago seminar talk. He told me in email, at the end of 2014, that he intended to stick with the framework of that paper.)
This is not about some metaphysical question like “What is search, really?” If you cannot decompose (logically, at least) a process into a sampling component and a solution-seeking component, then the NFL analysis and the DEM analysis certainly do not apply. That decomposition is necessary, not sufficient. The two frameworks apply under similar, but different circumstances.
In short, the question is not “What is search?” The question is “Is the definition of search in our analysis satisfied?”
Pretty sure natural selection is part of the sampling process, it biases the sampling process
Late to the party due to RL. Thanks to Tom for this excellent post which is now featured
They are!
No question, there are many things I fail to understand. But I do think the changes that occur in the niche, whilst themselves unpredictable (as for example the weather is unpredictable except statistically and in the short term) do bias selection in the gene pool of organisms occupying that niche
All praise mung.
A lot of the error of Marks et al. amounts to mistaking the simulator for the simuland.
They see a fitness function in the simulation of the evolutionary process. The simulator passes a description of an individual “into” the fitness function, and gets the fitness of the individual “out of” the fitness function.
So it must be that the fitness is information guiding the evolutionary process — right? It was Rumraket, IIRC, who recently made the apt observation that fitness is a description, not a prescription. As I wrote in the last paragraph of the OP:
No one paying attention primarily to biological models would be the least confused about this. Marks et al. have mistaken the program implementing the model for the modeled process.
It’s also Mung’s favorite equivocation.
Creationist brains seem wired to ignore this distinction. They think that any alternative to what they believe designed life must have the same prescriptive capabilities. It must be able to do “God’s job”, and of course, nothing can be up to that task. Some massive question begging right there
I’m not ignoring the earlier comments. I’m trying to assemble them into a whole, and respond to the whole instead of the pieces.
I’m not nearly as quick as some of you are. I generally have to think a lot to figure out what it is that I really want to say.
There’s no argument to be had on this point. Dembski and Marks spelled it out in the introduction to “Life’s Conservation Law: Why Darwinian Evolution Cannot Create Biological Information.”
If “Darwinian evolution” was inherently teleological, if it required an external source of “information” and if God was that source of information, then creationists who buy that should stop whining about Darwinian evolution and stop claiming that it’s not capable of this or that. That’s blasphemous.
The logical thing to do would be to become theistic evolutionists, but creationists are known for not having a knack for logic
I should mention that they’ve silently abandoned that claim (which was alive and well in August-September 2014 — see Dembski’s seminar talk at the University of Chicago, and his book Being as Communion: A Metaphysics of Information). They’ve also silently abandoned the Law of Conservation of [Active] Information — putatively a law of nature — of that paper (in which they silently abandoned Dembski’s Law of Conservation of [Complex Specified] Information). Now LCI is a principle that applies to computing.
Then Mung is logical. He simply doesn’t care for the company of his own kind.
Great, lets just get more evolutionists to admit this, then problem solved.
phoodoo,
But then we have to remove a lot of Darwin statues!
You know that was not an “evolutionist’s” quote, right?
Perhaps I’m getting ahead of Tom’s story here. I just want to ask why it matters whether evolution is (or models of evolution are) “evolutionary search”. One possible reason is that then Dembski, Ewert, and Marks’s theorem would apply. In their DEM papers, they have a set of possible processes called “evolutionary search”, and they prove that on average over all of them, the outcome is no better than just chosing a single genotype at random from the space of all possible genotypes.
As I’ve repeatedly complained here, that happens only because of all of the ridiculously bad processes that are included in their set of “evolutionary searches”.
What DEM do not show, is that all evolutionary processes must do this badly. If they could show that, they would have an impossibility theorem for models of evolution, and by extension for evolution itself. Then it would become a matter of great importance to know whether evolutionary models fall into the category of “evolutionary searches”.
But they don’t have any such impossibility theorem. Some (I would say basically all) reasonable models of evolution do a lot better than average. Once one has fitnesses and those affect the changes of genotype frequencies, that is enough to do a lot better than DEM’s average behavior.
So even if it were somehow established that models of evolution are “evolutionary searches” … so what?
My understanding of their argument is that most of the possible searches don’t work, therefore Jesus.
Fine Tuning restated in the language of bullshit mathematics.
Any takers?
They don’t need to do that. They aren’t trying to prove that evolution is impossible.
They are trying to establish that, without an outside source of information, natural selection cannot build “active information” into the genome. To do this they use their result that on average, the probability of reaching a “target” is no greater using natural selection than it is doing a simple single random sample from the space of all possible genotypes.
I am arguing that their averaging over all “evolutionary searches” does not establish that actual evolutionary processes would do this badly,
If Mung thinks that they are trying to prove something else, perhaps Mung could say what that is.
Actual evolutionary processes like random mutation, random genetic drift, and random environmental changes.
It really takes a genius to strip evolution from it’s lawful components, leaving only the random ones, and claim that it can’t do any better than a random search.
Oh, wait, this sounds familiar… Mungy is back to square one. Oh well
Umm… Because search has a purpose, and Darwinian evolution does not? Seems like a very big deal to me. Marks et al. are now referring to “putative models,” meaning that evolutionary models (excluding open-ended evolution in Tierra, which they dismiss ad hoc) are actually not models of “undirected Darwinism,” but instead searches for solutions to problems “specified in advance” (of what?). Then it is “obvious” that “search” requires information to “find what it’s looking for.” Evolutionary informatics is founded on this false intuition.
You’re big into mathematical models. Perhaps it seems to you that it doesn’t matter what terms we use, as long as the math says what it says. But I assure you that in federal court, and also in the court of public opinion, it matters hugely whether or not you agree to use the term search in reference to “any process that can be represented as a probability distribution” (quoting Ewert from memory).
Part of the problem here is the reduction of “evolutionary search for a solution to a given problem” to “evolutionary search.” When I ask whether a scientist is given a problem — a specification of a finite space
of possible solutions, along with a specification of the solution set 
— and devises a computational model (or instance) of evolution in order to generate a solution, treating possible solutions as organisms with fitnesses, the question clearly matters.
By the way, there is scads of stuff I might have quoted from the book. I put quite a bit of thought into the brief selection at the end of the OP. I believe that I succeeded in highlighting the most important issues.
… and natural selection. Forgot that one, did you?
“Conservation of information in the search for a search” is just an argument from improbability, dressed in a cheap tuxedo. (Like, wow, expressing improbability on a logarithmic scale makes everything different.)
1. Dawkins responded to the most common form of the argument from improbability, often called Hoyle’s fallacy, with cumulative selection. His full-blown computational example is Biomorphs Land. But the ID movement suppresses references to Biomorphs Land, and makes the monkey/Shakespeare model out to be his big illustration. Why? Dawkins eliminated only one of the two main elements of Hoyle’s fallacy (single-step selection) in the Weasel program, leaving them a target to work with.
2. Dembski responds to cumulative selection with “Oh, yeah!? Then where’d the process of cumulative selection come from?” Of course, he means a process of cumulative selection that hits a target specified independently of the process. He shifts the single-step selection of Hoyle’s fallacy into the space of “searches.” Then the “higher-order target” is the set of all “searches” that “hit the target” with some minimum probability. And the uniform probability of the higher-order target, in the space of “searches,” is much less than the uniform probability of the target in the space on which the “searches” are defined.
What was, is, and always will be the crucial issue is the “target.” A “process that can be represented as a probability distribution” is obviously not a search, in and of itself. The search is actually in the selection of process to generate an element of a target that is given, i.e., specified independently of the process. In the OP, I made an informed selection of an evolutionary process. I had a “goal in mind” (see the quote at the end of the OP). But this does not mean that the process itself has a goal in mind.
ID without a target is spelled SOL.
More evo-speak. What natural selection ACTUALLY is just a concept right? Its a thought, just like more is a thought and less, and big, etc.. It is a thought which refers to the idea that some things reproduce more, some things reproduce less. That’s not a system right? That’s not a process in any normal sense of the word.
Its like saying, some people are tall, some people are short. That’s not a process is it? Some trees are wide, some are tall and narrow. Again, not a process, not a system, rather its an observation.
16 year olds who just have gotten their drivers license, often have accidents. Not a process, just an observation.
Stock markets often go down during natural disasters. Its been observed. They don’t have to.
Observations. Not processes. Not a force. Not a mechanism.
And “evolutionary models” without targets fail spectacularly to convince anyone that evolution can be a designer substitute. Evolution without search doesn’t create anything, as your OP so clearly demonstrates.
I forgot it just like Tom forgot it in his model of evolution.
And of course, some dogs breed with other dogs. The fact that careful selective breeding produces wildly different sorts of dogs doesn’t mean selective breeding is a process, right? I mean, those who have written whole books on it were completely missing the fact that they had nothing to say, right? Nothing systemic about it at all. Right? And it’s the same thing with, say, cows and sheep, and corn and wheat, right? No process there at all.
The observation that selective breeding WORKS, which people had understood for millennia, was what triggered the idea of natural selection — it people can do it and it works, what if nature does it also? We know the process works.
But if the “target” is “whatever survives and reproduces a little better”, then evolution is a dandy designer and creator. Which has convinced every serious scientist who has ever studied this matter, tens of thousands of them. But other than those, and every layman who reads about it, and anyone who thinks logically…in fact, everyone but creationists are convinced. And that’s because evidence simply does not matter to creationists. If evidence refutes belief, then the evidence must be wrong.
MDE’s theorems are aimed at proving that the processes (or forces, whatever) of the conventional theory of evolution won’t be effective in improving adaptation, i.e. fitness. So whether or not the result strikes us as “creating anything”, their theorems are refuted if fitness on average increases.
Which it does in lots of reasonable models of evolutionary processes, though not in MDE’s weird gemisch of “evolutionary searches”.
So the Discovery Institute has rendered you oblivious to Dawkins’s main example, Biomorphs.
Seriously, Mung. I was not bullshitting when I said that Dawkins eliminated only one of two components of Hoyle’s Fallacy in the monkey/Shakespeare model. He replaced single-step selection with cumulative selection, but retained the target. The fact of the matter is that ID, in 30 years, has never managed a response to what Dawkins actually emphasized — evolution without a target.
You’ve actually swallowed a bullshit story that Darwinists are so-o-o irrational, and don’t even understand that they’re designing their models to hit targets, despite all their talk about a blind watchmaker. I mean, you’re an intelligent guy. And it makes this willful credulity of yours all the more distressing.
I am just as incredulous about his Biomorphs. But people don’t trot it out all the time here as an example of blind, mindless evolution performing creative acts that rival the acts of a deity.
And I find it deeply distressing that you think that his WEASEL in any way answered “Hoyle’s Fallacy.”
Fitness on average increases until it doesn’t. Didn’t you learn anything from the OP?
What’s the most impressive thing about evolution that we can learn from Tom’s model of evolution?
The most impressive thing I can learn from Mung’s comments is that Mung has no cogent argument supporting Marks-Dembski-Ewert’s assertions. That Mung cannot support the assertion that evolutionary models do no better than choosing a single genotype at random.
In Tom’s model they do a lot better than that, so Mung could start there.
I’m going to channel Tom here and say it’s the programmer that is doing the choosing.
Tom,
Joe appears to believe that fitness is always increasing and that your model somehow supports this idea. Is that what your model shows?
My post is largely an application what I learned from Joe. I did not understand what accounted for the curves in Figure 2 until I engaged actively in the discussion of his post (and went way far out of my way to supply you with Python code, you may recall, here and here). I’ve done a much better job of accounting for Glass’s results than Glass did. (He was happy to see irregularity in his results — his Figure 3 — and thus did not work as I did to make sense of things.)
The first sentence in the quotation of the OP is not my personal judgment. I got it from Joe Felsenstein, and perhaps should have attributed it to him. The qualitative behavior I’m showing you is standard fare in population genetics. The Glass model is not standard. But it works fine, for what I want to show you. And I thought it made the demonstration more interesting, to work with a model that an ardent critic of the New Atheism used in a paper critical of Dawkins.
In short, I don’t owe everything I know to Joe Felsenstein — just a whole lot of it.
You have no idea how I despise all of the talk about the programmer. There is not a single instance of the word modeler in the book. Models come from programmers. See a thing or two or three wrong with that?
Marks et al. seem genuinely to believe that an analysis of a program implementing a model is an analysis of the modeled process. That is multifariously fallacious: the simulator is not the simuland. But it plays mighty well with the circle-jerking techie fans of ID — the guys who love to tell each other stories of how their nuts-and-bolts competencies in computing imbue them with greater insight into evolutionary theory than evolutionary biologists have.
Of course I do not believe that fitness is always increasing. Tom’s simulations show that this is not true. But they do also show that fitnesses (in his 50-locus model) tend to a distribution whose mean is quite substantially above the mean you get from drawing a genotype at random.
MDE claim that if you have a space of genotypes with fitnesses, on average over all possible “evolutionary searches” you get a result that is no better than a random genotype. Tom’s simulation shows that this is not true for that model, and we have also argued that it is not true for essentially any reasonable model of evolutionary processes.
Mung has no answer to that except to suggest that the programmer is supplying information. The program is a simulation of a situation where there are genotypes that have different fitnesses (as genotypes are wont to do) with some mutation back and forth between alleles. A population that is doing that is not having information input from outside.
Simulations of molecular collisions in Brownian Motion, or of rocks falling down a hill (landslides), or erosion carving out a river bed are also done by programmers. Does anyone suggest that these processes behave the way they do because of input of information by the programmer? Not anyone with any sense.
Tom, to Mung:
You’ve been overestimating Mung’s intelligence for a long time, Tom. Time to reassess.
Mung has already declared that the mathematics involved is beyond him. But, nonetheless, he still knows it’s wrong.
I find it deeply distressing that you don’t.
more unbelievers:
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003818
HT: Rumraket
How about you create an OP on “Hoyle’s Fallacy” and how THE-DAWKINS-WEASEL eliminated any component of it. Then I’ll happily explain why you are wrong.
That’s a big if.
In some models of evolution, fitness on average increases, until it stops increasing then it no longer on average increases, because else it would be always increasing. So what happens to the average then?
It’s not a big if. Fitness increases on average above pure random sampling. How is that so hard to understand? If I get it, so can you
I sense an opportunity for a bet. Mung and I make a (model) population with many genotypes, and with fitnesses that differ from genotype to genotype. We measure the average fitness in that starting population.
Then we do some generations of reproduction, with those fitnesses affecting probabilities of reproduction and of survival. At the end we again measure the average fitness.
Say, an even-odds bet that the mean fitness will end up higher than it starts out. I’ll bet on that; Mung can take the other side.
You mean you program in which genotypes you will make reproduce more, then see if they reproduce more?
It’s not surprising that creationists start with their conclusions and force the data to fit. And I guess it’s not surprising that creationists assume everyone else does the same, because that’s the only approach they can grasp.
So?