Evo-Info 3: Evolution is not search

Introduction to Evolutionary Informatics, by Robert J. Marks II, the “Charles Darwin of Intelligent Design”; William A. Dembski, the “Isaac Newton of Information Theory”; and Winston Ewert, the “Charles Ingram of Active Information.” World Scientific, 332 pages.
Classification: Engineering mathematics. Engineering analysis. (TA347)
Subjects: Evolutionary computation. Information technology–Mathematics.

Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem. What I hope to convey in this post, primarily by means of graphics, is that fine-tuning a model of evolution, and thereby obtaining an evolutionary process in which a maximally fit individual emerges rapidly, is nothing like informing evolution to search for the best solution to a problem. We consider, specifically, a simulation model presented by Christian apologist David Glass in a paper challenging evolutionary gradualism à la Dawkins. The behavior on exhibit below is qualitatively similar to that of various biological models of evolution.

Animation 1. Parental populations in the first 2000 generations of a run of the Glass model, with parameters (mutation rate .005, population size 500) tuned to speed the first occurrence of maximum fitness (1857 generations, on average), are shown in orange. Offspring are generated in pairs by recombination and mutation of heritable traits of randomly mated parents. The fitness of an individual in the parental population is, loosely, the number of pairs of offspring it is expected to leave. In each generation, the parental population is replaced by surviving offspring. Which of the offspring die is arbitrary. When the model is modified to begin with a maximally fit population, the long-term regime of the resulting process (blue) is the same as for the original process. Rather than seek out maximum fitness, the two evolutionary processes settle into statistical equilibrium.

Figure 1. The two bar charts, orange (Glass model) and blue (modified Glass model), are the mean frequencies of fitnesses in the parental populations of the 998,000 generations following the 2,000 shown in Animation 1. The mean frequency distributions approximate the equilibrium distribution to which the evolutionary processes converge. In both cases, the mean and standard deviation of the fitnesses are 39.5 and 2.84, respectively, and the average frequency of fitness 50 is 0.0034. Maximum fitness occurs in only 1 of 295 generations, on average.

I should explain immediately that an individual organism is characterized by 50 heritable traits. For each trait, there are several variants. Some variants contribute 1 to the average number offspring pairs left by individuals possessing them, and other variants contribute 0. The expected number of offspring pairs, or fitness, for an individual in the parental population is roughly the sum of the 0-1 contributions of its 50 traits. That is, fitness ranges from 0 to 50. It is irrelevant to the model what the traits and their variants actually are. In other words, there is no target type of organism specified independently of the evolutionary process. Note the circularity in saying that evolution searches for heritable traits that contribute to the propensity to leave offspring, whatever those traits might be.

The two evolutionary processes displayed above are identical, apart from their initial populations, and are statistically equivalent over the long term. Thus a general account of what occurs in one of them must apply to both of them. Surely you are not going to tell me that a search for the “target” of maximum fitness, when placed smack dab on the target, rushes away from the target, and subsequently finds it once in a blue moon. Hopefully you will allow that the occurrence of maximum fitness in an evolutionary process is an event of interest to us, not an event that evolution seeks to produce. Again, fitness is not the purpose of evolution, but instead the propensity of a type of organism to leave offspring. So why is it that, when the population is initially full of maximally fit individuals, the population does not stay that way indefinitely? In each generation, the parental population is replaced with surviving offspring, some of which are different in type (heritable traits) from their parents. The variety in offspring is due to recombination and mutation of parental traits. Even as the failure of parents to leave perfect copies of themselves contributes to the decrease of fitness in the blue process, it contributes also to the increase of fitness in the orange process.

Both of the evolutionary processes in Animation 1 settle into statistical equilibrium. That is, the effects of factors like differential reproduction and mutation on the frequencies of fitnesses in the population gradually come into balance. As the number of generations goes to infinity, the average frequencies of fitnesses cease to change (see “Wright, Fisher, and the Weasel,” by Joe Felsenstein). More precisely, the evolutionary processes converge to an equilibrium distribution, shown in Figure 1. This does not mean that the processes enter a state in which the frequencies of fitnesses in the population stay the same from one generation to the next. The equilibrium distribution is the underlying change­less­ness in a ceaselessly changing population. It is what your eyes would make of the flicker if I were to increase the frame rate of the animation, and show you a million generations in a minute.

Animation 2. As the mutation rate increases, the equilibrium distribution shifts from right to left, which is to say that the long-term mean fitness of the parental population decreases. The variance of the fitnesses (spread of the equilibrium distribution) increases until the mean reaches an intermediate value, and then decreases. Note that the fine-tuned mutation rate .005 ≈ 10–2.3 in Figure 1.

Let’s forget about the blue process now, and consider how the orange (randomly initialized) process settles into statistical equilibrium, moving from left to right in Animation 1. The mutation rate determines

  1. the location and the spread of the equilibrium distribution, and also
  2. the speed of convergence to the equilibrium distribution.

Animation 2 makes the first point clear. In visual terms, an effect of increasing the mutation rate is to move equilibrium distribution from right to left, placing it closer to the distribution of the initial population. The second point is intuitive: the closer the equilibrium distribution is to the frequency distribution of the initial population, the faster the evolutionary process “gets there.” Not only does the evolutionary process have “less far to go” to reach equilibrium, when the mutation rate is higher, but the frequency distribution of fitnesses changes faster. Animation 3 allows you to see the differences in rate of convergence to the equilibrium distribution for evolutionary processes with different mutation rates.

Animation 3. Shown are runs of the Glass model with mutation rate we have focused upon, .005, doubled and halved. That is,  = 2 ⨉ .005 = .01 for the blue process, and  = 1/2 ⨉ .005 = .0025 for the orange process.

An increase in mutation rate speeds convergence to the equilibrium distribution, and reduces the mean frequency of maximum fitness.

I have selected a mutation rate that strikes an optimal balance between the time it takes for the evolutionary process to settle into equilibrium, and the time it takes for maximum fitness to occur when the process is at (or near) equilibrium. With the mutation rate set to .005, the average wait for the first occurrence of maximum fitness, in 1001 runs of the Glass model, is 1857 generations. Over the long term, maximum fitness occurs in about 1 of 295 generations. Although it’s not entirely accurate, it’s not too terribly wrong to think in terms of waiting an average of 1562 generations for the evolutionary process to reach equilibrium, and then waiting an average of 295 generations for a maximally fit individual to emerge. Increasing the mutation rate will decrease the first wait, but the decrease will be more than offset by an increase in the second wait.

Figure 2. Regarding Glass’s algorithm (“Parameter Dependence in Cumulative Selection,” Section 3) as a problem solver, the optimal mutation rate is inversely related to the squared string length (compare to his Figure 3). We focus on the case of string length (number of heritable traits) L = 50, population size N = 500, and mutation rate  = .005, with scaled mutation rate uʹ L2 = 12.5 ≈ 23.64. The actual rate of mutation, commonly denoted u, is 26/27 times the rate reported by Glass. Note that each point on a curve corresponds to an evolutionary process. Setting the parameters does not inform the evolutionary search, as Marks et al. would have you believe, but instead defines an evolutionary process.

Figure 2 provides another perspective on the point at which changes in the two waiting times balance. In each curve, going from left to right, the mutation rate is increasing, the mean fitness at equilibrium is decreasing, and the speed of convergence to the equilibrium distribution is increasing. The middle curve (L = 50) in the middle pane (N = 500) corresponds to Animation 2. As we slide down the curve from the left, the equilibrium distribution in the animation moves to the left. The knee of the curve is the point where the increase in speed of convergence no longer offsets the increase in expected wait for maximum fitness to occur when the process is near equilibrium. The equilibrium distribution at that point is the one shown in Figure 1. Continuing along the curve, we now climb steeply. And it’s easy to see why, looking again at Figure 1. A small shift of the equilibrium distribution to the left, corresponding to a slight increase in mutation rate, greatly reduces the (already low) incidence of maximum fitness. This brings us to an important question, which I’m going to punt into the comments section: why would a biologist care about the expected wait for the first appearance of a type of organism that appears rarely?

You will not make sense of what you’ve seen if you cling to the misconception that evolution searches for the “target” of maximally fit organisms, and that I must have informed the search where to look. What I actually did, by fine-tuning the parameters of the Glass model, was to determine the location and the shape of the equilibrium distribution. For the mutation rate that I selected, the long-term average fitness of the population is only 79 percent of the maximum. So I did not inform the evolutionary process to seek out individuals of maximum fitness. I selected a process that settles far away from the maximum, but not too far away to suit my purpose, which is to observe maximum fitness rapidly. If my objective were to observe maximum fitness often, then I would reduce the mutation rate, and expect to wait longer for the evolutionary process to settle into equilibrium. In any case, my purpose for selecting a process is not the purpose of the process itself. All that the evolutionary process “does” is to settle into statistical equilibrium.

Sanity check of some claims in the book

Unfortunately, the most important thing to know about the Glass model is something that cannot be expressed in pictures: fitness has nothing to do with an objective specified independently of the evolutionary process. Which variants of traits contribute 1 to fitness, and which contribute 0, is irrelevant. The fact of the matter is that I ignore traits entirely in my implementation of the model, and keep track of 1s and 0s instead. Yet I have replicated Glass’s results. You cannot argue that I’ve informed the computer to search for a solution to a given problem when the solution simply does not exist within my program.

Let’s quickly test some assertions by Marks et al. (emphasis added by me) against the reality of the Glass model.

There have been numerous models proposed for Darwinian evolution. […] We show repeatedly that the proposed models all require inclusion of significant knowledge about the problem being solved. If a goal of a model is specified in advance, that’s not Darwinian evolution: it’s intelligent design. So ironically, these models of evolution purported to demonstrate Darwinian evolution necessitate an intelligent designer.

Chapter 1, “Introduction”


[T]he fundamentals of evolutionary models offered by Darwinists and those used by engineers and computer scientists are the same. There is always a teleological goal imposed by an omnipotent programmer, a fitness associated with the goal, a source of active information …, and stochastic updates.

Chapter 6, “Analysis of Some Biologically Motivated Evolutionary Models”


Evolution is often modeled by as [sic] a search process. Mutation, survival of the fittest and repopulation are the components of evolutionary search. Evolutionary search computer programs used by computer scientists for design are typically teleological — they have a goal in mind. This is a significant departure from the off-heard [sic] claim that Darwinian evolution has no goal in mind.

Chapter 3, “Design Search in Evolution and the Requirement of Intelligence”

My implementation of the Glass model tracks only fitnesses, not associated traits, so there cannot be a goal or problem specified independently of the evolutionary process.

Evolutionary models to date point strongly to the necessity of design. Indeed, all current models of evolution require information from an external designer in order to work. All current evolutionary models simply do not work without tapping into an external information source.

Preface to Introduction to Evolutionary Informatics


The sources of information in the fundamental Darwinian evolutionary model include (1) a large population of agents, (2) beneficial mutation, (3) survival of the fittest and (4) initialization.

Chapter 5, “Conservation of Information in Computer Search”

The enumerated items are attributes of an evolutionary process. Change the attributes, and you do not inform the process to search, but instead define a different process. Fitness is the probabilistic propensity of a type of organism to leave offspring, not search guidance coming from an “external information source.” The components of evolution in the Glass model are differential reproduction of individuals as a consequence of their differences in heritable traits, variety in the heritable traits of offspring resulting from recombination and mutation of parental traits, and a greater number of offspring than available resources permit to survive and reproduce. That, and nothing you will find in Introduction to Evolutionary Informatics, is a fundamental Darwinian account.

1,439 thoughts on “Evo-Info 3: Evolution is not search

  1. phoodoo: There is nothing special about this post, it has run its course, its not interesting, and it only applies to you, as most others don’t care about it any longer at all. Its that simple. I think the post would have been a lot better if you could have whittled your complaints down to something manageable and coherent enough that it would actually be something that could be discussed. But I am not seeing it, and I don’t think anyone else is.

    Let’s break off a chunk. Here’s the opening paragraph. Please tell me what you don’t understand, having studied assiduously what follows.

    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.

    Emphasis added. I’m curious as to how you can critique my post, and say nothing at all that is related to what I indicated that the post would address.

  2. Tom English: phoodoo: There is nothing special about this post, it has run its course, its not interesting, and it only applies to you, as most others don’t care about it any longer at all.

    Funny how people’s perceptions differ. To me, it is one of the most substantive posts in months.

    As Jules Feiffer had his cartoon character say when doing a book review of The Bible, we look forward to more works by this author.

  3. Joe Felsenstein: Funny how people’s perceptions differ.To me, it is one of the most substantive posts in months.

    As Jules Feiffer had his cartoon character say when doing a book review of The Bible, we look forward to more works by this author.

    Seconded,

  4. Tom English: Emphasis added. I’m curious as to how you can critique my post, and say nothing at all that is related to what I indicated that the post would address

    There you go trying to manipulate Phoodoo into understanding with words again

  5. Mung:

    The second factor that could affect whether populations evolve in parallel concerns whether there are multiple ways to solve a problem posed by the environment.

    – Improbable Destinies p. 241

    Evolution as problem solving. Let’s hope this is just a single isolated case.

    You’ve got your eye on the ball, but you need to work on your follow-through.

    The author is referring to a “problem” identified after the fact of its being “solved” by an evolutionary process. The analysis of Marks, Dembski, and Ewert does not apply. I showed (with animation) in “The Law of Conservation of Information Is Defunct” that when the most probable outcome of a “search” is designated the “target,” active information is not conserved.

    Part of my difficulty in explaining the book is that Marks et al. have abandoned the claim that “Darwinian evolution is inherently teleological,” and have replaced it with a claim that the models are teleological. Now their line is something like “If the modelers can’t get evolution to work without designing their models to search for prespecified targets, then what of nature?” A modeler is not saying, as indicated by the math of Marks et al., that an evolutionary process magically sprang into existence to solve a problem s/he was given. The modeler is saying, “Here are circumstances in which the event tends to occur in an evolutionary process.” The modeler jointly selects the process and the event. Marks et al. are terribly sloppy when they say that the problem is “specified in advance” (I think I quoted that at the end of the OP). They are mistaking specified before the simulation of the process runs for specified in advance of the definition of the evolutionary process. The process and the event of interest are tailored to each other. The modeler is saying that the event occurs only under certain circumstances, not that the circumstances occur because the event was specified as the solution set of a problem.

    What I’ve just said applies even to Dawkins’s monkey/Shakespeare model of cumulative selection. Dawkins is not saying that the (simulated) experimental setup, in which the monkey is supplied with a copy of “METHINKS IT IS LIKE A WEASEL,” sprang magically into existence after he (Dawkins) was given a problem to which the answer was “METHINKS IT IS LIKE A WEASEL.”

    There is no independently specified problem that the monkey/Shakespeare model solves. The event of interest to the modeler is the event most likely to occur in the process over the long term.

    I keep asking you to write a specification of the problem that the modeler wrote the simulation model to solve. The reason you cannot do it is that you can only concoct a “problem” by specifying what tends to occur in the simulated process. This is much the same as making the “problem” solved by evolution in nature what tends to occur in evolutionary processes.

    I’ve tried to make this clear. I’m not in an argument with you. Please try to switch gears, and explain to me what you make of what I’ve said. Do you at least agree that if the modeler is solving a problem, then you ought to be able to write out a nontrivial problem? (Saying that the problem is to output a particular sentence won’t hack it. That would amount to saying that Dawkins wrote a “Hello, world!” program.)

  6. Joe Felsenstein: Funny how people’s perceptions differ. To me, it is one of the most substantive posts in months.

    Really? No kidding. Hm.

    I hear Donald Trump has a whole bunch of books praising Donald Trump.

    Funny.

  7. Tom English: Please tell me what you don’t understand, having studied assiduously what follows.

    I understand, I don’t buy it!

    This:

    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.

    You little card trick is to say, you just look at 1’s and 0’s, not what the 1’s and 0’s refer to and voila!, no telling the computer how to select the fit ones! Hohoho.

    That’s the whole gist of your entirely too long winded post.

    So when DME write:

    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

    They are also correct, you haven’t refuted this is any way shape or form. You did a cheap parlor trick, and you think no one will notice.

    In case people don’t notice the card trick is hidden RIGHT HERE:

    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.

    That’s it, that’s all one needs to know to see the slight of hand. 50 is fit and zero is unfit? Why? There is no why, its simply because you say so.

    Sorry it didn’t fool me. Can you pull a rabbit out of the Cambrian perhaps?

  8. phoodoo,

    In case people don’t notice the card trick is hidden RIGHT HERE:

    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.

    That’s it, that’s all one needs to know to see the slight of hand. 50 is fit and zero is unfit? Why? There is no why, its simply because you say so.

    phoodoo,

    The people who actually understand computer modeling are laughing their asses off at you.

  9. phoodoo,

    Instructing the computer to simulate a process is not instructing the process what to “do.” (Processes occur in nature.) When we describe a process, we commonly do not understand the consequences of the description. We don’t know what will occur in the process, even though we’ve described it. A simulation of the process enables us to learn what occurs in the process.

    We learn a great deal of value from our simulations of weather systems. It would be silly to say that weather forecasts are only what the programmer told the computer to do. A simulation of an evolutionary process is no more a parlor trick than is a simulation of a weather system.

  10. phoodoo: You did a cheap parlor trick, and you think no one will notice.

    I learned it from the Christian apologist David Glass (a theoretical physicist by training, and a very bright man, from what I’ve seen of him). His concerns in “Parameter Dependence in Cumulative Selection” are not at all yours.

  11. Joe Felsenstein: Funny how people’s perceptions differ. To me, it is one of the most substantive posts in months.

    It is funny but perceptions have nothing to do with facts, do they?

    For example: Joe Felseinsein posted his following perception here:

    Joe Felsenstein: The same way quantum mechanics could govern processes such as rocks rolling down a hill.Which it does.

    Unfortunately, Joe’s perception, or the author’s of the OP, doesn’t make it true, or a scientific fact, does it?

    Even children who read the General Relativity for Babies book I recommended earlier, know that gravity governs rock rolling down the hill, and not quantum mechanics…

    https://www.youtube.com/watch?v=ljRlB6TuMOU

  12. J-Mac,

    Even children who read the General Relativity for Babies book I recommended earlier, know that gravity governs rock rolling down the hill, and not quantum mechanics…

    Adults should be prepared to go a little further.

    In the end, there is no classical world; only a many-particle quantum mechanical one that, because of localizations due to environmental interactions, allows the emergence of the classical world of human perception.

  13. Tom English:
    phoodoo,

    Instructing the computer to simulate a process is not instructing the process what to “do.” (Processes occur in nature.) When we describe a process, we commonly do not understand the consequences of the description. We don’t know what will occur in the process, even though we’ve described it. A simulation of the process enables us to learn what occurs in the process.

    We learn a great deal of value from our simulations of weather systems. It would be silly to say that weather forecasts are only what the programmer told the computer to do. A simulation of an evolutionary process is no more a parlor trick than is a simulation of a weather system.

    You (and a whole lot of others) keep going there, with the whole, “If we don’t know the results of the process, we haven’t determined the outcome of the process”, which is just plain wrong. Whether its a weather forcasting program, or a program which selects the best wind turbine or whatever, right, you won’t know which wind turbine the program will choose, or what weather forecast the computer might predict ahead of time, but you most certainly ARE telling the computer how to select the one you want.

    The more you ignore this fact, the more it seems like you are perhaps intentionally obfuscating this. I can understand perfectly that keiths won’t get it, but I believe you will.

  14. Tom English: I had not originally planned to ask that Evo-Info 4 be featured, precisely because it will be mathematical. However, I think now that it is remarkable stuff…

    Not opposed to having posts featured. It’s the missing sell-by date that bothers me. 😉

    I think this thread had gone for three weeks without a comment when I asked it to be removed from it’s featured status. That breathed a new life into the thread, but the current discussion has nothing to do with the OP.

  15. Allan Miller:
    J-Mac,

    Adults should be prepared to go a little further.

    Alternatively, for the case of a rock rolling down a hill, one can simply ask how large the rock has to be for quantum mechanics to suddenly stop acting. Whatever size J-Mac chooses, then just ask whether the left half of that rock has quantum mechanics acting, and also the right half. But somehow not the whole rock?

  16. newton: Except they don’t know which one they want

    They have to be instructed. Do this. Do that. If this, then that. Perhaps there’s a computer out that that knows what to do when it doesn’t know what to do.

  17. Joe Felsenstein: Alternatively, for the case of a rock rolling down a hill, one can simply ask how large the rock has to be for quantum mechanics to suddenly stop acting.Whatever size J-Macchooses, then just ask whether the left half of that rock has quantum mechanics acting, and also the right half.But somehow not the whole rock?

    Congratulation! You just won yourself a Nobel Prize for unifying gravity and quantum mechanics! Don’t forget to prove it first… Lol

    Einstein is turning in his grave though…

    https://en.wikipedia.org/wiki/Quantum_gravity

  18. Mung: They have to be instructed. Do this. Do that. If this, then that. Perhaps there’s a computer out that that knows what to do when it doesn’t know what to do.

    If only Tom could come up with that parlor trick.

  19. J-Mac seems to believe that, if we say that quantum mechanics applies both to big rocks and little rocks, and that gravity also applies to both, that we have somehow unified the physics of quantum mechanics and gravity. I didn’t know it was that easy.

    The only way I can make sense of J-Mac’s position is that J-Mac is saying that at some size of small rock, the physics involved switches from quantum mechanics to gravity. Now that, unlike my position, would be a truly new development in physics.

  20. Joe Felsenstein,

    It seems that, in general, some people struggle with the idea of infinitesimal gradations. It’s got to be A or B. This infuses much thinking on evolution too. I wonder how they get on with calculus?

  21. Joe Felsenstein:
    J-Mac seems to believe that, if we say that quantum mechanics applies both to big rocks and little rocks, and that gravity also applies to both, that we have somehow unified the physics of quantum mechanics and gravity.I didn’t know it was that easy.

    The only way I can make sense of J-Mac’s position is that J-Mac is saying that at some size of small rock, the physics involved switches from quantum mechanics to gravity. Now that, unlike my position, would be a truly new development in physics.

    Quantum mechanics (QM – also known as quantum physics, or quantum theory) is a branch of physics which deals with physical phenomena at microscopic scales, where the action is on the order of the Planck constant. Quantum mechanics departs from classical mechanics primarily at the quantum realm of atomic and subatomic length scales. Quantum mechanics provides a mathematical description of much of the dual particle-like and wave-like behavior and interactions of energy and matter. Quantum mechanics is the non-relativistic limit of Quantum Field Theory (QFT), a theory that was developed later that combined Quantum Mechanics with Relativity.”

    Vs

    Gravity, or gravitation, is a natural phenomenon by which all things with mass are brought toward (or gravitate toward) one another, including planets, stars and galaxies, and other physical objects. Since energy and mass are equivalent, all forms of energy (including light) cause gravitation and are under the influence of it. On Earth, gravity gives weight to physical objects, and causes the ocean tides. The gravitational attraction of the original gaseous matter present in the Universe caused it to begin coalescing, forming stars – and for the stars to group together into galaxies – so gravity is responsible for many of the large scale structures in the Universe. Gravity has an infinite range, although its effects become increasingly weaker on farther objects.

    Gravity is most accurately described by the general theory of relativity (proposed by Albert Einstein in 1915) which describes gravity not as a force, but as a consequence of the curvature of spacetime caused by the uneven distribution of mass. The most extreme example of this curvature of spacetime is a black hole, from which nothing — not even light — can escape once past the black hole’s event horizon.[1] However, for most applications, gravity is well approximated by Newton’s law of universal gravitation, which describes gravity as a force which causes any two bodies to be attracted to each other, with the force proportional to the product of their masses and inversely proportional to the square of the distance between them.”

    What else can I say…???

  22. Joe obviously thinks that there is not discernible difference between microscopic scales where subatomic particles behave like both wave and particle (wave–particle duality) and matter…

    It just hit me; the last time I “saw” rocks rolling down the hill they appeared to behave as a wave or a particle or even both… lol

  23. Joe Felsenstein: Well, take a look at this report in Nature and let us know what you think.

    Or this report.

    Just the result of a quick Google search.

    From Joe’s link:

    “The next step is to try these experiments with atoms of larger mass, superposed over longer time scales and separated by greater distances This will push the envelope of macroscopicity further and reveal yet more about the nature of the relationship between the quantum and the macroworld.

    I guess Joe was talking about not real rocks, rolling down not real hill…

    It must have been an imaginary hill with imaginary rocks rolling down…lol

    Any other delusions you would like to propose?

  24. Mung: That breathed a new life into the thread, but the current discussion has nothing to do with the OP.

    You in fact made a comment highly relevant to the OP. I responded at length, above, and asked for your feedback. You have given me none, and instead tossed off this gem. Phoodoo is also presently on-topic. We’ll see what effect my observation has on him.

    My post is a huge embarrassment for Dembski and Marks. (I doubt that a true believer like Ewert has the capacity for embarrassment.) It’s become obvious that the ID crowd wants the post out of the spotlight. It refutes about as clearly as possible the story of information in “search” that Dembski has been telling, in one form after another (after another…), since No Free Lunch: Why Specified Complexity Cannot Be Purchased without Intelligence (2002). The apologists of ID are really good at word games. But it’s hard to deflect pictures showing clearly that evolutionary processes settle into equilibrium, rather than seek out individuals of maximum fitness.

    Put simply, the games that ID proponents have played recently are evidence that the post is having an impact. I’d otherwise have asked for the post not to be featured anymore. Now I am asking for it to stay featured until Evo-Info 4 comes (tomorrow, I hope — I’ve got quite an excess of writing, and I think I know now which of it I want to use, and how to organize it).

    Let’s return now to the question of whether evolutionary models are evolutionary searches for solutions to problems.

    Mung:

    The second factor that could affect whether populations evolve in parallel concerns whether there are multiple ways to solve a problem posed by the environment.

    – Improbable Destinies p. 241

    Evolution as problem solving. Let’s hope this is just a single isolated case.

    [To repeat myself, verbatim:]

    You’ve got your eye on the ball [meaning: you are on-topic], but you need to work on your follow-through.

    The author is referring to a “problem” identified after the fact of its being “solved” by an evolutionary process. The analysis of Marks, Dembski, and Ewert does not apply. I showed (with animation) in “The Law of Conservation of Information Is Defunct” that when the most probable outcome of a “search” is designated the “target,” active information is not conserved.

    Part of my difficulty in explaining the book is that Marks et al. have abandoned the claim that “Darwinian evolution is inherently teleological,” and have replaced it with a claim that the models are teleological. Now their line is something like “If the modelers can’t get evolution to work without designing their models to search for prespecified targets, then what of nature?” A modeler is not saying, as indicated by the math of Marks et al., that an evolutionary process magically sprang into existence to solve a problem s/he was given. The modeler is saying, “Here are circumstances in which the event tends to occur in an evolutionary process.” The modeler jointly selects the process and the event. Marks et al. are terribly sloppy when they say that the problem is “specified in advance” (I think I quoted that at the end of the OP). They are mistaking specified before the simulation of the process runs for specified in advance of the definition of the evolutionary process. The process and the event of interest are tailored to each other. The modeler is saying that the event occurs only under certain circumstances, not that the circumstances occur because the event was specified as the solution set of a problem.

    What I’ve just said applies even to Dawkins’s monkey/Shakespeare model of cumulative selection. Dawkins is not saying that the (simulated) experimental setup, in which the monkey is supplied with a copy of “METHINKS IT IS LIKE A WEASEL,” sprang magically into existence after he (Dawkins) was given a problem to which the answer was “METHINKS IT IS LIKE A WEASEL.”

    There is no independently specified problem that the monkey/Shakespeare model solves. The event of interest to the modeler is the event most likely to occur in the process over the long term.

    I keep asking you to write a specification of the problem that the modeler wrote the simulation model to solve. The reason you cannot do it is that you can only concoct a “problem” by specifying what tends to occur in the simulated process. This is much the same as making the “problem” solved by evolution in nature what tends to occur in evolutionary processes.

    I’ve tried to make this clear. I’m not in an argument with you. Please try to switch gears, and explain to me what you make of what I’ve said. Do you at least agree that if the modeler is solving a problem, then you ought to be able to write out a nontrivial problem? (Saying that the problem is to output a particular sentence won’t hack it. That would amount to saying that Dawkins wrote a “Hello, world!” program.)

  25. J-Mac: Any other delusions you would like to propose?

    How about the notion that Einstein’s relativistic mechanics holds not only near the speed of light, but everywhere? I would think J-Mac would have a problem with that, too. If so, at what speed does relativistic mechanics suddenly start working?

  26. Tom English: Instructing the computer to simulate a process is not instructing the process what to “do.” (Processes occur in nature.) When we describe a process, we commonly do not understand the consequences of the description. We don’t know what will occur in the process, even though we’ve described it. A simulation of the process enables us to learn what occurs in the process.

    We learn a great deal of value from our simulations of weather systems. It would be silly to say that weather forecasts are only what the programmer told the computer to do. A simulation of an evolutionary process is no more a parlor trick than is a simulation of a weather system.

    Emphasis added.

    phoodoo: You (and a whole lot of others) keep going there, with the whole, “If we don’t know the results of the process, we haven’t determined the outcome of the process”, which is just plain wrong.

    You’re using the word determine equivocally. A deterministic computational process is indeed fully determined by the program and inputs submitted to the computer. If I were to send you a program, and tell you to run it on your computer, then you would understand perfectly well the sense in which you do not determine the outcome of the process. In the latter sense of determine, you knowingly produce an outcome with specified attributes. The epistemological issues are complex, and I do not mean to suggest that I’ve just worked through them. My point is only that determinism is not what is at issue when we consider whether a person determines the outcome of a deterministic simulation of a physical process.

    phoodoo: Whether its a weather forcasting program, or a program which selects the best wind turbine or whatever, right, you won’t know which wind turbine the program will choose, or what weather forecast the computer might predict ahead of time, but you most certainly ARE telling the computer how to select the one you want.

    You’re anthropomorphizing the tool. And you’re smuggling in search. Even in evolutionary search, the role of simulated evolution is to sample the space of possible solutions, not to search. The “select the one you want” component is logically distinct from the (perhaps evolutionary) sampling component. What you’ll see in the OP is most definitely not my telling the computer to “select the one you want.” What I did, in fine tuning the parameters of the simulator, was to cause the fittest of individuals to occur very rarely. The way you’re explaining how the “search” works is fundamentally and plainly wrong. The way that Marks, Dembski, and Ewert explain “evolutionary search” is fundamentally and plainly wrong.

    phoodoo: The more you ignore this fact, the more it seems like you are perhaps intentionally obfuscating this. I can understand perfectly that keiths won’t get it, but I believe you will.

    You haven’t even bothered to read writing of mine I’ve linked to previously. The following is from the preface (“Sampling Bias Is Not Information“) I added to my first (1996) paper on the NFL theorems, correcting my false claim that conservation of information accounts for conservation of performance. (The error was expository, not mathematical.)

    ———————

    0.2 Understanding the Misunderstanding of Information

    The root error is commitment to the belief that information is the cause of performance in black-box optimization (search). The NFL theorems arrived at a time when researchers commonly claimed that evolutionary optimizers gained information about the fitness landscape, and adapted themselves dynamically to improve performance. Wolpert and Macready (1995) observe that superior performance on a subset of functions is offset precisely by inferior performance on the complementary subset. In online discussion of their paper, Bill Spears referred to this as conservation of performance. My paper suggests that conservation of information accounts for conservation of performance.

    The lemma of Sect. 3.1, “Conservation of Information,” expresses the absolute uninformedness of the sample selection process. The performance of a black-box optimizer has nothing whatsoever to do with its information of the objective function. But the paper recognizes only that information gain is impossible, and claims incoherently that prior information resides in the optimizer itself. Conservation of this chimeral information supposedly accounts for conservation of performance in optimization. Here are the salient points of illogic:

    1. Information causes performance.
    2. The optimizer gains no exploitable information by observation, so it must be prior information that causes performance.
    3. There is no input by which the optimizer might gain prior information, so it must be that prior information inheres in the optimizer.
    4. Prior information of one objective function is prior misinformation of another. Conservation of performance is due to conservation of information.

    It should have been obvious that prior information is possessed only after it is acquired. The error is due in part to a mangled, literalistic half-reading of (Wolpert and Macready, 1995, p. 8, emphasis added):

    The NFL theorem illustrates that even if we know something about [the objective function] … but don’t incorporate that knowledge into [the sampler] then we have no assurances that [the sampler] will be effective; we are simply relying on a fortuitous matching between [the objective function] and [the sampler].

    The present work (Sect. 6) concludes that:

    The tool literally carries information about the task. Furthermore, optimizers are literally tools — an algorithm implemented by a computing device is a physical entity. In empirical study of optimizers, the objective is to determine the task from the information in the tool.

    This reveals confusion of one type of information with another. When a toolmaker imparts form to matter, the resulting tool is in-formed to suit a task. But such form is not prior information. Having been formed to perform is different from having registered a signal relevant to the task. An optimization practitioner may gain information of a problem by observation, and then form a sampler to serve as a proxy in solving it. Although the sampler is informed to act as the practitioner would, it is itself uninformed of the problem to which it is applied, and thus cannot justify its own actions. The inherent form that accounts for its performance is sampling bias.

    Unjustified application of a biased sampler to an optimization problem is merely biased sampling by proxy. The NFL theorems do not speak to this fundamental point. They specify conditions in which all of the samplers under consideration are equivalent in overall performance, or are nearly so. Ascertaining that none of these theorems applies to a real-world circumstance does not justify a bias, but instead suggests that justification may be possible. There is never a “free lunch” for the justifier.

  27. Joe Felsenstein: How about the notion that Einstein’s relativistic mechanics holds not only near the speed of light, but everywhere?

    Good thing you get special treatment here or your comment would end up in guano…

    You probably didn’t copy and paste properly, so it looks like you got 2 concepts mixed up …Relativistic mechanics refers to the motion of bodies whose relative velocities approach the speed of light…

    Regarding your” everywhere” you are probably referring to the constant speed of light where Einstein assumed that since the speed of light appeared to be constant everywhere, therefore, laws of physics have to be the same everywhere…

    A word of advise Joe; stop the bleeding now before it is too late…

  28. Tom English: But it’s hard to deflect pictures showing clearly that evolutionary processes settle into equilibrium, rather than seek out individuals of maximum fitness.

    Was this the point of your OP, that the evolutionary process doesn’t maximize fitness? If you were an evolutionist I’d say you’d cut off your nose to spite your face.

    If that’s what your OP states, that evolution does not maximize fitness, then I completely agree that is front-page news here at TSZ.

    Now in the same way that I can rather easily find evolutionists who present evolution as problem-solving, I am likewise sure that I can find evolutionists claiming that evolution maximizes fitness.

    As I said way up-thread, I don’t see any threat to ID here.

    As far as problem solving, I suggest you read the book by Losos. It’s all about experiments to see if organisms could solve the problems put before them by the experimenters. Adaptation is all about problem solving. Evolutionary theory wouldn’t stand a chance without this way of conceiving of the evolutionary process.

  29. Tom English: The author is referring to a “problem” identified after the fact of its being “solved” by an evolutionary process.

    It’s an adaptation because it was the solution to a problem. It’s identified as such after the fact. So how do we know it’s an adaptation? Because that’s what adaptations are. It’s a tautology.

  30. J-Mac: Relativistic mechanics refers to the motion of bodies whose relative velocities approach the speed of light…

    Actually, it doesn’t. Rather, it refers to mechanics based on theory of relativity. And that applies everywhere, not just at velocities approaching the speed of light.

  31. Mung: It’s all about experiments to see if organisms could solve the problems put before them by the experimenters. Adaptation is all about problem solving. Evolutionary theory wouldn’t stand a chance without this way of conceiving of the evolutionary process.

    Darwinism wouldn’t stand a chance. That’s why I am not a Darwinist and not a pan-adaptationist. But evolutionary theory is not limited to pan-adaptation.

  32. J-Mac: Regarding your” everywhere” you are probably referring to the constant speed of light where Einstein assumed that since the speed of light appeared to be constant everywhere, therefore, laws of physics have to be the same everywhere…

    J-Mac is correct that “everywhere” was ill-chosen (nothing was cut-and-pasted). I should have said “at all velocities”.

    Does J-Mac really believe that relativistic mechanics applies only above some threshold velocity? This would be like J-Mac’s unusual belief that quantum mechanics does not apply to rocks falling down hills..

  33. Joe Felsenstein: Does J-Mac really believe that relativistic mechanics applies only above some threshold velocity?

    Joe, It doesn’t matter what I believe! It only matters what I can prove or if someone else has done it…You are fishing for an excuse to cover up your boo-boo with speculative nonsense… It will not work because, I will say it again, for quantum mechanics to be governing rocks rolling down the hill 2 things would have to happen:
    1. rocks would have to be subatomic particles, which they are clearly not.
    2. The theory of gravity would have to be somehow unified with quantum mechanics into quantum gravity or else. It hasn’t. It will probably not be. Both theories can’t be right. Get it? Read my comment at 3:17 pm sent to guano…

    Regarding your relativistic mechanics here is what Britannica says while still not that accurately. I just don’t feel like writing it…

    “Relativistic mechanics, science concerned with the motion of bodies whose relative velocities approach the speed of light c, or whose kinetic energies are comparable with the product of their masses m and the square of the velocity of light, or mc2. (This one is for Neil ) Such bodies are said to be relativistic, and when their motion is studied, it is necessary to take into account Einstein’s special theory of relativity. As long as gravitational effects can be ignored, which is true so long as gravitational potential energy differences are small compared with mc2, the effects of Einstein’s general theory of relativity may be safely ignored.

    The bodies concerned may be sufficiently small that one may ignore their internal structure and size and regard them as point particles, in which case one speaks of relativistic point-particle mechanics; or one may need to take into account their internal structure, in which case one speaks of relativistic continuum mechanics. This article is concerned only with relativistic point-particle mechanics. It is also assumed that quantum mechanical effects are unimportant, otherwise relativistic quantum mechanics or relativistic quantum field theory—the latter theory being a quantum mechanical extension of relativistic continuum mechanics—would have to be considered. The condition that allows quantum effects to be safely ignored is that the sizes and separations of the bodies concerned are larger than their Compton wavelengths. (The Compton wavelength of a body of mass m is given by h/mc, where h is Planck’s constant.) Despite these restrictions, there are nevertheless a number of situations in nature where relativistic mechanics is applicable. For example, it is essential to take into account the effects of relativity when calculating the motion of elementary particles accelerated to higher energies in particle accelerators, such as those at CERN (European Organization for Nuclear Research) near Geneva or at Fermilab (Fermi National Accelerator Laboratory) near Chicago. Moreover, such particles are caused to collide, thus creating further particles; although this creation process can only be understood through quantum mechanics, once the particles are well separated, they are subject to the laws of special relativity.

    You are wasting my time Joe!

  34. Pedant: Ah, delicious irony.

    Irony could be and actually probably is an effect of a non-linear-delayed-feedback systems or backward causation, which I’m sure you know how it works and how it evolved…
    Delicious 😉

    Doing an OP on it…I expect your full input… lol

  35. Mung:
    So Joe, haven’t you argued that evolution maximizes fitness? Perhaps even in this very thread?

    Both Joe and Tom seem to be confused…They keep posting links for me to check out that totally contradict their views…Look at the thread back a day or two..I challenged them on that and never got the answer…

  36. Tom English: Again, nothing barks so loudly as J-Mac’s omissions.

    Joe corrected himself that his question was not properly worded…So why would I have answered it if Joe himself admitted he hadn’t made it clear enough?
    Can you answer that?
    There is only one conclusion…You were barking at me without knowing what Joe really meant… So, unless your brain is quantum entangled with Joe’s, there is only one conclusion… I’ll let you figure it out yourself or my comment is going to be shot down by the unbiased hitman… 😉

  37. J-Mac: Irony could be and actually probably is an effect of a non-linear-delayed-feedback systems or backward causation, which I’m sure you know how it works and how it evolved…
    Delicious

    Or alternatively it is the voodoo the designer do. End of story

Leave a Reply