Evolutionist: Our Best Defense Against Anti-Science Obscurantism

_{Cornelius Hunter

May 30, 2011

Intelligent Design

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Intelligent Design}

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Evolutionists say undirected, random events, such as mutations, accumulated to create the entire biological world. An analogy once used for this claim is that of a room full of monkeys pounding away at typewriters and producing Hamlet. Today the analogy needs to be updated from typewriters to computer keyboards, but otherwise remains apropos. When the letters are selected at random, a page (or screen) full of text is going to be meaningless. And the problem is no easier in the biological world. Whether English prose or molecular sequences, the problem is that there are relatively few meaningful sequences in an astronomically large volume of possibilities. Nor does selection help because the smallest sequence that could be selected—such as a small gene—is not very small. All of this is rather intuitive and for centuries evolutionists have been trying to solve the problem. Their latest solution is being called natural genetic engineering. Read more

Comments

Clearly, in that general condition, the solution space is much larger, and my point is that evolutionary processes are successful because they do not search the entire combinatorial space, but the solution space only.
First, let's clear up a possible misunderstanding. As we've seen from the quote from Wikipedia, in a GA, potential solutions are encoded in a genome, and this genome is then mutated and subjected to selection. So in that sense every possible solution is part of the solution space. Are we agreed so far? Now let's assume for the sake of argument that the sequences of the genomes in the population are chosen at random, as in WEASEL and ev. How have we reduced the combinatorial space?Mung_{June 7, 2011
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Well, “search” is of course a metaphor...
Not in GA's it isn't. And if in evolution search is only a metaphor then to say that the search space gets reduced is meaningless. But then your claim about how evoltion finds potential solutions breaks down. It no longer has a basis.Mung_{June 7, 2011
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Elizabeth Liddle:
...so if I don’t respond (or only partially) in the next couple of days, don’t think I’ve run away!
Perhaps you've bitten off more than you can chew ;) Too easy to forget on these blogs just how many irons one has in the fire. Some more for you to chew on. Chew slowly. Don't choke! And don't wash it down. Elizabeth Liddle @82:
No, evolution, and indeed, GAs differ from WEASEL programs, but only in the (important however) sense that neither GAs nor evolution is “searching” for a single solution, whereas WEASEL (and Hangman) is.
I think you're wrong. SURPRISE! I say that evolution differs from both Weasel and GA's and that Weasel is a GA. Are you saying that a GA cannot search for a single solution? Why not? Why does looking for one solution disqualify an algorithm from being a GA? How many solutions must a GA look for, minimum, to qualify as a GA?
Indeed, in WEASEL, the problem is stated in terms of the solution “find the closest match to the word phrase: methinks it is like a weasel”.
I have no idea what that means. It sounds like gobblydegook. The problem is stated. Find the closest match to the phrase xxx. The GA encodes potential solutions to the problem in the form of candidate strings. The GA then employs mutation and selection to create the candidate solutions for the next generation.
So it’s completely convergent search.
So what. Aren't searches in general convergent? What's the point of having a search that doesn't converge?
In the case of GAs, the search is not, typically convergent at all (if it were, we wouldn’t bother with a GA). The problem may be: find the antenna configuration that produces the highest SNR.
ok, you have a mistaken conception of GA's. Hopefully you'll study and catch up. Or I may post more material. We use GA's precisely because they are convergent. Even your example is indicative of that fact and appears to me to be just as convergent as WEASEL.
And my point is that the GA, as in evolution, does not have to search every single possible configuration to find every possible solution (and there may be many that it does not find). Instead, it searches hierarchically, and when any part-solution to the problem is found, the search space is than reduced to solutions that build on that part-solution, and so on.
That sounds to me exactly how WEASEL works. Oh well. WEASEL doesn't search every possible string of the same length of the target phrase. The search space, you say it gets reduced. But how does that happen?Mung_{June 7, 2011
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@ Mung and Kairosfocus: Thanks for your detailed responses and questions. Again, I will need a bit of time to do them justice, so if I don't respond (or only partially) in the next couple of days, don't think I've run away! And I also appreciate the opportunity for a real dialogue. Thanks!Elizabeth Liddle_{June 5, 2011
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Dr Liddle: I must first say that I appreciate your straightforwardness in your discussion. That makes for real dialogue instead of having to try to rebut cleverly distractive or dismissive talking points or outright abuse. In that context, I will pause and look at your remark in 88, inserting my markup on points: ______________ >> Yes, human-designed GAs are usually designed to solve a very specific problem (so the fitness function is what is designed, chiefly) for a human purpose, usually the solution to a specific problem, usually in a fairly low-dimensional fitness landscape. a --> Thank you for this frank admission That doesn’t mean we can’t extrapolate b --> This is of course our old friend the argument by analogy. nature is not writing a code for a GA or running it on a computer. c --> Now, oddly enough, I have a lot of respect for well-developed analogies; not least as analogous thinking is key to inductive reasoning, especially the sort of argument on a case by case basis where one reasons by material family resemblance. d --> My concern i this case is that the analogies are all too apt in one key sense, and the dis-analogy is precisely tied to the search-space implications of scaling up the size of he haystack in light of the config space of just 1,000 bits or 125 Bytes or 143 ASCII characters. e --> So, scaling up by extrapolation may in this case have a qualitative effect, as Abel pointed out in his recent paper on a universal plausibility bound; as has been linked already. to systems where the problem is intrinsic to the environment,and might be written as “how to persist in this environment”, f --> The problem I have here is how to arrive in the environment, rather than how to persist in it once arrived. and the fitness landscape is intrinsically high-dimensioned. g --> If you mean that the possibility pace is very large, and the regions of interest are very small by comparison, that is key. Clearly, in that general condition, the solution space is much larger, h --> The space of possibilities from which a solution is to be found may indeed be very large, but the problem is that that then makes solution sets -- islands of complex and specific function -- very isolated indeed in large seas of non-functional configurations. and my point is that evolutionary processes are successful because they do not search the entire combinatorial space, but the solution space only. i --> You are here implying that the "solution space" is a continent of function, and that one needs not concern oneself about the wider set of possibilities. j --> But this assumes a start-point within the island of function, and implies so large a connected region of solutions that fit on a nice trend pattern that you dismiss the issue of getting to the island's shoreline. k --> That is precisely what there is no right to assume, indeed, it boils down to an implicit acknowledgement that the searches you are looking at are micro-evolutionary, within an island of existing function l --> The real problem on the table is how to get to such islands of function, not how to hill climb within them. m --> I have already pointed out above some of the reason why we have good warrant for understanding that complex function of information based systems, technological, linguistic and biological, will show the pattern of relatively isolated islands of function in large configuration spaces that make the topology more or less like a Pacific ocean with isolated islands. n --> For instance, to get from a Hello World to an operating system, one cannot step in small, functional increments, but must transform one's whole design of the software system, and must create whole algorithms, data structures and coding patterns. There are principles in common -- why Hello World is a typical "first program" -- but there are entire courses of study between a Hello World and designing one's own operating system. o --> Similarly, See Spot Run is not going to change in small steps under spontaneous control of random walk based trial and error rooted in duplicate and vary till function emerges and somehow integrates into a coherent narrative, to become as much as this blog post, not within the resources of the observed cosmos. p --> Similarly, in an organism, the code for a typical protein of 300 AA is already beyond the search resources of the cosmos, especially if one has to cross fold domains to get a new function. q --> Add in the regulatory requirements to govern when across the lifespan or in response to what external stress the protein must be expressed and transported, and the complexity has exploded. r --> Going on, complex organisms must be embryologically feasible, growing form a cell to an integrated organism with distinct cell types, tissues in their proper place, organs, and integrated systems, Other wise it cannot function, whether in the embryo or as a living and reproducing organism. s --> And to transform a single celled organism to this, is a huge leap in information requisites well beyond the search capacity of the cosmos again. With no observed sign of an easy small-step continuity from the one to the other. All has been inferred based on the requisites of Darwinism, in a context controlled by Lewontin's a priori materialism held to be the defining essence of "science." t --> And, this assumes we have the first cluster of unicellular organisms. But to cross the bridge from a complex, racemic mix of chemicals in a pond or a volcano vent or the like, to an organised living cell is just as complex and the intermediate steps are just as missing from the world of observation. u --> Mix in that our observed cosmos is fine tuned to sit at an operating point that makes abundant water, abundant carbon, etc possible, and gives these the sort of properties they have, also has in it environments conducive to C-Chemistry, cell based intelligent life. v --> This puts design on the table as a very reasonable explanation for the observed cosmos, one anchored in scientific observations. A designer capable of building a very special type of cosmos, in effect. w --> Similarly, there is but one observed class of sources for the sort of complex functional integration and associated information systems we observe in life: intelligence. We have not as yet invented a full blown kinematic vNSR [we have some of the key components in hand now], so that our products are self-replicating, but Venter has given us proof of concept. x --> So, it is inherently reasonable based on observed cause-effect patterns, to conclude that on best explanation, we live in a designed cosmos, that life is designed, and that complex multicellular organisms are also designed. y --> this does not force all to accept this, but it does mean that the discussion should not be censored or poisoned. Both of which are concerns. >> _______________ I also endorse Mung's slate of questions. GEM of TKIkairosfocus_{June 5, 2011
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Elizabeth:
Clearly, in that general condition, the solution space is much larger, and my point is that evolutionary processes are successful because they do not search the entire combinatorial space, but the solution space only.
Mung:
A claim with no basis in fact.
Elizabeth:
It’s not supposed to be based in fact, it’s based in logic. But supported by fact.
So let's start from scratch, if you will. By combinatorial space you mean... By solution space you mean... Your basis for claiming that the solution space is much larger is... Is it safe to say that the solution space is a subset of the combinatorial space? What can we call the space that is within the combinatorial space but which is not within the solution space? How do we know what is within the solution space and what is not? How do we know the size of the solution space? How is it that evolution "knows" not to step outside the solution space and into the non-solution space? If evolution can't tell the two apart, how is it that it manages to stay within a space that has boundaries of which it is completely unaware? Take your time.Mung_{June 4, 2011
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I’m not really sure why there is an issue here – most people seem to accept, for instance, that “microevolution” occurs (peppered moths, guppies, beaks of Galapagos finches etc), right? Or perhaps you don’t?
1. I define micro-evolution as changes in the frequency of an allele in a population. Is that an acceptable definition? 2. What do changing frequencies of alleles in populations have to tell us about how those alleles arose in the first place? 3. IOW, before something can affect the frequency of an allele in a population, the allele must first exist. Call it a search for new alleles.
I’m not really sure why there is an issue here
Perhaps because you are talking about one thing, and the rest of us are talking about something completely different. The search problem continues to exist, and selection can't help.Mung_{June 4, 2011
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F/N: Re Dr BOT at 3:
Monkeys at typewriters are just monkeys at typewriters. I don’t know any biologists who would take this claim seriously, it just indicates a total failure to understand the basics of evolution.
Actually, the commonest5 use of this illustration in recent decades was to promote the idea that even very unlikely events were "inevitable" once there were enough time and resources to throw at it. And, this was often argued by -- you guessed it -- promoters of evolutionary materialism. It is precisely because of the success of the rebuttal on the actual implications, that we see this backing away and denial. (Sort of like the pretence nowadays that Biologists did not speak about junk dna, or don't call themselves Darwinists, or don't use terms like Macro-evolution.) Of course, the root problem is that the challenge begins to bite very fast: 125 bytes of info is very short for any meaningful control situation. And yet the resources of the observable cosmos are wholly inadequate to search a space of possibilities to any extent significantly less than zero, if we have 1,000 bits worth of info. And of course first life is looking at about 100 - 1,000 k bits worth of info, and novel body plans are looking at 10 - 100 mn bits. Nor are you in a position to show empirically that the general pattern of functional information being in isolated islands, is broken in this case. Indeed, we know that protein fold domains -- sequences that fold correctly to work -- are deeply isolated in AA sequence space. (And more, cf above.) GEM of TKI GEM of TKIkairosfocus_{June 4, 2011
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It's not supposed to be based in fact, it's based in logic. But supported by fact. I'm not really sure why there is an issue here - most people seem to accept, for instance, that "microevolution" occurs (peppered moths, guppies, beaks of Galapagos finches etc), right? Or perhaps you don't? In those instances, the population doesn't have to "search" every possible combination of every allele, or every possible new allele, in order to adapt quickly to new environmental conditions, a change in bark colour; a change in predator prevalence/stream-bed properties; a change in prevalent seed sizes, because once embarked on a minimally advantageous "solution", only subsets of that "solution space" are "searched". I didn't even expect this to be controversial. Perhaps you thought I meant something I didn't.Elizabeth Liddle_{June 4, 2011
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Clearly, in that general condition, the solution space is much larger, and my point is that evolutionary processes are successful because they do not search the entire combinatorial space, but the solution space only.
A claim with no basis in fact.Mung_{June 4, 2011
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kairosfocus, Thanks so much for your post @87. So timely. Once again MathGrrl's claims about ev are shown to be utterly without merit.
A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain.
Also worthy of being repeated, from your @78:
There is precisely zero observational evidence for such a structure to the config space of genomes, and every evidence that even short mutations are overwhelmingly likely to be deleterious, and even the overwhelming majority of “successful” adaptations work by breaking an existing genetic capacity, not by spontaneously creating one out of chance variation and success.
Mung_{June 4, 2011
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Yes, human-designed GAs are usually designed to solve a very specific problem (so the fitness function is what is designed, chiefly) for a human purpose, usually the solution to a specific problem, usually in a fairly low-dimensional fitness landscape. That doesn't mean we can't extrapolate to systems where the problem is intrinsic to the environment,and might be written as "how to persist in this environment", and the fitness landscape is intrinsically high-dimensioned. Clearly, in that general condition, the solution space is much larger, and my point is that evolutionary processes are successful because they do not search the entire combinatorial space, but the solution space only.Elizabeth Liddle_{June 4, 2011
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Dr Liddle: I think, rather, the point is that GA's are all very carefully designed by knowledgeable and intelligent designers, to search strictly delimited domains of scope suited to search, using algorithms that reward progress towards an implicit goal on some figure of merit [hill climbing]. Let us clip Wiki, testifying against interest, to make the discussion a bit more specific:
In a genetic algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached . . . . A typical genetic algorithm requires: a genetic representation of the solution domain, a fitness function to evaluate the solution domain. A standard representation of the solution is as an array of bits. Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, which facilitates simple crossover operations. Variable length representations may also be used, but crossover implementation is more complex in this case. Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming. The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem dependent . . . . Once we have the genetic representation and the fitness function defined, GA proceeds to initialize a population of solutions randomly, then improve it through repetitive application of mutation, crossover, inversion and selection operators.
in short, GA's are optimisation problems, which makes them inherently goal directed. They depend on a mapping between a variable string or similar structure, and a solution domain which has a fitness function that gives values to points in the domain. Optimisation is by incremental hill climbing on tossing out rings of random samples, i.e. it depends on having trends that lead you uphill to "good" solutions. In short, they are ALWAYS within islands of function [start near the goal in a zone where feedback to random sampling of points with fitness values will tell you where to go], and are thus irrelevant to how we get to shorelines of such islands in the midst of vast seas of non-function. This is the key issue. An inherently goal directed search, within a set up zone of interest. But is the implicit assumption that getting to an island of function is an easy problem correct? Not at all. For, for first life, credibly we are talking about 100 - 1,000+ kbits of functional information. At just 100 k bits, we would be dealing with a config space of 9.99 * 10^30,102, to try to find islands of function in it. To be credible, we have to have enough scope of search that it is reasonable that we would hit on an island.(Recall my response to Dr BOT above on the assertion that I am just assuming that functional configs come in islands.) Our observed universe has about 10^80 atoms, and these will go through about 10^150 Planck time quantum states. There is no way that any search of the possibility space for relevant molecules of life could sample a fraction of the space appreciably different from zero. That is, an observed cosmos scope search rounds down to zero. That is, the key challenge is to get first to shores of function. A similar challenge holds for novel body plans, which have to innovate of order of 10 - 100 Mbits of fresh integratedly functional information. In that context to focus on how -- having arrived at an island of function by intelligent design -- a GA is able to hill climb through modest casting out of rings of fresh samples and moving uphill, is to beg the question. But then, Johnson -- replying to Lewontin -- aptly observed:
For scientific materialists the materialism comes first; the science comes thereafter. [[Emphasis original] We might more accurately term them "materialists employing science." And if materialism is true, then some materialistic theory of evolution has to be true simply as a matter of logical deduction, regardless of the evidence. That theory will necessarily be at least roughly like neo-Darwinism, in that it will have to involve some combination of random changes and law-like processes capable of producing complicated organisms that (in Dawkins’ words) "give the appearance of having been designed for a purpose." . . . . The debate about creation and evolution is not deadlocked . . . Biblical literalism is not the issue. The issue is whether materialism and rationality are the same thing. Darwinism is based on an a priori commitment to materialism, not on a philosophically neutral assessment of the evidence. Separate the philosophy from the science, and the proud tower collapses. [[Emphasis added.] [[The Unraveling of Scientific Materialism, First Things, 77 (Nov. 1997), pp. 22 – 25.]
So, to really see what is going on, we have to look at the implicit assumptions. In this case, we are being invited to not notice the strings and curtains, paying attention only to the puppets onstage. Yes, GA's come on stage on an island of function and are able to move towards the uphill direction that is built in as the way to make progress. But all of that is set up by someone offstage. Just as, when we hear a song on an MP3 player, it is not coming from the machine, but from the person who recorded and loaded the song in an instrument designed to play it. GEM of TKIkairosfocus_{June 4, 2011
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Well, I haven't been clear: I didn't say that Darwin's theory was teleological - it wasn't. But Darwin himself did not rule out a Creator-breathed seed, as is clear from this final paragraph. His point was (in essence) that from there on, natural selection was a designer-mimic. So the idea that the starting point was designed (with the intention that it would then evolve into many complex and diverse lifeforms) is not anti-Darwin. I don't think it's correct, though - I think Darwin's principles can be applied all the way back to self-replicating units that we would hesitate to call "alive". But others differ. But the application of Darwinian principles from a given point onwards doesn't depend on where you think that initial point came from. That's what we do with GAs after all - we "design" a system in which "critters" will evolve to find a solution to a problem we want to solve, saving us the trouble of designing a solution ourselves.Elizabeth Liddle_{June 4, 2011
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Lizzie: There is nothing in Darwin’s theory that says that a minimal “seed” organism wasn’t intelligently designed, and designed in such a way that subsequent diversification would inevitably follow by Darwinian principles. So yes, Life could have been Designed to evolve. But that is compatible with Darwin’s theory. His theory was on the Origin of Species, not the Origin of Life. He specifically says so at the very end of the book. That is flat out wrong- teleology is not allowed in Darwin's theory nor the current theory of evolution. Read Darwin's "On the Origins Of Species..." and see how many times he cites "chance" and how many times he cites a designing agency. Geez his whole point about natural selection was it is a designer mimic. Also as Dawkins and others have said a designed or creted biologymeans we are lokng at a totly different type of biology.Joseph_{June 4, 2011
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First: yes "the fittest survive" doesn't just border on circularity, it is circular. That's why I think it's an unfortunate formulation, because it casts as a hypothesis something that is self-evident. Secondly: I'm still working on a response to your other thread. I'll try to get it up today.Elizabeth Liddle_{June 4, 2011
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Dr Liddle: Pardon a repeated emphasis: the problem is not to move around within the body plan island of function. It is to arrive there. It is not that the fittest survive [by definition, verging on circularity], but that they arrive, that has to be explained. When that fitness is an embryologically feasible, metabolising, vNSR info based self replicating cell based reproducing entity. GEM of TKIkairosfocus_{June 4, 2011
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Mung @ 72
Elizabeth Liddle @27: It’s a bit like the game of hangman (do kids play hangman in the US)? You start by guessing letters at random, but once you’ve got a couple – a vowel, say, in a particular spot, then the search space starts to collapse. Eventually the number of possible solutions reduces to perhaps one or two words. As for your last point – well the game of hangman is a case in point. So are GAs. Stochastic processes can be very good at finding winners when part of an evolutionary algorithm. We call it WEASEL. As in, how many generations does it take our program to find “Methinks it is like a weasel.” And that may be how GA’s work, or WEASEL programs, or hangman, but it’s now how evolution works. You should know better. Shame.
Yes, I should have cited WEASEL. No, evolution, and indeed, GAs differ from WEASEL programs, but only in the (important however) sense that neither GAs nor evolution is "searching" for a single solution, whereas WEASEL (and Hangman) is. Indeed, in WEASEL, the problem is stated in terms of the solution "find the closest match to the word phrase: methinks it is like a weasel". So it's completely convergent search. In the case of GAs, the search is not, typically convergent at all (if it were, we wouldn't bother with a GA). The problem may be: find the antenna configuration that produces the highest SNR. And my point is that the GA, as in evolution, does not have to search every single possible configuration to find every possible solution (and there may be many that it does not find). Instead, it searches hierarchically, and when any part-solution to the problem is found, the search space is than reduced to solutions that build on that part-solution, and so on. That was my point. I hope I have made it a little more clearly now. Mung @73
Elizabeth Liddle @38: Evolution isn’t looking for a needle in a haystack… So true. It’s looking for numerous sort-of-needles in numerous sort-of-haystacks. Or rather, it doesn’t really quite know what it is looking for, or where to look for it. Or even better, it’s not really looking for anything at all. So why people think it’s a search, or can be modeled as a search, is beyond me. Good point Elizabeth.
Well, "search" is of course a metaphor, and of course you are correct (as I endorse above) that in evolution (but also in GAs) there is no single solution. However, I think the metaphor works reasonable well, as long as we think in terms of looking for a solution to a problem, rather than looking for a single hidden object. The important point, though, is that the number of possible combinations is irrelevant to the issue of whether GAs or evolutionary algorithms can produce solutions to the problem of "persistence" in a given environment, because only an increasingly small series of subsets of "promising" solutions is explored.Elizabeth Liddle_{June 4, 2011
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F/N A: It seems we need to point to Orgel's key contrast again:
. . . In brief, living organisms are distinguished by their specified complexity. Crystals are usually taken as the prototypes of simple well-specified structures, because they consist of a very large number of identical molecules packed together in a uniform way. Lumps of granite or random mixtures of polymers are examples of structures that are complex but not specified. The crystals fail to qualify as living because they lack complexity; the mixtures of polymers fail to qualify because they lack specificity. [[The Origins of Life (John Wiley, 1973), p. 189.]
The vNSR based self replication of metabolising, living forms is utterly different form the process of forming a crystal based on molecular structure and forces of interaction. And the order of a crystal is utterly distinct form the organisation of a living form, and again form the randomness of the crystals in a granite counter top or in a tar. F/N B: I need to underscore why the root of the tree of life is so crucial. On the evo mat frame, this is claimed to be the result of chance circumstances and blind mechanical forces in some warm pond or undersea volcanic vent or a comet's dirty snowball, etc. But, this is incredible, and as the abstract marked up already shows, there is an utter failure - per needles in haystacks -- to credibly account for the complex functionally specific organisation and information involved on chance circumstances [the other main source of high contingency]. By direct contrast, we know that FSCO/I is routinely produced by intelligence. And Venter has provided proof of concept. So, it is highly reasonable, indeed the inference to best empirically supported explanation to conclude that life's origin is best explained on design. Choice contingency. (Just as, such design best explains the way the observed cosmos is so set to a fine-tuned operating point that supports C-chemistry, cell based life.) Design is on the table. And when we turn to origin of body plans (which, recall, unfold from a single cell by an algorithmic integrated process of development), we see that we again run into: the origin of FSCO/I. A third inference to design is warranted. Design is a coherent explanation and one that accounts for the empirical fact of FSCO/I that is not accounted for on any other grounds. And, the same issue of functionally specific and complex contingent organisation is the thread running through the three contexts. GEM of TKIkairosfocus_{June 4, 2011
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Nothing Liz, feel free to ignore me. Ignore me, in the same way in which you ignored the onset of recorded information in the thread yesterday. The real details get messy… and they’re harder to scrub clean with that Darwinian Dishsoap you’re selling.
I have no wish to ignore you, Upright BiPed, and if I inadvertently missed a previous post, I apologise. I often follow a link from the "latest post" list on the front page, and fail to notice that there have been a number of intervening posts. I'd be grateful if you could link, or give a post reference to the posts in question. Cheers Lizzie (not Liz, usually, I've never felt up to being a Liz, probably because of Liz Taylor, whom I don't resemble much :))Elizabeth Liddle_{June 4, 2011
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PS: there are now something like 1/4 million plus fossil species identified, on millions of fossils collected and billions observed. The body forms of something like 1/2 or more of all currently living known forms can be found in the fossils collected. In short, we have good reason to infer to a good cross sectional sample. the dominant pattern of that cross section is sudden appearance without reasonable antecedents, stasis of form, and disappearance and/or continuity into the current era. We OBSERVE islands of function but assume a branching tree of life with conjectural ancestral species, the notoriously and all too evidently peristently missing links.kairosfocus_{June 4, 2011
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Dr BOT: Your label and dismiss tactics do not impress me, nor do they impress the astute onlooker. Observe your key admission:
Many systems, like computer software, are brittle – they break unless you tinker with them in a highly structured way – and in this way they are strikingly different than biology. You can’t evolve everything so selecting examples of things that are not easily evolvable does not disprove evolution.
The highlighted shows your own question-begging assumption, underlined by the way you dismissed my challenge to show the link between a unicellular organism and an arthropod. It is held that the one evolved into the other, similarly that several dozen top tier body plans evolved from the unicellular world. The actual observed evidence -- and which is as Gould, Patterson and others have admitted DOMINANT in the fossil record -- is of sudden appearance, stasis and disappearance and/or continuation into the modern era. This is the actual observed pattern, hence terms like Cambrian life revolution. (BTW, I am not amused at your suggestion of incompetent or dishonest quotation out of context. Do you not realise that Gould et al developed their alternative model, punctuated equilibria, because they wanted a theory that better fitted that dominant pattern? That is a matter of well known history of biology. I suggest you read the review in the just linked, shortly after the clip.) Let me do a bit more quoting from Gould to see that the above is in the context of his wider work and expresses a point noted all the way back to Darwin, who recognised that the actual pattern of the fossil record was not supportive to his theory, which he hoped would change with further explorations, but in fact the further work has shown that the dominant pattern evident from the beginning is real [a natural lawlike regularity is often evident from the earliest observations and then persists in the face of onward investigations . . . ]:
"The absence of fossil evidence for intermediary stages between major transitions in organic design, indeed our inability, even in our imagination, to construct functional intermediates in many cases, has been a persistent and nagging problem for gradualistic accounts of evolution." [[Stephen Jay Gould (Professor of Geology and Paleontology, Harvard University), 'Is a new and general theory of evolution emerging?' Paleobiology, vol.6(1), January 1980,p. 127.] "All paleontologists know that the fossil record contains precious little in the way of intermediate forms; transitions between the major groups are characteristically abrupt." [[Stephen Jay Gould 'The return of hopeful monsters'. Natural History, vol. LXXXVI(6), June-July 1977, p. 24.] "The extreme rarity of transitional forms in the fossil record persists as the trade secret of paleontology. The evolutionary trees that adorn our textbooks have data only at the tips and nodes of their branches; the rest is inference, however reasonable, not the evidence of fossils. Yet Darwin was so wedded to gradualism that he wagered his entire theory on a denial of this literal record:
The geological record is extremely imperfect and this fact will to a large extent explain why we do not find intermediate varieties, connecting together all the extinct and existing forms of life by the finest graduated steps [[ . . . . ] He who rejects these views on the nature of the geological record will rightly reject my whole theory.[[Cf. Origin, Ch 10, "Summary of the preceding and present Chapters," also see similar remarks in Chs 6 and 9.]
Darwin's argument still persists as the favored escape of most paleontologists from the embarrassment of a record that seems to show so little of evolution. In exposing its cultural and methodological roots, I wish in no way to impugn the potential validity of gradualism (for all general views have similar roots). I wish only to point out that it was never "seen" in the rocks. Paleontologists have paid an exorbitant price for Darwin's argument. We fancy ourselves as the only true students of life's history, yet to preserve our favored account of evolution by natural selection we view our data as so bad that we never see the very process we profess to study." [[Stephen Jay Gould 'Evolution's erratic pace'. Natural History, vol. LXXXVI95), May 1977, p.14.] [[HT: Answers.com]
The evidence is that micro-variations and adaptations within an island of function are real. Beyond that level, all fades into just so stories and speculations. The root reason for this is the point of contact between biology and information systems. Namely, that in the heart of the cell -- embryogenesis develops the full organism from a single living cell [lobster zygote to lobster . . . ] -- there is an information system. The brittleness of info systems against too much random variation is precisely the in-common reason why biological systems and software show islands of function. So, yes, we see adaptation and variation, but also we see boundaries whereby to cross those we need an intelligently directed input to cross a sea of non functional configurations. The answer tothe challenge to go from amoeba-like organism to a lobster ofr the like it so observe how it happens every day around us: embryogenesis is an algorithmic unfolding that is based on duplication, controlled specialisation and formation of a tightly integrated structure based on specialised cells, tissues, organs and systems forming a coherent body plan. The level of information involved to do that is plainly of the order of 10 - 100+ million bases. We observe this process in action routinely , day by day. Now to move from the hypothetical universal common unicellular ancestor to a lobster of the equivalent in the Cambrian era, would require the origin of that program. That would more or less have to be by a process of duplication and random variation, incrementally rewarded by differential success. This implies a smoothly varying continental structure to the config space for the related information systems, one traversible by a tree of life pattern. There is precisely zero observational evidence for such a structure to the config space of genomes, and every evidence that even short mutations are overwhelmingly likely to be deleterious, and even the overwhelming majority of "successful" adaptations work by breaking an existing genetic capacity, not by spontaneously creating one out of chance variation and success. As outlined above, islands of function are a commonplace observation for functional configs in large spaces of possibilities. And indeed the deep isolation of protein fold domains are a capital example of this. Such proteins need to take up very particular AA sequences, with some room for variation, but not a lot, to fold and function properly. If you look at how tRNA needs to fold into the cloverleaf then the L-arm, you will see again just how constrained variations will be for function to emerge [the arms have to match for the fold to work]. The tRNA is of course a key component of protein manufacture, which is again a step by step algorithmic process, in a nanotech factory in the cell. We know that intelligence can get us to islands of function, per massive observation. In addition the needle in the haystack analysis warns us that within very short order, random walks are not going to be credibly able to do the same, within the probability resources of our solar system or the observed cosmos as a whole. There is plenty of reason to infer to design as the best explanation of what we see in life forms, absent a priori imposition of materialism or what is tantamount to materialism. But also, we have ample evidence that such imposition is a material issue. I suggest you work through the IOSE units on OOL and OO Body Plans, to see the force of this set of issues. GEM of TKIkairosfocus_{June 4, 2011
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Actually, it is not the case that self-replicators have been observed to exist.
Good point! Perfect self replicators aren't often observed - but I'm not sure they haven't been totally unobserved - imperfect self replicators are all around us, and are exactly what is needed for evolution - perfect self replication doesn't generate variety so nothing evolves ;)DrBot_{June 4, 2011
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Mung: Generally speaking, cells self-replicate or may specialise by a regulatory process [methylation and all that]. Organisms reproduce,and in some cases may be cloned. getting to the capacity to replicate is a challenge, and getting an embryologically sound body development plan/algorithm is a challenge. Until these challenges a re properly faced and acknowledged, there will be no progress. And, we need to note that Weasel is targetted search, based on Hamming distance and reward of increments in proximity to target (the Hamming Oracle). GA's that are based on convenient genomes that specify nice trendy fitness functions, with slopes amenable to hill climbing, are again produces to intelligent design, and assume in effect that one is already on an island of function. In the vast seas of non-functional configs, there will be no nice trend. I find that there are a lot of just so stories and a lot of seriously begged questions on evolutionary materialist models of origin of life and of body plans. Finally, as a sampler of what adaptation can do, let us think about the dog-wolf kind. Imagine, wolves and dogs of all varieties are now recognised as a single species. And, let us realise just how arbitrary the line "species" can be, e.g. US-style Elks and Red deer interbreed freely in New Zealand where both were stocked. And we must remember the circumpolar gull complex where there is smooth variation around the pole -- all within an obvious island of function and with a given body plan. I gather the ring is sufficiently broad that the two gulls in W Europe do not (normally?) interbreed, thought the gradients in the population are said to be smooth. And of course in the Galapagos, the bird varieties apparently can breed across species lines, quite successfully. But, what we see -- as opposed to what may be speculated -- are limits, generally held to be about the level of the family. GEM of TKIkairosfocus_{June 4, 2011
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Please, stop projecting assumptions onto me —
You are making assumptions, there is nothing being projected - although I appreciate that you sincerely believe that your assumptions are actually facts.
can you show the step by step continuity between say an amoeba and a lobster?
Why do I have to do this?
Similarly, between say a “Hello World” program and an operating system?</blockquote) WHAT?
Between a sentence like “See Spot run” and a computing science textbook?
You want me to show you how things that don't evolve, evolve ... KF, sorry to be blunt but when you start demanding that people show how one can get from 'hello world' to an OS as some kind of proof of evolution all it illustrates is your lack of understanding of the topic - despite the numerous correctives I've supplied on these matters. Not all search spaces are searchable by genetic algorithm. Many systems, like computer software, are brittle - they break unless you tinker with them in a highly structured way - and in this way they are strikingly different than biology. You can't evolve everything so selecting examples of things that are not easily evolvable does not disprove evolution.
In short, islands of function are what we observe.
Hmm, I wonder what the full quotes from Gould in their context look like ;) What you are referring to is the fossil record - a sparse record collected over millions of years. If you looked back at the history of computing with the same granularity you would see that we went from the abacus to the desktop PC in a single step!
DrBot_{June 4, 2011
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DrBot @44:
evolution relies on the existence of self replicators (something that has been empirically observed to exist)
Actually, it is not the case that self-replicators have been observed to exist. When a woman gives birth she is not giving birth to another self of herself. Nor is she giving birth to another self of her husband. Neither she nor her husband "self-replicated." She is giving birth to a new and distinct self.Mung_{June 3, 2011
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Elizabeth Liddle @38: Evolution isn’t looking for a needle in a haystack... So true. It's looking for numerous sort-of-needles in numerous sort-of-haystacks. Or rather, it doesn't really quite know what it is looking for, or where to look for it. Or even better, it's not really looking for anything at all. So why people think it's a search, or can be modeled as a search, is beyond me. Good point Elizabeth.Mung_{June 3, 2011
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Elizabeth Liddle @27:
It’s a bit like the game of hangman (do kids play hangman in the US)? You start by guessing letters at random, but once you’ve got a couple – a vowel, say, in a particular spot, then the search space starts to collapse. Eventually the number of possible solutions reduces to perhaps one or two words. As for your last point – well the game of hangman is a case in point. So are GAs. Stochastic processes can be very good at finding winners when part of an evolutionary algorithm.
We call it WEASEL. As in, how many generations does it take our program to find "Methinks it is like a weasel." And that may be how GA's work, or WEASEL programs, or hangman, but it's now how evolution works. You should know better. Shame.Mung_{June 3, 2011
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Well, some algorithms self-replicate with variance, which is what a GA is.
Um, no. Sheesh. I recently re-posted a number of links to introductory free online material on GA's. HEREMung_{June 3, 2011
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Dr Bot: Please, stop projecting assumptions onto me -- can you show the step by step continuity between say an amoeba and a lobster? Similarly, between say a "Hello World" program and an operating system? Between a sentence like "See Spot run" and a computing science textbook? We can fairly easily show that in the space of configs for any reasonably complex cluster of digital entities [and here note DNA is digital] the vast majority of the space will be taken by nonsense configs, and the meaningful ones will be deeply isolated. Protein fold domains -- proteins being essentially 20 state per element systems, are also deeply isolated. And, just for capping off, we can look at the observation of Gould on the nature of fossils as collected:
. . long term stasis following geologically abrupt origin of most fossil morphospecies, has always been recognized by professional paleontologists. [[The Structure of Evolutionary Theory (2002), p. 752.] . . . . The great majority of species do not show any appreciable evolutionary change at all. These species appear in the section [[first occurrence] without obvious ancestors in the underlying beds, are stable once established and disappear higher up without leaving any descendants." [[p. 753.] . . . . proclamations for the supposed ‘truth’ of gradualism - asserted against every working paleontologist’s knowledge of its rarity - emerged largely from such a restriction of attention to exceedingly rare cases under the false belief that they alone provided a record of evolution at all! The falsification of most ‘textbook classics’ upon restudy only accentuates the fallacy of the ‘case study’ method and its root in prior expectation rather than objective reading of the fossil record. [[p. 773.]
In short, islands of function are what we observe. The smoothly shaded off tree of life -- with conjectured in-dills between the observed discrete points -- is what is conjectural. Precisely the opposite to the impression commonly communicated. GEM of TKIkairosfocus_{June 3, 2011
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