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On FSCO/I vs. Needles and Haystacks (as well as elephants in rooms)

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Sometimes, the very dismissiveness of hyperskeptical objections is their undoing, as in this case from TSZ:

Pesky EleP(T|H)ant

Over at Uncommon Descent KirosFocus repeats the same old bignum arguments as always. He seems to enjoy the ‘needle in a haystack’ metaphor, but I’d like to counter by asking how does he know he’s not searching for a needle in a needle stack? . . .

What had happened, is that on June 24th, I had posted a discussion here at UD on what Functionally Specific Complex Organisation and associated Information (FSCO/I) is about, including this summary infographic:

csi_defnInstead of addressing what this actually does, RTH of TSZ sought to strawmannise and rhetorically dismiss it by an allusion to the 2005 Dembski expression for Complex Specified Information, CSI:

χ = – log2[10^120 ·ϕS(T)·P(T|H)].

–> χ is “chi” and ϕ is “phi” (where, CSI exists if Chi > ~ 1)

. . . failing to understand — as did the sock-puppet Mathgrrrl [not to be confused with the Calculus prof who uses that improperly appropriated handle) — that by simply moving forward to the extraction of the information and threshold terms involved, this expression reduces as follows:

To simplify and build a more “practical” mathematical model, we note that information theory researchers Shannon and Hartley showed us how to measure information by changing probability into a log measure that allows pieces of information to add up naturally:

Ip = – log p, in bits if the base is 2. That is where the now familiar unit, the bit, comes from. Where we may observe from say — as just one of many examples of a standard result — Principles of Comm Systems, 2nd edn, Taub and Schilling (McGraw Hill, 1986), p. 512, Sect. 13.2:

Let us consider a communication system in which the allowable messages are m1, m2, . . ., with probabilities of occurrence p1, p2, . . . . Of course p1 + p2 + . . . = 1. Let the transmitter select message mk of probability pk; let us further assume that the receiver has correctly identified the message [[–> My nb: i.e. the a posteriori probability in my online discussion here is 1]. Then we shall say, by way of definition of the term information, that the system has communicated an amount of information Ik given by

I_k = (def) log_2  1/p_k   (13.2-1)

xxi: So, since 10^120 ~ 2^398, we may “boil down” the Dembski metric using some algebra — i.e. substituting and simplifying the three terms in order — as log(p*q*r) = log(p) + log(q ) + log(r) and log(1/p) = log (p):

Chi = – log2(2^398 * D2 * p), in bits,  and where also D2 = ϕS(T)
Chi = Ip – (398 + K2), where now: log2 (D2 ) = K
That is, chi is a metric of bits from a zone of interest, beyond a threshold of “sufficient complexity to not plausibly be the result of chance,”  (398 + K2).  So,
(a) since (398 + K2) tends to at most 500 bits on the gamut of our solar system [[our practical universe, for chemical interactions! ( . . . if you want , 1,000 bits would be a limit for the observable cosmos)] and
(b) as we can define and introduce a dummy variable for specificity, S, where
(c) S = 1 or 0 according as the observed configuration, E, is on objective analysis specific to a narrow and independently describable zone of interest, T:

Chi =  Ip*S – 500, in bits beyond a “complex enough” threshold

  • NB: If S = 0, this locks us at Chi = – 500; and, if Ip is less than 500 bits, Chi will be negative even if S is positive.
  • E.g.: a string of 501 coins tossed at random will have S = 0, but if the coins are arranged to spell out a message in English using the ASCII code [[notice independent specification of a narrow zone of possible configurations, T], Chi will — unsurprisingly — be positive.

explan_filter

  • S goes to 1 when we have objective grounds — to be explained case by case — to assign that value.
  • That is, we need to justify why we think the observed cases E come from a narrow zone of interest, T, that is independently describable, not just a list of members E1, E2, E3 . . . ; in short, we must have a reasonable criterion that allows us to build or recognise cases Ei from T, without resorting to an arbitrary list.
  • A string at random is a list with one member, but if we pick it as a password, it is now a zone with one member.  (Where also, a lottery, is a sort of inverse password game where we pay for the privilege; and where the complexity has to be carefully managed to make it winnable. )
  • An obvious example of such a zone T, is code symbol strings of a given length that work in a programme or communicate meaningful statements in a language based on its grammar, vocabulary etc. This paragraph is a case in point, which can be contrasted with typical random strings ( . . . 68gsdesnmyw . . . ) or repetitive ones ( . . . ftftftft . . . ); where we can also see by this case how such a case can enfold random and repetitive sub-strings.
  • Arguably — and of course this is hotly disputed — DNA protein and regulatory codes are another. Design theorists argue that the only observed adequate cause for such is a process of intelligently directed configuration, i.e. of  design, so we are justified in taking such a case as a reliable sign of such a cause having been at work. (Thus, the sign then counts as evidence pointing to a perhaps otherwise unknown designer having been at work.)
  • So also, to overthrow the design inference, a valid counter example would be needed, a case where blind mechanical necessity and/or blind chance produces such functionally specific, complex information. (Points xiv – xvi above outline why that will be hard indeed to come up with. There are literally billions of cases where FSCI is observed to come from design.)

xxii: So, we have some reason to suggest that if something, E, is based on specific information describable in a way that does not just quote E and requires at least 500 specific bits to store the specific information, then the most reasonable explanation for the cause of E is that it was designed. The metric may be directly applied to biological cases:

Using Durston’s Fits values — functionally specific bits — from his Table 1, to quantify I, so also  accepting functionality on specific sequences as showing specificity giving S = 1, we may apply the simplified Chi_500 metric of bits beyond the threshold:
RecA: 242 AA, 832 fits, Chi: 332 bits beyond
SecY: 342 AA, 688 fits, Chi: 188 bits beyond
Corona S2: 445 AA, 1285 fits, Chi: 785 bits beyond

Where, of course, there are many well known ways to obtain the information content of an entity, which automatically addresses the “how do you evaluate p(T|H)” issue. (As has been repeatedly pointed out, just insistently ignored in the rhetorical intent to seize upon a dismissive talking point.)

There is no elephant in the room.

Apart from . . . the usual one design objectors generally refuse to address, selective hyperskepticism.

But also, RTH imagines there is a whole field of needles, refusing to accept that many relevant complex entities are critically dependent on having the right parts, correctly arranged, coupled and organised in order to function.

That is, there are indeed empirically and analytically well founded narrow zones of functional configs in the space of possible configs. By far and away most of the ways in which the parts of a watch may be arranged — even leaving off the ever so many more ways they can be scattered across a planet or solar system– will not work.

The reality of narrow and recognisable zones T in large spaces W beyond the blind sampling capacity — that’s yet another concern — of a solar system of 10^57 atoms or an observed cosmos of 10^80 or so atoms and 10^17 s or so duration, is patent. (And if RTH wishes to dismiss this, let him show us observed cases of life spontaneously organising itself out of reasonable components, say soup cans. Or, of watches created by shaking parts in drums, or of recognisable English text strings of at least 72 characters being created through random text generation . . . which last is a simple case that is WLOG, as the infographic points out. As, 3D functional arrangements can be reduced to code strings, per AutoCAD etc.)

Finally, when the material issue is sampling, we do not need to generate grand probability calculations.

The proverbial needle in the haystack
The proverbial needle in the haystack

For, once we are reasonably confident that we are looking at deeply isolated zones in a field of possibilities, it is simple to show that unless a “search” is so “biased” as to be decidedly not random and decidedly not blind, only a blind sample on a scope sufficient to make it reasonably likely to catch zones T in the field W would be a plausible blind chance + mechanical necessity causal account.

But, 500 – 1,000 bits (a rather conservative threshold relative to what we see in just the genomes of life forms) of FSCO/I is (as the infographic shows) far more than enough to demolish that hope. For 500 bits, one can see that to give every one of the 10^57 atoms of our solar system a tray of 500 H/T coins tossed and inspected every 10^-14 s — a fast ionic reaction rate — would sample as one straw to a cubical haystack 1,000 LY across, about as thick as our galaxy’s central bulge. If such a haystack were superposed on our galactic neighbourhood and we were to take a blind, reasonably random one-straw sized sample it would with maximum likelihood be straw.

As in, empirically impossible, or if you insist, all but impossible.

 

It seems that objectors to design inferences on FSCO/I have been reduced to clutching at straws. END

Comments
Gordon Davisson: Before going to the next argument, I would like to point out that, in your argument about how big the functional space is (let's call it the "inflating the functional space" argument), you have not addressed an important comment that I have made: I paste it again here: "And there is another important point which is often overlooked. 543 bits (mean complexity) means that we have 1:2^543 probabilities to find one superfamily in one attempt, which is already well beyond my cutoff of 150 bits, and also beyond Dembski’s UPB of 520 bits. But the problem is, biological beings have not found one protein superfamily once. They have found 2000 independent protein superfamilies, each with a mean probability of being found of 1:2^543. Do you want to use the binomial distribution to compute the probability of having 2000 successes of that kind?" Now, let's make a mental experiment, and let's suppose that the functional islands are so frequent and big that we can fins a functional sequence out of, say, 10^10 random sequences. You will agree that this is an exaggeration, even more optimistic than the false estimates I have discussed preciously. Well, that would be the probability of finding (in one attempt) one functional island. If you agree (as you seem to agree) that we have at least 200 unrelated and independent functional islands in the proteome (the superfamilies), that means that the "successful" search has happened 2000 times independently. So, even in this highly imaginary scenario, the probability of getting 2000 successes would be approximately 10^20000. Quite a number, certainly untreatable by any realistic probabilistic system with realistic probabilistic resources. A number with which no sane scientist wants to deal. I would appreciate a comment on that, in your future answers.gpuccio
September 4, 2014
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Gordon Davisson: Let's go on: "So what’s the actual density of functional sequences?" You say: "Short answer: I don’t know." Which, since Socrates, has always been a good statement. :) But you go on:
Some quick googling turned up a couple of sources on the subject: Douglas Axe’s paper “Estimating the prevalence of protein sequences adopting functional enzyme folds“, which (if I’m reading it right) puts the overall density of functional sequences at 1 in 10^64 (and those corresponding to a particular function at 1 in 10^77). This is actually fairly close to my made-up figure, though it’s purely coincidence! It’s also significantly beyond what evolution can realistically achieve (at least as far as I can see), but far higher than you’d think from looking at the Fits values for individual genes. If Axe’s figure is right (and I’m not missing something), evolution has a big problem. But Axe’s also may be far from the real value.
And then you quote Arthur Hunt's famous statement which opposes to Axe's "reverse" estimate of a folding/function sequence out of 10^70, the idea that "What is interesting is that the forward approach typically yields a “success rate” in the 10^-10 to 10^-15 range – one usually need screen between 10^10 -> 10^15 random sequences to identify a functional polymer. This is true even for mRNA display. These numbers are a direct measurement of the proportion of functional sequences in a population of random polymers, and are estimates of the same parameter – density of sequences of minimal function in sequence space – that Axe is after." Well, I have great respect for Arthur Hunt, but what he says here is simply wrong. It is not true that according to data there is an "uncertainty" in the quantification of foldung/functional sequences in random libraries. The simple truth is that Axe's data (and those of some other, who used similar reverse methodology) are true, while the forward data are wring. Not because the data themselves are wrong, but because they are not what we are told they are. The most classical paper about this froward approach is the famous Szostak paper: Functional proteins from a random-sequence library http://www.nature.com/nature/journal/v410/n6829/pdf/410715a0.pdf I have criticized that paper in detail here some time ago, so I will not repeat myself. The general idea is that the final protein, the one they studies and which has some folding and a strong binding to ATP, is not in the original random library of 6 * 10^12 random sequences of 80 AAs, but is derived through rounds of random mutation and intelligent selection for ATP binding from the original library, where only a few sequences with very weak ATP binding exist. Indeed, the title is smart enough: "Functional proteins from a random-sequence library" (emphasis added), and not "Functional proteins in a random-sequence library". The final conclusion is ambiguous enough to serve the darwinian propaganda (which, as expected, has repeatedly exploited the paper for its purposes): "In conclusion, we suggest that functional proteins are sufficiently common in protein sequence space (roughly 1 in 10^11) that they may be discovered by entirely stochastic means, such as presumably operated when proteins were first used by living organisms. However, this frequency is still low enough to emphasize the magnitude of the problem faced by those attempting de novo protein design." Emphasis mine. The statement in emphasis is definitely wrong: the authors "discovered" the (non functional) protein in their library by selecting weak affinity for ATP (which is not a function at all) and deriving from that a protein with strong affinity (which is a useless function, in no way selectable) by RV + Intelligent selection (for ATP binding). That's why the bottom up studies like Szostak's tell us nothing about the real frequency of truly functional, and especially naturally selectable proteins in a ranodm library. That's why they are no alternative to Axe's data, and that's why Hunt's "argument" is simply wrong. Probably unaware of all that, you go on in good faith:
If the 10^-10 to 10^-15 range is right, then we’ve got clear sailing for evolution. Now, I’m not going to claim to understand either Axe or Hunt’s reasoning in enough detail to make any claim about which (if either) is right, or what that tells us about the actual number. What I will claim is that getting a solid upper bound on that density is necessary for your reasoning to work. And there’s no way to get that from a specific gene’s Fits value (even if it is really large, like Paramyx RNA Polymerase).
I think I have fully answered those points, both in this post and in the previous one. So, I will not repeat myself. Finally, as an useful resource, I give here a reference to a paper about protein engineering and mRNA display: De novo enzymes: from computational design to mRNA display http://www.cbs.umn.edu/sites/default/files/public/downloads/TibTech%20Review%20with%20Cover.pdf Please, look in particular at Box 2 and Figure 1. of which I quote here the legend:
Figure 1. General scheme for enzyme selection by mRNA display. A synthetic DNA library is transcribed into mRNA and modified with puromycin. During the subsequent in vitro translation, this modification creates a covalent link between each protein and its encoding mRNA. The library of mRNA-displayed proteins is reverse transcribed with a substrate-modified primer, thereby attaching the substrate to the cDNA/RNA/protein complex. Proteins that catalyze the reaction of the substrate modify their encoding cDNA with the product. Selected cDNA sequences are amplified by PCR and used as input for the next round of selection.
Emphasis mine.gpuccio
September 3, 2014
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For those interested in reading gpuccio’s insightful comments on the dFSCI concept, here are the post #s within this thread: 133, 140, 146, 149, 152, 173-175, 189, 190.Dionisio
September 2, 2014
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Gordon Davisson: "Theoretical probability calculation (aka the probability of what?):" You say: "First, since you’re mainly interested in the origin of completely new genes (e.g. the first member of a superfamily), I agree that selection doesn’t help (at least as far as I can see — there’s a lot I don’t know about evolution!)." I think you see very well. This sets the field for the following discussion, so let's understand well what it means. There are, IMO, only two ways to try to counter ID arguments about the origin of protein superfamilies, especially in the form I generally use. The first is to rely on NS to overcome the probabilistic barriers. That is easily shown as non scientific, because there is no reason, either logical or empirical, to believe that complex functions in general, and functional proteins in particular, can be deconstructed as a general rule (for proteins, I would say not even occasionally) into simpler steps which are both functional and naturally selectable. The complete absence of ant empirical evidence of functional intermediates between protein superfamilies (IOWs, their absolute separate condition at sequence level) is the final falsification of that approach, as I have argued many times. Of course, neo darwinists try all possible excuses to explain that, but I am very much sure that they fail miserably. Luckily, you did not follow that approach, so we need not spend further time on it. The second approach is to deny that protein superfamilies really exhibit dfSCI. There is only a way to do that: assume that the search space of protein sequences is really full of functional islands, big islands and archipelagos, maybe whole continents. I will cal this approach "the myth of frequent function", the idea that some swimming can easily bring us in view of land. This is the way you take in your post, do I will try to show why it is a myth. First of all, you make an interesting distincion between function and functional islands, saying that each function has many islands. There are two possible ways to see that: a) Each basic superfamily is made of many clustered islands, sequence related. For example, a superfamily can include different families, and each family can include different proteins. Sometimes the families are very related at sequence level, other times they are not. This is true. The superfamily is a level of grouping that I usually use because superfamilies are certainly unrelated at sequence level. But the same discourse can be done for folds (which are about 1000), or for families (which are about 4000). the reasoning is rather similar. Nor does considering 4000 islands of families instead of 2000 of superfamilies change much. Individual proteins are many more, but most of them are present in great number of homologues. So, even if we consider an island for each protein we are still with a number of islands which is not really huge. The fact is, the 2000 superfamilies represent clusters which, being unrelated at sequence level, are certainly very distant in the ocean of the search space. b) Another aspect is that the same function can be implemented from different islands, even rather unrelated at sequence level. We know that there are structures, even individual proteins, which are very similar as structure and function, but share only very limited homology. That os more or less the concept of the "rugged landscape, and we will go back to it in our discussion. I has avoided this point in my first round of pots for simplicity, but it is indeed a very strong point in favor of ID theory. Now, I say that the concept of "frequent function" is a myth. Why? Because all that is known denies it. And neo darwinists are forced to defend it with false reasonings. You make your first point here:
Here I disagree. As I said, for your approach it’s not the number of functions that matter, it’s the number of islands; and for that, I’m pretty sure that 2000 is a vast, vast underestimate. First, because that’s the number we know of; we have no reason to think it’s anything but a miniscule subset of what exists. Second, because (at least as I understand it), the Fits value would count the size of each different gene in a superfamily (rather than the size of the superfamily group as a whole), so you’d also have to multiply by the number of possible (again, not just known) functional islands in that superfamily’s range. More technically: the number of known functional islands is a lower bound on the total number of existing (in sequence space) functional islands; but for your argument you need an upper bound, so that you can get an upper bound on the overall density of functional sequences.
I want to make a first counter point which is very important. You betray here a methodological error which is almost the rule in our debate (on the side of neo darwinists), but is an error just the same. I tried to point at that error in one of my posts to you, when I said: "I would like to remind here that ID is trying, at least, to offer a quantitative approach to the problem of probabilities in biological evolution. That is not only the duty of ID: it is the duty of anyone who is interested in the problem, most of all the duty of those who believe that the neo darwinian model is a good solution. After all, it’s the neo darwinian model which is vastly based on RV (a probabilistic system). So, it is the cogent duty of neo darwinists to show that their random explanation is appropriate. They have repeatedly avoided any serious analysis of that aspect, so ID is trying to do that for them. " IOWs, it's not so much that for my argument I need an upper bound, so that I can get an upper bound on the overall density of functional sequences. The real problem is that darwinists absolutely need some empirical support to their probabilistic model, and they have none. However, let's try to do the work for them. You say: "I’m pretty sure that 2000 is a vast, vast underestimate. " No, it is not. "First, because that’s the number we know of; we have no reason to think it’s anything but a miniscule subset of what exists. " No reason? Let's see. 1000 superfamilies (more or less) are already found in LUCA. Many of them are very complex, for example the domains in alpha and beta subunits of ATP synthase. So, those were "found" in the first few hundreds of million years of our planet's life. The other 1000 (more or less) emerged in the rest of natural history, with slowing rate. Why? Why, if that is just "a minuscule subset of what exists"? If there are so many thousands of different foldings and superfamilies potentially useful in the existing biological context, ready to be found, how is it that the powerful mechanism of RV and NS have been slowing so much their efficiency in the last, say 4 billion years, after the frenetic "luck" of the beginning? How is it that we have not a lot of other molecular machines which generate ATP from proton gradients? Why haven't we simpler machines which do that? Was it really necessary to have all those chains, each of them so conserved? The simple truth is, there is absolutely no reason to think that "it’s a minuscule subset of what exists". That is only wishful thinking of neo darwinists, supported by nothing. But let's go on. You say that the important point is the total number of islands. Firts of all, the important point is rather the total surface of islands which correspond to an useful, naturally selectable function. And that is much, much less. But it is not true just the same. The probability computed in that way would just be the probability of finding one of those functions. Now, lets say that the total "selectable" surface is, say 4 square Kms. And let's say, for the sake of reasoning, that it is made of two sub archipelagos: ankyrin (46 Fits) and Paramyx RNA Pol (1886 Fits) That means that, of those 4 square Kms of islands, only 1:10^575 (more or less) of the surface is Paramyx RNA Pol, while the rest is ankyrin. Now, if you find ankyrin, your reasoning could be fine. But are you really saying that, if you find Paramyx RNA Pol, that is easily explained by the facts that ankyrin is much more likely, and is functional too? What a way of using probability is that? What a scientific methodology is that? If you find Paramyx RNA Pol, that's what you have to explain. So, as I hope you can see, dFSCI is very useful and appropriate, because it allows us to distinguish between relatively more "likely" results and results which cannot be explained at all by RV. So again, let's say for the sake of argumentation that we can explain ankyrin (that I don't believe). In which way does that help to explain Paramyx RNA Pol, or ATP synthase?. So, I will keep my scientific dFSCI and the scientific data from Durston, and I will leave the unscientific wishful thinking and imagination to neo darwinists. You are free to make your choice. More in next post (tomorrow, I suppose).gpuccio
September 2, 2014
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Gordon Davisson: Thank you for your detailed answer. I don't want (and I suppose you don't want either) to start an unending discussion repeating the same points. Therefore, I will try to comment only on the points in your post which allow some new clarification or argument. For the rest, I stick to what I have already said. In your introduction, you say: "I think I’d better concentrate on what I see as the main issue here: whether your reasoning is sufficient to show that RV+NS are insufficient to produce new functional proteins (de novo, not variants)." And that's perfectly fine. "I think I understand how you’re using theoretical probability calculations and experimental results (“we haven’t seen it happen”) much better now." I am happy of that. "I was initially under the impression your argument was like Dembski’s: entirely theoretical. After our previous conversation, I switched to the understanding that it was entirely based on the experimental results. Now I understand you’re using both, and I think I’ve got the hang of how you’re interrelating them." Good. I agree that I am using both, like everybody usually does in empirical science. We build theories to explain facts. "And perhaps not surprisingly, don’t think you can fully support your conclusion in either way. Let me start with the theoretical probability calculation" OK. So, to the first point.gpuccio
September 2, 2014
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F/N: The old switcheroo game, sadly, continues. In the teeth of a very specific discussion of the search challenge of a configuration space of relevant scale, and a very definite focus on functionality dependent on configuration leading to isolated islands of function and relatively tiny possible scope of search, the "answer" is to pretend that search does not confront a needle in haystack challenge by pretending that probability calculations rather than search challenge is pivotal. FSCO/I is real, as real as the text in posts in this thread. As real as the coded algorithmic info in D/RNA chains. As real as the fold-function constraints on proteins in AA sequence space leading to deeply isolated islands of function manifested in the real-world distribution of proteins (which will reflect dominant processes of exploration). As real as, the key-lock fit tight coupling and integration to form 3-d functional structures in both the world of life and that of technology (which, per AutoCAD etc can be converted into coded strings). And the blind sample challenge is real, as can be seen from the utter absence of a credible, empirically grounded account of origin of cell based life after decades of increasing embarrassment. (That's why a typical talking point today -- never mind Miller-Urey etc appearing as icons of the lab coat clad mythology in science textbooks -- is to try to run away from the root of the tree of life, declaring it somehow off limits and not part of "Evolution.") The blind search challenge is real, as real as needing to account incrementally for the rise of novel proteins and regulatory circuits as well as 3-d key-lock functional organisation to form the dozens of main body plans of life, with ever so many deeply isolated protein fold domains in the AA sequence space. The blind search challenge is real, as real as the need to account for reasonable mutation rates, population genetics, time to fix mutations once found, and to discover incremental ever-improving forms across the branches of the tree of life. Where, it remains the case that there is exactly one empirically warranted causal force behind FSCO/I . . . design -- with trillions of cases in point, just on the Internet -- so, the pretence that this is some mysterious, hard to find, hard to test thing is strawman tactic nonsense. Apart from the sort of tests and empirical results we have seen with micro-organisms -- the most favourable case for evolution [and no the ducking, dodging, obfuscation and pretence that supportive results undermine the findings speak inadvertent volumes to the strength of the point (remember, up to 10^12 malaria parasites in a victim and a chronic condition that recurs again and again providing a pool of experiments with 10's or 100's of millions of walking "labs" across Africa and Asia . . . 10^20 reproductive events being easily on the table] -- we can easily see the nature of the challenge in light of findings on random text generation tests that we can for convenience summarise by clipping Wiki testifying against interest in its infinite monkeys theorem article:
The theorem concerns a thought experiment which cannot be fully carried out in practice, since it is predicted to require prohibitive amounts of time and resources. Nonetheless, it has inspired efforts in finite random text generation. One computer program run by Dan Oliver of Scottsdale, Arizona, according to an article in The New Yorker, came up with a result on August 4, 2004: After the group had worked for 42,162,500,000 billion billion monkey-years, one of the "monkeys" typed, "VALENTINE. Cease toIdor:eFLP0FRjWK78aXzVOwm)-‘;8.t" The first 19 letters of this sequence can be found in "The Two Gentlemen of Verona". Other teams have reproduced 18 characters from "Timon of Athens", 17 from "Troilus and Cressida", and 16 from "Richard II".[24] A website entitled The Monkey Shakespeare Simulator, launched on July 1, 2003, contained a Java applet that simulates a large population of monkeys typing randomly, with the stated intention of seeing how long it takes the virtual monkeys to produce a complete Shakespearean play from beginning to end. For example, it produced this partial line from Henry IV, Part 2, reporting that it took "2,737,850 million billion billion billion monkey-years" to reach 24 matching characters: RUMOUR. Open your ears; 9r"5j5&?OWTY Z0d...
In short, successes are well short of the 500 - 1,000 bit threshold and much further short of the jump from 100 - 1,000 kbits for a first genome (itself a major OOL challenge), to 10 - 100+ mns credibly required for new body plans. We still have only 10^57 atoms in our solar system, mostly H in the sun. We still have only ~ 10^80 atoms in the observed cosmos. We still have 10^-14 s as a reasonable fast rxn rate and perhaps 10^17 s. The realistic config space challenges utterly overwhelm the threshold case of a blind one straw sized sample from a cubical haystack as thick as our galaxy. And no, one does not require a wild goose chase after every possible mechanism that hyperskeptical objectors can dream up when one easily knows that the scope of search of the toy example of giving every atom in the solar system 500 coins and flipping, observing and testing a state each 10^14 times per second -- comparable to the oscillation frequency of visible light -- utterly overwhelms what is possible in a Darwin's pond or in a reproducing population of cell based life forms. The coin flipping atoms search, whether scatter-shot random sample or incremental random walk from blind initial configs, still face only doing a sample of possibilities that is as a straw to a cubical haystack 1,000 LY across, for our solar system. And a similar calc for 1,000 bits ends up doing a one straw sample to a haystack that would swallow up our 90 bn LY across visible cosmos and not notice it. That is, we are confidently able to apply the premise of statistics that a small, blind sample of a large population tends strongly to reflect its bulk, not isolated unrepresentative clusters. Which, BTW is exactly the line of reasoning that grounds the statistical form of the 2nd law of thermodynamics. In short, the empirical result is backed up by the blind search needle in haystack analysis summarised in the infographic in the OP and elsewhere. The twists, side-slips, side tracks and more in the end only serve to underscore the force of the point. The ONLY empirically warranted, analytically plausible explanation for FSCO/I is design. For reasons that are not that hard to figure out. And so, we have excellent reason to hold that until SHOWN otherwise, FSCO/I is a strong, empirically grounded sign of design as cause. KFkairosfocus
September 2, 2014
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Hi, all! Sorry I'm late in replying (as usual), and I'll probably be even slower in the future (going on a trip, and I may have intermittent time & Internet access). I won't have time to reply to everything that's been said, so I want to concentrate on what I see as the core of my digreement with gpuccio. If I have time later (hah!), I'll try to reply to Upright Biped and kairosfocus as well. GP: You've covered a lot of ground in your replies; thanks for the extensive explanation! I agree with some of it, disagree with some, and semi-agree with a lot; all of which could easily spawn extensive discussion (the question of whether ID and RV+NS are really the only relevant hypotheses is ... a very complicated question), so even though many are worth discussing, I think I'd better concentrate on what I see as the main issue here: whether your reasoning is sufficient to show that RV+NS are insufficient to produce new functional proteins (de novo, not variants). I think I understand how you're using theoretical probability calculations and experimental results ("we haven't seen it happen") much better now. I was initially under the impression your argument was like Dembski's: entirely theoretical. After our previous conversation, I switched to the understanding that it was entirely based on the experimental results. Now I understand you're using both, and I think I've got the hang of how you're interrelating them. And perhaps not suprisingly, don't think you can fully support your conclusion in either way. Let me start with the theoretical probability calculation. Theoretical probability calculation (aka the probability of what?): First, since you're mainly interested in the origin of completely new genes (e.g. the first member of a superfamily), I agree that selection doesn't help (at least as far as I can see -- there's a lot I don't know about evolution!). But I don't agree that the other factors I mentioned -- the number of different functions and the number of "islands" per function -- are irrelevant. The format I used to describe them is derived from Dembski's formulation, not to your approach, so let me restate my (again, completely made-up) example in a more relevant format: In my example, to keep the math simple, we're looking at genes are 750 base pairs long, and of those 250 are fully required for the gene’s function, so the genes have 500 Fits. That means the probability of any particular one of these genes arising at random is 1 in 2^500 (about 10^150). But (again, in my example) there are 2^300 such functional islands here. In my previous discussion I broke that into 2^50 functions, each with 2^250 islands -- but this distinction is only relevant for Dembski's formulation, for your approach (again, at least as I understand it) it's really the total number of islands that matter. You could also break them up into superfamilies, or whatever, but it's really the total number that matters. That means that about 1/2^200 (about 10^60) of the sequence space corresponds to a function. That's not high enough that evolution is likely to find any such sequence at random (I estimate there've been something like 10^45 bacteria alive since the origin of life), but it's far far higher than the 10^150 number you'd get just from looking at a particular island (and remember it's completely made up -- more on that later). My first, and most critical claim, is that it's that the probability of hitting any island, not the probability of hitting a specific island that matters. Actually, a better way to describe this is the density of functional sequences in the overall sequence space. Now, you made a couple of points about this probability:
Another big problem is that the “any possible function” argument is not really true. Even if we want to reason in that sense (which, as explained in my point a, is not really warranted), we should at most consider “any possible function which is really useful in the specific context in which it arises”.
Agreed. Many (/most) functional sequences that arise will be irrelevant to the organism they happen to arise in, and thus not be selected for. Also, even most of the ones that are beneficial to the organism will still be lost to genetic drift before selection really has a chance to kick in. So there are several more factors to include for a full calculation.
The number of really useful functions, that can be naturally selected in a specific cellular context, is certainly smnall enough that it can be overlooked. Indeed, as we are speaking of logarithmic values, even if we considered the only empirical number that we have: 2000 protein superfamilies that have a definite role in all biological life as we know it today, that is only 11 bits. How can you think that it matters, when we are computing dFSCI in the order of 150 to thousands of bits?
Here I disagree. As I said, for your approach it's not the number of functions that matter, it's the number of islands; and for that, I'm pretty sure that 2000 is a vast, vast underestimate. First, because that's the number we know of; we have no reason to think it's anything but a miniscule subset of what exists. Second, because (at least as I understand it), the Fits value would count the size of each different gene in a superfamily (rather than the size of the superfamily group as a whole), so you'd also have to multiply by the number of possible (again, not just known) functional islands in that superfamily's range. More technically: the number of known functional islands is a lower bound on the total number of existing (in sequence space) functional islands; but for your argument you need an upper bound, so that you can get an upper bound on the overall density of functional sequences. So what's the actual density of functional sequences? Short answer: I don't know. Some quick googling turned up a couple of sources on the subject: Douglas Axe's paper "Estimating the prevalence of protein sequences adopting functional enzyme folds", which (if I'm reading it right) puts the overall density of functional sequences at 1 in 10^64 (and those corresponding to a particular function at 1 in 10^77). This is actually fairly close to my made-up figure, though it's purely coincidence! It's also significantly beyond what evolution can realistically achieve (at least as far as I can see), but far higher than you'd think from looking at the Fits values for individual genes. If Axe's figure is right (and I'm not missing something), evolution has a big problem. But Axe's also may be far from the real value. There's a response by Arthur Hunt over at the Panda's Thumb ("Axe (2004) and the evolution of enzyme function"), which says:
Studies such as these involve what Axe calls a “reverse” approach – one starts with known, functional sequences, introduces semi-random mutants, and estimates the size of the functional sequence space from the numbers of “surviving” mutants. Studies involving the “forward” approach can and have been done as well. Briefly, this approach involves the synthesis of collections of random sequences and isolation of functional polymers (e.g., polypeptides or RNAs) from these collections. Historically, these studies have involved rather small oligomers (7-12 or so), owing to technical reasons (this is the size range that can be safely accommodated by the “tools” used). However, a relatively recent development, the so-called “mRNA display” technique, allows one to screen random sequences that are much larger (approaching 100 amino acids in length). What is interesting is that the forward approach typically yields a “success rate” in the 10^-10 to 10^-15 range – one usually need screen between 10^10 -> 10^15 random sequences to identify a functional polymer. This is true even for mRNA display. These numbers are a direct measurement of the proportion of functional sequences in a population of random polymers, and are estimates of the same parameter – density of sequences of minimal function in sequence space – that Axe is after.
If the 10^-10 to 10^-15 range is right, then we've got clear sailing for evolution. Now, I'm not going to claim to understand either Axe or Hunt's reasoning in enough detail to make any claim about which (if either) is right, or what that tells us about the actual number. What I will claim is that getting a solid upper bound on that density is necessary for your reasoning to work. And there's no way to get that from a specific gene's Fits value (even if it is really large, like Paramyx RNA Polymerase). The empirical argument: overview Now, I want to turn to the empirical side of your argument: that we've never seen dFCSI produced without intelligence, which is evidence that it cannot be produced without intelligence. There are a number of complications here, but I want to concentrate on what I see as the big problem with this: we can only infer the nonexistence of something from not detecting it if we should expect to detect it. We've never actually seen an electron; but given how small they are, we don't expect to be able to see them, so that's not evidence they don't exist. We've only seen atoms fairly recently, but that didn't count as evidence against their existance for the same reason. On the other hand, if we'd gotten to the point where we should be able to see them, and hadn't... then that would have been evidence against them. We've never seen a supervolcano erupt; that should be really easy to detect, but they're rare enough that we don't expect to have seen one anyway. In the case of completely novel genes, I'd expect them to be both rare and hard to detect, so making an empirical case against them is going to be very difficult. But let me take care of a minor issue. I'm going to be referring fairly extensively to the Lenski experiment, so I want to clarify my stance on its results. You said:
First of all, I hope we agree that no dFSCI at all has emerged from the Lenski experiment. At most, loss of function has come out. And the small regulatory change about citrate.
I have to disagree about the loss of function statement. At least as I understand it, a new functional connection was made between duplicates of two pre-existing functional elements (a gene and a regulatory sequence), creating a new function. This is certainly not a new functional gene (your main concern), but I'd argue it's a gain of function and (at least as I understand it) a gain of dFSI (the continuous measure) if not dFSCI. The empirical argument: rarity vs. sample size The basic idea here is that in order to empirically show that something has a probability less than p, we need more than 1/p trials (with some complications if events aren't independent, probability isn't constant, etc -- I'll ignore these). AIUI the Lenski experiment has now been running for about 60,000 cell generations, with the population having recently reached 60,000. If I simplify the situation and assume the population was constant, that's 3.6*10^9 cells in the study. That means we only expect to see any event that has a probability over 1 in 3.6*10^9. If something doesn't happen over the course of a Lenski-scale experiment, the most we can safely say is that its probability is below 1 in 10^8 or so, and events with probabilities below 10^11 are unlikely to occur in an experiment like this. But the wild population of bacteria is estimated at around 10^30. That means events with probabilities between 1 in 10^11 and 10^30 will be unlikely to show up in Lenski's experiment, but occur constantly in the wild. And events with probabilities down to 1 in 10^45 are so are likely to have occured over the course of life on Earth. Evolution's sample size is far larger than Lenski's, or indeed any plausiple controlled experiment, and hence it can "see" events of much lower probability than we can. The empirical argument: detection efficiency As you pointed out, just because a new functional sequence arises doesn't mean it'll be selected and spread through the population. Metaphorically speaking, evolution doesn't "see" every new sequence. But we won't "see" everything that happens either. In Lenski's experiment, he doesn't scan each new bacterium for new genes/functions/etc, he waits for something visible to happen (overall fitness goes up, ability to digest citrate appears, etc), then goes back and looks at what happened. That means that if a new functional sequence arises but doesn't spread through the population (i.e. evolution doesn't "see" it), then Lenski won't see it either. That means that his detenction efficiency is necessarily lower than evolution's. It also means that for every new benefificial mutation that he detected, there are probably lots of others that occurred, but didn't spread and hence weren't detected. And for every new function (cit*) that was detected, there were probably several that weren't relevant to the bacteria, or died out due to drift, or something like that. Now, you could imagine doing a more detailed experiment than Lenski's: one where you actually examined each new bacteria for new genes, functions, etc... but doing that at anything like Lenski's scale would require a huge investment of time, money, effort, etc. I'd argue there's a tradeoff here: the more detailed observations you make, the smaller scale you can afford to work in. One of the reasons I'm using Lenski as my example here is that I think he's in about the sweet spot of this tradeoff, but if you know of a better experiment please point me to it. The empirical argument: detecting a process There's actually another reason I'm picking out Lenski's experiment here: his method lets him track the history of significant events (like the cit* phenotype), not just the event itself. Suppose we had an experiment without this feature, and we saw some part of the process of the emergence of a completely new gene. Would we be able to detect this as a significant event? Let's take a look at a sequence of possible events leading to a new gene: 1) A series of mutations take a particular genetic sequence on a random tour of sequence space... that happens to wind up "next to" a functional sequence. If this happened in an experiment, I'm pretty sure nobody would realize it'd come within a mutation of a functional sequence, so nothing significant would be detected. 2) Another mutation changes it into the functional sequence. This is really the event we're looking for, but if we just saw this single step (an almost-functional sequence mutating into a functional one), the probability of that mutation is going to be pretty high (in a reasonable sample size). Also, the gene probably has a pretty low level of function, so its Fits value (if we could calculate it at this point) would be fairly small. Would this be considered new dFSCI? 3) Selection spreads the new functional mutation through the population... If we just saw this part alone, we wouldn't necessarily realize it was even a new gene, so wouldn't categorize it as new dFSCI. 4) Additional mutations optimize the gene's function, and also spread through the population. Again, just microevolution, not new dFSCI. But it does increase the Fits value, maybe pushing it beyond the relevant probabilty threshold... ...If I'm understanding this right, that means there aren't very many experiments that're going to be useful for looking for new dFSCI. That is, you have a very limited set of experiments to draw data from (and aggregate sample sizes from, etc). Which brings me to a final question: what experiments do you consider relevant for the "no dFSCI" argument? What do you know about their sample sizes, expected detection efficiency, etc?Gordon Davisson
September 2, 2014
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UB, Mung: I will patiently wait for my turn. No fight between fellow IDists! :)gpuccio
September 1, 2014
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lol roger that. I will put evolutionary algorithms in a queue.Mung
September 1, 2014
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Well...this is either the third or fourth time I've tried to re-engage Gordon on his statement, so you other cats have to wait. :)Upright BiPed
September 1, 2014
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#179 Upright BiPed
Hmmm…I thought we might have heard back from Gordon Davisson by now.
I agree with gpuccio on his post #180:
Maybe he[GD] is busy, and had not the time to read the comments.
Here's a summary list of posts related to GD: GD's most recent post in this thread:
126 Gordon Davisson August 28, 2014 at 6:28 pm
Posts addressed to GD after his latest post:
127 Upright BiPed August 28, 2014 at 10:20 pm 128 gpuccio August 28, 2014 at 10:54 pm 130 kairosfocus August 29, 2014 at 1:39 am 131 gpuccio August 29, 2014 at 2:50 am 133 gpuccio August 29, 2014 at 3:32 am 140 gpuccio August 29, 2014 at 6:10 am 145 Mung August 29, 2014 at 1:50 pm 146 gpuccio August 30, 2014 at 4:30 am 149 gpuccio August 30, 2014 at 7:22 am 152 gpuccio August 30, 2014 at 8:01 am 173 gpuccio August 31, 2014 at 5:44 am 174 gpuccio August 31, 2014 at 7:19 am 175 gpuccio August 31, 2014 at 7:47 am
Dionisio
September 1, 2014
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For those interested in reading gpuccio’s insightful comments on the dFSCI concept, here are the post #s within this thread: 133, 140, 146, 149, 152, 173-175.Dionisio
September 1, 2014
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I trust Gordon is taking a break to find out if evolutionary algorithms really are intelligently designed. :D I suppose though that even that is begging the question, since the claim being made is with respect to functional information beyond some minimum threshold, isn't it?Mung
September 1, 2014
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UB: Maybe he is busy, and had not the time to read the comments. However, I am grateful that he offered me the occasion to elaborate on some not so obvious aspects. I would certainly appreciate very much his comments, I hope he can get in touch sooner or later, in the unending flow of information that is this blog! :)gpuccio
September 1, 2014
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Hmmm...I thought we might have heard back from Gordon Davisson by now.Upright BiPed
September 1, 2014
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Dionisio: Yes, "produced". It's just typos, unfortunately I have not always the time to re-read everything with care. And the error signal does not work when the alternative word is a correct english word. Maybe I just subconsciously hope that random errors will make my posts better! :)gpuccio
August 31, 2014
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For those interested in reading gpuccio’s interesting explanation of the dFSCI concept, here are the post #s within this thread: 133, 140, 146, 149, 152, 173-175.Dionisio
August 31, 2014
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gpuccio @ 173
Yes, my point is that it has never been observed to be produces without conscious intelligence (empirical observation).
Did you mean "to be produced" ? I have problems with the auto-correcting feature when writing posts too.Dionisio
August 31, 2014
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Gordon Davisson: Finally, you say:
It also seems to conflict with the empirical approach I thought you were taking. Suppose we observed dFSCI appearing (in, say, something like the Lenski bacteria experiment): would that be evidence that it can be produced naturally, or evidence that some intelligence intervened to add it? Without some way of distinguishing the two, I don’t see how the claim that dFSCI only comes from intelligence can be empirically tested.
First of all, I hope we agree that no dFSCI at all has emerged from the Lenski experiment. At most, loss of function has come out. And the small regulatory change about citrate. Now, I will be very clear: if a new protein exhibiting clear dFSCI emerged in the Lenski experiment (and we could be sure that it did not exist before in the system), that would be a serious blow for ID theory and especially for my version of it. A very serious blow. Maybe not the end of it, but a great support to the neo darwinian model (I would say, the first empirical support). Why? Because the Lenski experiment is a controlled experiment in a controlled system, and there is absolutely no reason to assume that the biological designer would "intervene" in it, maybe to prove Lenski right and demonstrate that he does not exist. We must be serious in our scientific approach. The purpose of experiments is not to prove our personal ideas, but to find what is true. The Lenski experiment has been designed (well, I must say) to support the neo darwinian model. If it succeeds, it will support it. If it fails... no need to comment further. Up to now, it has failed. But let's suppose that it succeeds. Let's suppose that something like ATP synthase emerges in the system, something that did not exist before. A serious blow for ID theory, no doubt. But also a fact that requires explanation. Now, luckily, the Lenski experiment is well designed, and it would allow us not only to observe the final event, but also to try to understand it. Indeed, that's why they freeze intermediate states, so that they can study them retrospectively, to understand what happens and why it happens. So, if our new complex functional protein emerges, we can try to understand the "path" that brings to it. Now, two things are possible: a) The path is compatible with the neo darwinian model. IOWs, we find a lot of functional intermediates, each of them naturally selectable in the context of the experiment, and each transition is in the range of the probabilistic resources of the system. OK, so the neo darwinian explanation is supported, at least in this case. A very serious blow to ID theory. b) There is no neo darwinian path. The protein just emerges, more or less gradually, without any selectable intermediate, and against the probability laws. This would be a very interesting situation. Still, I would not argue for an intervention of the designer: that would be ad hoc, and is against my principles. But I would say that we would have a fact that cannot reasonably be explained with our present understanding of the laws of physicals, biochemistry and biology. If confirmed, that type observation would probably require a thorough rethinking of all that we know. The design hypothesis, in this regard, is frankly more "conservative". But, luckily (at least for me), such an event not only has nor been confirmed, but has never been observed. So, at present, the Lenski experiment (like all the other evidence from biological research) fully supports ID theory. Well, I believe that's all. You final remark is:
Therefore, I am now confused. Is the source of my confusion clear enough that you can see what I’m worried about, and clarify it for me?
I have sincerely tried. With some detail. I hope you have been able to read my posts (just as a summary: #131, 133, 140, 146, 149, 152, 173, 174 and this one).gpuccio
August 31, 2014
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Gordon Davisson: You say:
All of the functional complexity in the biological world, and Dembski isn’t sure if it actually qualifies as CSI (in his sense) because of unknowns like the ones I’ve been harping on. If he can’t tell for sure if a human exhibits CSI, what hope have you of telling if this new mutation you just observed does?
I can't speak for Dembski. Maybe I am mentally obtuse, but I really believe that I can say that ATP synthase, and many other proteins, do exhibit dFSCI, and therefore CSI (in its basic meaning) and therefore allow a design inference as the best available explanation for their origin. And I have tried to explain why. You say:
…but that sounds more like the sort of argument from theoretical probability that Dembski’s working toward, and it means you do need to take those messy unknowns into account in your calculations.
Well, I have tried to explain what I think of those "messy unknowns", and why, and why I don't think they are so "messy", after all. Nest post (the last one, I hope) is about Lenski.gpuccio
August 31, 2014
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Gordon Davisson: Some more thoughts:
Here’s where your #101 confused me, because it seems to conflict with what I thought I knew about your approach. My understanding from previous discussion (which I admit I haven’t always followed in full detail) was that your argument that dFSCI can’t be produced without intelligence is that it hasn’t been observed to. In order to make a solid case like this, you really have to be able to go on and say “…and if it could be produced naturally, we would have observed it.” And that means we must have tested many cases, and (in each of those cases) be able to tell if dFSCI has appeared.
Yes, my point is that it has never been observed to be produces without conscious intelligence (empirical observation). And that is absolutely true. If we exclude the biological world, no example exists of dFSCI which was not designed, while we have tons of dFSCI which is designed by humans. A special case are computers and, in general, algorithms designed by humans. While they can certainly output objects with apparent dFSCI, I believe that it can be easily demonstrated that the dFSCI in the output is related to the dFSCI which had already been inputted into the system. I believe that Dembski's and Marks' work about active information is very good about that. I will not deal further with this aspect because after all it is not my field. Just a simple example will be enough to show my position. A computing algorithm can certainly output strings of increasing functional complexity. A simple case would be a software which outputs the digital figures of pi. If it outputs 10 digits, the search space for that string is 10^10. If it outputs 100 digits, the search space is 10^100. As the target space is always the same (1), the functional complexity apparently increases of 90 orders of magnitude. But: a) The specification is always the same: a string which corresponds to the decimal digits of pi. This is a very important point. Algorithms cannot generate new specifications, because they have no idea of what a function is. They have no notion of purpose (indeed, they have no notion of anything). They can "recognize" and use only what has been in some way "defined" to them in their input, or can be deducted from the initial input according to rules which are already in the initial input too, or can be deducted from it. b) According to the dFSCI concept, if the digits of p can be computed (and they can) and if the computing algorithm is simpler than the output, then we should consider the complexity of the computing algorithm, rather than the complexity of the output. IOWs, if we find in a computing environment a string which corresponds to the first 10^1000 digits of pi, it is probably more likely that the computing algorithm arose by chance in the system, rather than the string itself, and that the string was simply computed (necessity) by the random algorithm. Why? because the complexity of the random computing algorithm is probably lower than 10^1000 bits! Another way to say that is that, when the observed string is computable, we should consider the Kolmogorov complexity instead of the apparent complexity. These consideration can be summed up by saying that computing algorithms cannot generate new original dFSCI (a new functional specification, which requires high complexity to be implemented).gpuccio
August 31, 2014
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KF thank you for the interesting story about the islands.Dionisio
August 30, 2014
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Box @ 168
The problem of form in the organism — how does a single cell (zygote) reliably develop to maturity “according to its own kind” — has vexed biologists for centuries.
...and it has blown my poor mind for the last few yearsDionisio
August 30, 2014
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gpuccio @ 165
A common cognitive problem obviously unites us.
Agree. :)Dionisio
August 30, 2014
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gpuccio @ 166
I hope we can soon discuss more in detail this fascinating issue.
I hope so too! :)Dionisio
August 30, 2014
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Gpuccio and Dionisio I guess your cognitive problem with regard to the magic of RV+NS are much more 'severe' than mine. I'm trying my utmost to descent to your level :) With regard to development procedures and dynamic equilibrium a quote by Stephen Talbott:
The problem of form in the organism — how does a single cell (zygote) reliably develop to maturity “according to its own kind” — has vexed biologists for centuries. But the same mystery plays out in the mature organism, which must continually work to maintain its normal form, as well as restore it when injured. It is difficult to bring oneself fully face to face with the enormity of this accomplishment. Scientists can damage tissues in endlessly creative ways that the organism has never confronted in its evolutionary history. Yet, so far as its resources allow, it mobilizes those resources, sets them in motion, and does what it has never done before, all in the interest of restoring a dynamic form and a functioning that the individual molecules and cells certainly cannot be said to “understand” or “have in view”. We can frame the problem of identity and context with this question: Where do we find the context and activity that, in whatever sense we choose to use the phrase, does “have in view” this restorative aim? Not an easy question. Yet the achievement is repeatedly carried through; an ever-adaptive intelligence comes into play somehow, and all those molecules and cells are quite capable of participating in and being caught up in the play.
Box
August 30, 2014
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D: Antigua is a good base to move around between the islands, and yes, there are ferry boats that make the run. Barbuda is a very special case, but actually both Montserrat and Antigua are effectively bilingual (there are complications . . . ), with significant Hispanic settlements from the Dominican Republic. You could be understood, or at least could make your way around. Barbuda, BTW, was a slave breeding plantation that was willed in perpetuity to the former slaves and is a sort of commune -- complete with wild deer and actually deer hunting. Pink sand, too! Not to mention the Lagoon, a major eco site and home base for the Magnificent Frigate Bird . . . never mind it is a bit of a pirate bird, it is an awesome creature and a beautiful soaring flier. KFkairosfocus
August 30, 2014
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Box: Indeed, I am trying ti deepen my understanding of what is known about developmental procedures, with the aim of concluding a post about that, and I must say that the scenario is really overwhelming. You have emphasized the right point: the flux of information between epigenome and genome (yes, in that order) seems to be the key, but it is a key that still vastly eludes our understanding. It is absolutely true that living beings are systems which constantly exist in a "far from equilibrium" state which defies any simple explanation (and I am speaking of design explanations, non design explanations are just out of the game from the very beginning). I hope we can soon discuss more in detail this fascinating issue. :)gpuccio
August 30, 2014
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Dionisio: A common cognitive problem obviously unites us. :)gpuccio
August 30, 2014
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Box You and I don't understand the power of the magic 'n-D e' formula RV+NS Apparently KF and GP don't understand it either ;-)Dionisio
August 30, 2014
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