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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
Y'all have blown my mind with that level of math that is above my pay grade. But I would like to understand this you're discussing here. Please, can you use simpler examples to explain the terminology you are using? For example, what could be the information associated with the following text? 039009005009019007024001025010014002030004 Thank y'all in advance for your help with this.Dionisio
August 25, 2014
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>>> 1) An explicit statement of what specification is being used. 2) An explicit statement of what chance hypothesis is being used. 3) A calculation of p(T|H) which actually takes into account all ways that the specification can be met, and calculates their probability under the stated chance hypothesis. This seems key. KF, Is it beyond you?rich
August 25, 2014
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Gordon Davisson, I'm impressed with your command of math. would you be willing to try and calculate the SI of an object, as the regulars here don't seem interested / are unable to? I don't think KFs macro-scale example has any bearing on life, personally.rich
August 25, 2014
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...and apparently I've failed at unicode. The "?S(T)"s throughout my previous comment should be phi_S(T).Gordon Davisson
August 25, 2014
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Hi, KF. I disagree about whether there's an elephant in the room -- actually, in this particular case, I think there are two of them. I think comparing Dembski's CSI metric with Durston et al's Fits metric is an excellent way to illustrate the problems, so I want to concentrate on the differences between them. But first, I have to point out a couple of technical problems with your derivation of the S metric:
Chi = – log2(2^398 * D2 * p), in bits, and where also D2 = ?S(T) Chi = I_p – (398 + K_2), where now: log2 (D2 ) = K_2 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 + K_2) 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)] [...]
I think you've lost track of the meaning of ?S(T) (= D2 = 2^(K_2) ) here. In Dembski's formulation, ?S(T) is a measure of the descriptive complexity of the specification, that is, it's a measure of how verbose you have to be to describe the specification. It has nothing to do with the solar system vs. universe (that's actually what the 398 bits relate to, and they're already taking the entire universe into account), it has to do with the difference between "bidirectional rotary motor-driven propeller" and "bidirectional rotary motor-driven propeller consisting of [...detailed description here...]". Assuming 398 + K_2 =< 500 bits corresponds to assuming K_2 =< 102 bits, which is not quite 13 bytes, or (using Dembski's assumption of a library of 10^5 "basic concepts") a little over 6 concepts. Using Dembski's "basic concept" approach, "bidirectional rotary motor-driven propeller" (4 concepts) would fit easily in this limit, but anything much longer wouldn't. You may be tempted to write this off as a quibble, but I disagree. I think one of the problems people run into when applying CSI is being sloppy about exactly what specification they're using in the analysis. Don't be sloppy; be clear: specify your specifications!
(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.
In this example, you're under the specification complexity limit (3 concepts: "message", "ASCII", "English"), but you're ignoring another important factor: the p in question isn't the probability of that particular event, it's the probability of any event meeting the specification. In this case, those 500 coin flips correspond to 62.5 ASCII characters, and since English is generally estimated to have an entropy of around 1 bit per letter, there are probably about 2^62 meaningful English messages of that length. So p ~= 2^62 / 2^501 = 2^439, and you haven't met the 500-bit threshold. (There's also the problem that in Dembski's formulation, p must be computed under a well-defined chance hypothesis; in your calculation, you're just assuming complete randomness. I'll return to this distinction later.) Ok, that's it for the minor technical quibbles; now let me get to my main topic of interest: contrasting CSI and Fits, and showing that the Fits don't qualify as CSI. There are a number of minor differences between CSI and Fits, but IMO there are only two that really matter: how they approach the Texas sharpshooter fallacy, and how they handle probability calculations. The latter is the one that the EleP(T|H)ant refers to, but let me start with the Texas problem. Elephant #1: the Texas sharpshooter fallacy For those unfamiliar with it, the Texas sharpshooter fallacy refers to drawing a circle around the tightest cluster of bullet holes, and claiming that was the target you were aiming for. In the original version of Dembski's CSI, he avoided this problem by insisting that the target ("specification") be defined independently of the data:
Specifications are the independently given patterns that are not simply read off information. By contrast, the "bad" patterns will be called fabrications. Fabrications are the post hoc patterns that are simply read off already existing information.
(From Dembski's 1997 paper "Intelligent Design as a Theory of Information".) Durston's approach, on the other hand, consists of ... simply reading off the existing information (the range of sequences for a given gene/domain/whatever), and treating that as a specification. Unless I've seriously misunderstood something here, Durston's specifications correspond precisely with what Dembski calls fabrications. Now, in later versions of CSI, the independence criterion was replaced by an adjustment for "specificational resources" (the ?S(T) I was discussing earlier). Essentially, the idea is that if you can state the specification tersely, it's relatively independent, and you get a small adjustment; if you're just reading it off the data, it'll be quite long, and you'll get a huge adjustment (and no CSI). In the case of Durston's measure, the description of the specification will basically be a list of which amino acids can occur at which positions, which'll be longer than the sequence in question, and hence give no CSI (well, technically it'll give negative CSI). To back up a little: what's really needed here is the probability of something specified occurring; that can be loosely broken into three factors: - The number of specifications. - The number of significantly different ways each of those specification can be met. - The number of minor variants on each of those different ways there are. Dembski's ?S(T) addresses the first of these factors, and requires that the p calculation take the last two factors into account. Durston's approach gives an estimate the last of these factors (and winds up rolling the second factor into its incredibly verbose specifications). But in order to reconcile the two, you need a way to isolate and calculate the second factor; and I've never seen an approach that even estimates it. This is a problem I haven't really seen addressed in any treatment of the improbability of functionality evolving. It's simply fallacious to get excited about the improbability of this evolving, when if something else had evolved we'd be looking at the improbability of that evolving instead. Even in Dembski's work, when he sketches out how to compute the CSI of the bacterial flagellum (see "Specification: The Pattern That Signifies Intelligence"), he treats that basic structure as the only possible way of satisfying the specification "bidirectional rotary motor-driven propeller" -- but I don't think any of us has any idea how many different structures could satisfy that specification, and it's the sum of their probabilities that matters. Elephant #2: probability and probability distributions Here, the difference is more obvious and well-known (and is what the "EleP(T|H)ant" pun refers to). Durston's Fits only correspond to probabilities if all sequences are equally probable. Dembski's CSI requires that you compute the probability based on each relevant chance hypothesis (and you'll generally get a different CSI value for each hypothesis). Essentially, with Durston's approach, you compute Fits and then ask if selection can account for it. With Dembski's, you figure out what selection can do, then compute CSI based on that. By treating Fits as CSI, you're implicitly assuming that selection doesn't matter; but you can't assume that, you need to show that! Or to put it another way: Durston's Fits correspond to log probabilities under the chance hypothesis that the AA sequence is entirely random. But calculating a positive CSI under that hypothesis only refutes that hypothesis; and since the theory of evolution holds that selection (and other factors) make some sequences more likely than others, you haven't actually refuted evolution, you've refuted a strawman. When you say:
... 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.
I've never seen one. If you have one, please give it, but note that any analysis that claims to do this had better include at least these three elements: 1) An explicit statement of what specification is being used. 2) An explicit statement of what chance hypothesis is being used. 3) A calculation of p(T|H) which actually takes into account all ways that the specification can be met, and calculates their probability under the stated chance hypothesis. If you haven't got all three of those, you don't have a valid calculation of CSI.Gordon Davisson
August 25, 2014
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Rich: I should add that even in Shannon's original paper, they used more than one way to get at information carrying capacity values. A direct count of state possibilities is well known and common, indeed that is what lies behind, my memory has 4 Giga Bytes, or a file size is 768 kB, etc. Context allows us to assess functional specificity, and we can convert that to a threshold metric as shown. So, the above is quite legitimate. It is ALSO in order to show that blind samples from a population need to be sufficiently large to become likely to capture rare phenomena, and that one needs not do elaborate probability calculations to do that. That is what the needle in haystack analysis is about. And, enough has been noted above and onwards for a reasonable person to see why FSCO/I will naturally result in that sort of rarity. RTH's talking point quip on needle-stacks is silly rhetoric, not a serious objection. If you doubt me, shake up a shoebox full of fishing reel parts for a while -- make sure it's a cheapo [I have too much respect for the integrity of decent engineering and good workmanship to advise otherwise . . . ] -- and see if something functional results. Predictably not, for precisely the needle in haystack reasons given. KFkairosfocus
August 25, 2014
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R: Have you done a course in school where they discussed origin of life, etc? Does Miller-Urey ring a bell? If not, look up and read in say Wikipedia -- never mind the agendas in that notorious site on topics like this, that's what you will see in school. Similarly, have you done basic probability, such as why the odds of tossing a 7 total with two dice is 6/36? Those will affect what would be needed to reasonably further answer. KFkairosfocus
August 25, 2014
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PS: Further to this, examine genomes, noting the algorithmic content that regulates and effects protein assembly using Ribosomes with mRNA as control tape. Also, take time to observe the commonplace functionality-destroying effects of ever so many mutations. Then, multiply by the requisites of folding and function [note on Sickle Cell trait and anaemia as a case in point], with the phenomenon of thousands of fold domains with a good fraction of these being of one or a few proteins. Where, in amino acid sequence space, we see deep isolation of such domains so there is no easy stepping stones path across the space in light of available planetary or solar system atomic resources and time. In short, deeply isolated islands of specific, complex function based on coded DNA information executed on nanotech numerically controlled machines. Now, give us the observationally grounded a priori evolutionary materialist account that does not beg big questions. NC machines FYI are routinely produced by design, as are algorithms and associated digitally coded functionally specific complex information. In our observation, ONLY by design. And, on the search challenge analysis above, for excellent reason.kairosfocus
August 25, 2014
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Rich: Instead of making ill-founded assertions on retractions (where, by whom, when) kindly respond to the specifics of the OP, including that the common files we handle every day are in fact measured, quantified instances of functionally specific complex organisation and associated information. If you doubt me on functional specificity, do a little experiment. Call up Word, and make an EMPTY file, then save under a convenient name. Go in with a file opening utility [notice the apparently meaningless, repetitive character strings?], and clip a character or two at random. Save again. Try to click-open the file. With rather high probability, it will report as corrupt. Functional specificity. That is, in the expression above, S = 1, it has been shifted from the default 0 based on objective evidence. Empirically, objectively confirmed. Then, too, look at the file size, maybe 1 kByte. Complexity measured in bits. Compare: Chi_500 = I*S - 500 Chi_500 (Word File X) = (1 * 8 * 1024) - 500 = 7,692 functionally specific bits beyond the solar system threshold. Inference on the FSCO/I as sign principle: designed. Observation: designed. As, expected. It is time to drop the ill-advised gotcha snip and snipe rhetorical games, Rich. KFkairosfocus
August 25, 2014
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roding @29:
What I don’t fully understand yet though is, how do you determine what is being “searched” for? For example, how do you calculate the odds of a cell being formed and what assumptions would you make. Would you assume basic building blocks are in place first (e.g., amino acids), or is the calculation done from scratch. Not sure if that makes any sense, but hopefully you’ll get my drift.
This is a good question. kf will provide a solid answer, but perhaps I can offer a couple of thoughts if I may. There are myriad ways to run a sample calculation on the odds of something like a first functional cell coming about by purely naturalistic means. Typically, those calculations make all kinds of concessions to the naturalistic scenario to allow the maximum likelihood of succeeding. For example, such calculations often include all the particles in the known universe, reacting at an incredibly fast rate, ignoring interfering reactions, ignoring breakdown of nascent structures, and on and on. What the calculations inevitably show is that the resources of the entire known universe could not possibly give us any realistic chance of even basic cellular structures arising on their own, much less an entire functional cell. I need to emphasize that the calculations often include just raw odds of the basic building blocks coming together in the right configuration. This is by far the most favorable approach to a naturalistic scenario. Other calculations try to capture things like chiralty, etc. Those calculations end up being even more stringent. A while back on UD, I wrote:
I’m willing to grant you all the amino acids you want. I’ll even give them all to you in a non-racemic mixture. You want them all left-handed? No problem. I’ll also grant you the exact relative mixture of the specific amino acids you want (what percentage do you want of glycine, alanine, arganine, etc.?). I’ll further give you just the right concentration to encourage optimum reaction. I’m also willing to give you the most benign and hospitable environment you can possibly imagine for your fledgling structures to form (take your pick of the popular ideas: volcanic vents, hydrothermal pools, mud globules, tide pools, deep sea hydrothermal vents, cometary clouds in space . . . whichever environment you want). I’ll even throw in whatever type of energy source you want in true Goldilocks fashion: just the right amount to facilitate the chemical reactions; not too much to destroy the nascent formations. I’ll further spot you that all these critical conditions occur in the same location spatially. And at the same time temporally. Shoot, as a massive bonus I’ll even step in to prevent contaminating cross reactions. I’ll also miraculously make your fledgling chemical structures immune from their natural rate of breakdown so you can keep them around as long as you want. Every single one of the foregoing items represents a huge challenge and a significant open question to the formation of life, but I’m willing to grant them all. Now, with all these concessions, what do you think the next step is? Go ahead, what is your theory about how life forms?
The above deals just with the simple, pure odds of the constituent components coming together in the right order. But as mentioned, it ignores the other numerous, and so far as we know, insurmountable problems with a naturalistic origins scenario. ----- What is the materialistic response to this most basic and obvious problem with a naturalistic origin of life scenario? Largely the response is three fold: 1. There must be some other natural law we haven't discovered yet that will overcome the problem. We just need to keep looking. [This is not only unlikely; it is a logical impossibility. But this is a topic for another time.] 2. The calculations proposed were too stringent. It is much easier for life to form, so not a problem. [This is exactly the opposite of reality. As mentioned, the calculations tend to give every possible advantage to a naturalistic scenario, and it still comes up as woefully inadequate.] 3. Given that we don't know exactly, with complete precision what the various parameters and odds are, we cannot do any calculation at all and cannot reach any preliminary assessment or conclusion. [This is a silly, even juvenile, objection, but it is a favorite of, for example, Elizabeth Liddle on these pages. It is essentially a confession of ignorance about how life could have come about through naturalistic means, coupled with a refusal to grapple with the obvious implications by falling back on a hyper-technical demand for precision that is rarely, if ever applied to any other problem by anyone in any other area of science.]Eric Anderson
August 24, 2014
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gpuccio @28:
Very simply, I would say that we can measure not only the information carrying capacity of a medium, but also, and especially, the minimum information carrying capacity that is necessary to implement a function, and the rate between that and the total information carrying capacity of the medium we are observing, and that implements the function.
Once the system is set up, the minimum amount of information to carry out a function -- any function -- is a single bit. We can use a single switch to initiate any function or cascade of functions. Now setting up the system in the first place is a different issue, to be sure, and a great deal of information is required to set up a complex functional system. Quantifying that information, however, is another matter. It is relatively easy in the case where our "function" is simply the transmission of a particular message. In that case, if we know the relevant parameters (potential characters/alphabet, the specific code, what is required to encode the message, etc.) then we can calculate the minimum carrying capacity required to carry that message. And, if we treat the transmission of the message as our "function" in this case, we can then collapse the analysis and equate that quantity to the amount of information required to perform our function (meaning, to convey the message). I don't dispute that in these kinds of very narrow situations -- particularly in which we define the very conveyance itself as our desired "function" -- that we can come up with a measurement that equals or at least approximates the amount of "information" required to convey the message. It is much less clear, however, that we can measure the amount of information actually contained in that message. Let's set aside for a moment the larger challenge of measuring the amount of information in something instantiated in three-dimensional space in the real world. Even in the case of the written or spoken word -- say, a poem -- can we really mathematically and precisely measure the amount of information in the poem? Sure, we can measure the amount of carrying capacity required to transmit the message of the poem in a particular language across a particular medium. But that is a purely statistical measure. It ignores all the other aspects of the information contained in that poem: things like intent, purpose, meaning, pragmatics, and so on -- the things that give the real heart and soul and substance to the information in question. Indeed, the very things that we typically think of when we talk about "information" informing us in some way. To say that those things don't matter would be to commit the exact same fallacy that so many anti-ID people commit when they argue that the so-called "Shannon information" is all that needs to be dealt with. Generate that and you're done, they say. We would be right back to the classic examples of a meaningful sentence versus a meaningless string of characters. So, yes, we can measure carrying capacity all day. We can generate bits and bytes and look at Shannon calculations until we are blue in the face. But through such means we will never arrive at a measurement of the real substantive information contained therein. That information -- the part that really matters -- is better understood in terms of intent and purpose and goals and meaning, than in terms of bits and bytes.Eric Anderson
August 24, 2014
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R: in the first instance, we have chemicals of relatively low complexity in a pond, the ocean, a comet core or whatever; perhaps as one sees from a Miller-Urey type exercise. The hope is that -- without direction or purpose (this is blind, a random walk in the space of possible configs) -- they form self-replicating then metabolising and self-replicating entities. Somewhere along the line, codes enter and encapsulation with gating, giving us cell based life. Long on speculative hopes, short on empirical evidence. Beyond, it is hoped they go on to form diverse body plans. At each stage there are numerous blind random walk searches as outlined to find a first functional configuration. Search, of course is metaphorical, even as natural "selection" is. At each stage the only empirically warranted solution to the FSCO/I generation challenge, is design. Which is strictly verboten! KFkairosfocus
August 24, 2014
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It seems ID's information measuring claims have retracted. It used to be look at a thing, measure specified information, probabilistically infer design. Has ID given up on measuring the SI of a thing? It appears to me KF has.rich
August 24, 2014
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KF, yes your description #17 definitely helps, thanks for taking the time to do this. What I don't fully understand yet though is, how do you determine what is being "searched" for? For example, how do you calculate the odds of a cell being formed and what assumptions would you make. Would you assume basic building blocks are in place first (e.g., amino acids), or is the calculation done from scratch. Not sure if that makes any sense, but hopefully you'll get my drift.roding
August 24, 2014
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Eric: Very simply, I would say that we can measure not only the information carrying capacity of a medium, but also, and especially, the minimum information carrying capacity that is necessary to implement a function, and the rate between that and the total information carrying capacity of the medium we are observing, and that implements the function. That's just another way of describing dFSCI, which is a definite measure.gpuccio
August 24, 2014
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kf: Thanks for the useful post, as always. One minor quibble just in terms of terminology: You mention the ability to "measure information". I'm not sure that anyone has ever done that, or indeed, that it is possible. Information carrying capacity of a medium? Yes, that can be measured. Can information -- the substantive, functional, meaningful aspects of what really makes up information -- be objectively measured in a precise mathematical way? I've never seen it and personally I doubt it.Eric Anderson
August 24, 2014
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Gpuccio, I am only providing what some evolutionary biologists are saying how evolution is a naturalistic process and not one that requires periodic infusion of information to succeed. It contradicts Meyer's thesis. I am saying that Behe, Axe, Durston and Meyer have to address it. So far no one has. I am skeptical that there is enough reshuffling of the genome to produce what has happened but there has to be a focused analysis on it. Eventually, Brosius's thesis will be confirmed or falsified by the analysis of genomes. Both the Darwinian approach and Brosius's form of punctuated equilibrium are testable.jerry
August 24, 2014
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F/N: Just struck me as a parallel, the frequency of visible light is about 4 - 8 * 10^14 Hz, cycles per second. We are talking here about flipping and reading 500 coins every four cycles of red light, or in the ball park of as fast as light vibrates. KF PS: Yes, there are good reasons why light frequencies and fast reaction rates would be of fairly similar order of magnitude having to do with involved energies on order of a few to several eV, and responses of orbiting electrons in atoms. (And thinking in terms of eV is convenient never mind it is a dated unit.)kairosfocus
August 24, 2014
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R, was I helpful? Where do you need more? KFkairosfocus
August 24, 2014
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F/N: Links: First link, courtesy wayback machine. Second one. KFkairosfocus
August 24, 2014
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Mung, Thanks. Dead, I guess. I will have to come up with a way to put up or link Dan Peterson's classic pair of articles. Later, got silly season to attend to after spending time on hyperskeptical dismissals of inductive logic. KFkairosfocus
August 24, 2014
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jerry: It's very simple. I have no doubts that they find what they find. I have all the possible doubts that what they find cannot be explained without design. There bis no analysis of numbers or probabilities there. You can throw in words like "zillions", but it would be better to count what can happen and what really happens. I have no doubts that new genes appear. I have no doubts that non coding regions are used to make new genes. I have no doubts that transposons have an important role. So, I ma perfectly comfortable with all those findings. I am perfectly sure that a quantitative analysis of what happens can prove that new dFSCI is constantly generated in those processes: IOWs, they are designed processes.gpuccio
August 24, 2014
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Could you please explain how you calculate P(T|H)? Thanks!!Jerad
August 23, 2014
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hey kf, fyi, those links in @ 17 return 404 Not Found.Mung
August 23, 2014
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Jerry, search space challenge in a context where FSCO/I exhibits islands of function in a much larger sea of non-function. An explanation that may account for hill climbing does not account for blind island finding in a vast sea with very limited resources relative to the scale of the sea. KFkairosfocus
August 23, 2014
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Gpuccio, I posted several things he has written on my comment from three months ago. See link above. Here is one that sums up what he claims,
The evolution of new genes can ensue through either gene duplication and the neofunctionalization of one of the copies or the formation of a de novo gene from hitherto nonfunctional, neutrally evolving intergenic or intronic genomic sequences. Only very rarely are entire genes created de novo. Mostly, nonfunctional sequences are coopted as novel parts of existing genes, such as in the process of exonization whereby introns become exons through changes in splicing. Here, we report a case in which a novel nonprotein coding RNA evolved by intron-sequence recruitment into its structure. cDNAs derived from rat brain small RNAs, revealed a novel small nucleolar RNA (snoRNA) originating from one of the Snord115 copies in the rat Prader–Willi syndrome locus. We suggest that a single-point substitution in the Snord115 region led to the expression of a longer snoRNA variant, designated as L-Snord115. Cell culture and footprinting experiments confirmed that a single nucleotide substitution at Snord115 position 67 destabilized the kink-turn motif within the canonical snoRNA, while distal intronic sequences provided an alternate D-box region. The exapted sequence displays putative base pairing to 28S rRNA and mRNA targets.
Essentially what he is saying is that the zillions of genomic shuffling that goes on occasionally produces a genomic sequence that when translated provides a useable protein and the organism then expats it for some use. The key idea is exaption. Disagree but he helps run one the biggest medical research labs on the planet researching this process. My guess is that they find a lot of examples but not enough to account for more than a very small percentage of functional proteins.jerry
August 23, 2014
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R: I hear you, unfortunately when one deals with the sort of hyperskepticism and tendency to pounce we face, there is a need for something of adequate technical level . . . even, when that is at introductory College level. Consider the text of this post or your own. To be text in English, we have letters, spaces, punctuation marks, all coded for in on/off, high/low digital signals. Something like 100 1001. The common code is ASCII, as you may remember from IT class. Text like that is different from random gibberish: yudkngfsitirsgdoiha64tvxtiu . . . or a fixed repeating sequence: DSDSDSDSDSDSDSDSDSDSDSDSDSDSDS . . . Computer code for a program is similar, telling the PC to carry out step by step actions that in effect are recipes. These can be seen as being like beads on a string: -*-*-*-*-* . . . They are called strings. Now, short words or phrases could be produced at random, e.g. "sit" and "do" come up. But when we go to strings of 72 or more ASCII characters, all the atoms in our solar system, flipping 500 coins a hundred million million times every second, could only sample the equivalent of a straw to a cubical haystack as thick as our galaxy. That is a distance so long that light would take 1,000 years to traverse it. To cross the stack, a beam of light would have to have been travelling since about the time that William the Conqueror invaded England. Light takes less than 2 s to travel from the Moon to Earth. In short the atomic resources of our whole solar system, running at a generously fast rate, would not be able to sample more than one straw to a haystack as big as that. Even if the stars in our neighbourhood -- the ones we see in our sky -- were in such a stack, predictably a blind sample from such a haystack would get hay nothing else. That is the needle in haystack "big number" challenge that is at stake. There is only one observed source for such text -- design. By an intelligence. Now, this is not just a toy example. If you look at an AutoCAD drawing file, it is much the same as the text strings. Similarly, DNA has coded strings that instruct molecular machines in the cell to make protein molecules step by step, as is shown in the original post. These machines, Ribosomes, are what we call numerically controlled machines and the mRNA made from the DNA is a control tape. If we saw such machines, in a factory, we would not even pause to suggest they came about by blind chance or mechanical necessity similar to tossing dice or having a heavy object fall when you drop it. But, when it comes to the cell -- which also is able to replicate itself -- we are dealing with something that long predates factory automation or even our own existence. How did it come to be? Can this be reasonably taken as the result of blind chance and mechanical necessity in the warm little pond envisioned by Darwin, or some similar environment? Has this or even the origin of FSCO/I strings been observed happening without design? Given the sort of search challenge above, is it reasonable to conclude that this did or must have happened by chance and necessity? To answer, consider why it is the only observed source of FSCO/I is design? KF PS: This introduction from some years ago and this part 2, may help.kairosfocus
August 23, 2014
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roding @ 14 you brought up a very good point. I think Kairosfocus has done a tremendous work trying to explain the referred subject, but it's not an easy thing to do. What KF and GP explain in their OPs and comments in rather general terms, could be applied to the cell fate determination mechanisms in the first few weeks of human development. Basically, think that we all started from a cell known as zygote which is the product of a very complex process called "conception" (I'll skip that event, in order to simplify the discussion, but perhaps will have to refer back to it later). Words like choreography and orchestration appear often in biology literature these days. Perhaps those two concepts could serve as analogy for describing biological processes to the rest of us? In either case, we would still deal with reductionist approaches to describe complex systems to commoners like me. But perhaps the scientists could benefit from trying to explain what they know in simpler terms? Just thinking out loud.Dionisio
August 23, 2014
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hi roding, kf maintains a website as well. check out the linked. http://iose-gen.blogspot.com/2010/06/introduction-and-summary.html That said, I sympathize. I think first we need a basic course in probabilities and then one in statistics with a follow-on in sampling theory. :) Information theory is rather tangential, imo, as it is really a misnomer for communication theory so I would only go down that path far enough to understand what Shannon's theory was about and what it was not about. cheersMung
August 23, 2014
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I sense FSCO/I is an important concept and Kairosfocus work seems comprehensive, I have to confess I don't understand much of it. I don't have any math ability beyond high school and am not familiar at all with information theory. KF - have you considered trying to write a simpler description of FSCO/I for the layperson? I don't know if using analogies would help, but there must be a way for us mere mortals to understand this without needing an advanced degree! Thanks.roding
August 23, 2014
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