Took a look.

Last I checked each aa is coded for with 3 nucleotide bases, and in turn each base has 4 states, so in effect we are looking at up to six bits or so per aa. [Actually, there will be various slight mods on AA constraints and observations, but that is good enough for rough work.]

I see no material fault with your work.

But that is not the real problem. the real issue is that we are facing a situaiton where people developed a pre-info ager theory, which threw out an unexpected bridge to info theory, in 1948 – 53. And, once we did rthe stiudies on DNA and proteins, we see that we are dealign with very sophisticated info sysrtems. And, info systems we know come from intelligence. but that “cannot” be permitted under the dominant paradigm of evo mat.

So — with all due respect to those to whom this does not apply — no end of delaying, foot dragging and even temper tantrum tactics on the obvious conclusion of the matter.

GEM of TKI

I’d have to agree that 1000 is more practical since even relatively “simple” biological systems exceed that and it prevents any “gotcha moments” where Darwinists may attempt to trumpet aloud any special exception that might be found.

BTW, did you find any errors in my own explanation in #103 and #137? I believe I’m correct but it’d be nice to be double-checked by an expert so I don’t go around repeating errors.

Pardon, but I must insist: the context is that you are discussing a concept that is broader than any one person, and wehich as I note has been used in several phrasings.

Over the years I have been using FSCI as a DESCRIPTIVE term for what Orgel, Yockey, Wickens etc were getting at, I emphasised “functionally specific/ specified complex info” and recently find I like the alternate “function-specifying complex info” — esp in contexts of algorithms or structures that are precisely organised or shaped to function, like 747s and arrow heads.

In ALL cases a subset of CSI is intended, just that the function in question is what gives the de facto, observable specification. (Ef it ain’t wuk, it ain’t wot we does want . . . [Cf the philosophical concept that, e.g. “food” is functional stuff.)

GEM of TKI

1,000 bits is a better “practical” limit, as the search window set by our observed cosmos is 10^150 states.

So, since 1 k bits has a config space that is 10 times the square of 10^150, it means that a cosmic scope search could not sample more than 1 in 10^151 actually of the space.

That aptly captures the odds of worse than 1 in 10^150 in Dembski’s UPB.

GEM of TKI

Kindly look at the glossary on FSCI.

Kindly look at my comment where I say, “According to your [jerry's] understanding”. I’m not talking about the FAQ’s characterization, I’m talking about jerry’s.

Kindly look at the glossary on FSCI. You will see that it is usually used in several expanded and sufficiently comparable senses that “specifying” and “specified” make no effective difference.

FSCI is a subset of CSI in any case, as the issue is that the specification is tied to observed functionality. And indeed, both concepts arise from the same context: observing the functionality of the nanomachines in living cells.

CSI went more general, FSCI sticks to the OBSERVED functionality focus for specifying the complex oragnisation in question. So, DNA exhibits FSCI, so does a string of 143 ASCII characters giving a message in English. So does an arrowhead or a Jumbo Jet — the design spec relative to their functions.

GEM of TKI

Khan #104,

In the other link I gave I noted that the “ice fish carr[ies] a partially deleted copy of alpha1 and lack the beta globin gene altogether. These deletions are inextricably linked to its lower blood viscosity…” IOW, a destructive mutation that gives a benefit in this limited environment. The number of repeats apparently required for this “functionality” is 4 repeats or 96 informational bits. AFAIK additional repeats are unnecessary duplications. As I mentioned tying function to biological information is the hard part, so I may be wrong on this and this example might require more than 100. No big deal either way. Not to mention, I suppose it could be argued that a degenerative, and repetitive, change like this should not even count as FCSI, although I’d leave that determination to the experts. I personally believe there will be found special exceptions where 500+ informational bits can be exceeded by non-foresighted processes, and ID theory will need to account for them, but that’s just my opinion.

GSV #124,

Machine code is binary, thus one bit. The biological code is a quaternary code, thus 2 bits. I was just explaining the overall concept and using an easy example. I didn’t bother pulling up any sequence data, but in short

informational bits = (length of functional sequence) X 2

For the ice fish example I was assuming that the 3 amino acids were encoded via ~12 nucleotides (if I’m wrong in this assumption please correct me). So 12 X 2 = 24 informational bits. Then 24 X 4 repeats = 96 informational bits. As I said, easy. Although as I’ve mentioned this is just an estimate of the true biological information content since we’re still trying to figure out exactly how everything is encoded. So I’m sure there are plenty of caveats my quick example does not take into account. For example, how to calculate for frameshifting and an encoding scheme where the same information is reused for multiple different applications?

Also, the biological example I gave on this thread were based upon generalizations. So the accuracy could be questioned but I highly doubt the numbers are going to change dramatically. And in general I prefer to deal in straight informational bits instead of probabilities (1 in 10^150 corresponds to 500 informational bits) when it comes to measuring complexity in biology since these are information-based replicators, not pebbles on a beach where probabilities would be more appropriate.

Here’s 2 other examples where I ran the numbers: here and here

“Is it possible for information to specify something but not be specified by something else?”

I am not aware of any.

The reason I asked is this: According to your understanding, CSI is, by definition, specified by something, and FSCI, by definition, specifies something, although it is not necessarily specified by anything. Under those definitions, FSCI is not defined such that it’s a subset of CSI.

At this stage, I suspect that we are seeing a Panda’s Thumbster or Talk Origins [or ilk] attempt to mischaracterise FSCI in order to then — without proper warrant — claim that it is a confused, and useless concept; brushing it aside rhetorically.

But in fact, it is at root s simple descriptive phrase, one that is pretty much self defining if anything:

1 –> Some things require/use information to function, and that info is specified by the functionality it achieves.

2 –> That information is sometimes fairly complex, and when that happens, it requires a fair quantity of storage; which can be measured in bits at work, i.e bits that are functional.

3 –> to illustrate, think here, of a CD which is empty — 700 or so MBytes of bits that in that context are set up to provide storage — i.e formatting and precise organisation. Then load some files, of reasonable size.

4 –> That will be complex and functionally specific in some externally recognisable context, requiring hardware, algorithms, programs, programming/storage languages and associated onward target applications to read and put it to work, i.e info storage systems require FURTHER FSCI to work.

5 –> Look at the DNA-ribosome-enzyme etc info storage and processing system in cells: DNA stores, Ribisomes etc read and translate, creating amino acid chins that then fold, agglomerate and are transported to use-sites,w here they may for instance self-assemble into a functioning flagellum. (Three weeks back, Feb 3, there was a video hosted here at UD on that onward self-assembly, which is in part based on the precise structures and capacities of the assembled proteins.)

6 –> Then, observe back to the OOL researchers in the 1970′s to 80′s, to see that this has been recognised for a generation at least as applying to life in the cell, leading them to naturally form the concepts — NB definitions try to give precise borders to concepts, i.e concepts are based on abstracting key commonalities of examples and are logically/ epistemologically prior to precising definitions — CSI and FSCI:

Living organisms are distinguished by their specified complexity. Crystals fail to qualify as living because they lack complexity; mixtures of random polymers fail to qualify because they lack specificity.6 [Source: L.E. Orgel, 1973. The Origins of Life. New York: John Wiley, p. 189.]

[TBO summarise in TMLO ch 8, 1984:] Yockey7 and Wickens5 develop the same distinction, that “order” is a statistical concept referring to regularity such as could might characterize a series of digits in a number, or the ions of an inorganic crystal. On the other hand, “organization” refers to physical systems and the specific set of spatio-temporal and functional relationships among their parts. Yockey and Wickens note that informational macromolecules have a low degree of order but a high degree of specified complexity. In short, the redundant order of crystals cannot give rise to specified complexity of the kind or magnitude found in biological organization; attempts to relate the two have little future. [I add: save in obfuscatory rhetoric]

7 –> So, by 1984, the CONCEPTS for FSCI and CSI were identified and exemplified with “this is that” and “this other is NOT that” cases, by leading — and non-ID — OOL researchers.

8 –> So, the initial generation of ID thinkers and researchers, starting with Thaxton et al, built on EXISTING concepts that were known to be relevant to the context of OOL and the functioning of the cell based on information rich organisation.

9 –> In particular, we may observe that FSCI and CSI are actually fairly familiar to those who have had to design, develop, debug or troubleshoot information-based technological systems. [Mystery solved on why such a high proportion of ID thinkers and workers come form fields that use CSI and FSCI based systems, making us familiar with the concepts and their most credible causes. Lets just note that biologists as such are usually not familiar at design and development level with such complex info-based systems.]

10 –> And so, the inference that where we see such systems design is a known cause, and therefore a serious candidate for best explanation, is a very obvious one. But, how does one make such a distinction on a reasonable and objectively warranted basis?

11 –> Already in TMLO, there is a hint, for Thaxton et al address not only classical thermodynamics but statistical thermodynamics on trying to work out the likelihood of forming proteins and/or DNA on a platnetary scale, thus the equilibrium conc in a hypothetical [and very generous] pre-biotic soup. For, they bring in Brillouin Information.

12 –> this brings up the info school of thermodynamics, and the astonishing parallel between thermodynamic entropy and the H-metric of average info per symbol in info theory. For, as Harry Robertson summarises in his Statistical Thermophysics:

. . . It has long been recognized that the assignment of probabilities to a set represents information, and that some probability sets represent more information than others . . . if one of the probabilities say p2 is unity and therefore the others are zero, then we know that the outcome of the experiment . . . will give [event] y2. Thus we have complete information . . . if we have no basis . . . for believing that event yi is more or less likely than any other [we] have the least possible information about the outcome of the experiment . . . . A remarkably simple and clear analysis by Shannon [1948] has provided us with a quantitative measure of the uncertainty, or missing pertinent information, inherent in a set of probabilities [NB: i.e. a probability should be seen as, in part, an index of ignorance] . . . .

[deriving] S({pi}) = – C [SUM over i] pi*ln pi

[where [SUM over i] pi = 1, and we can define also parameters alpha and beta such that: (1) pi = e^-[alpha + beta*yi]; (2) exp [alpha] = [SUM over i](exp – beta*yi) = Z [Z being in effect the partition function across microstates, the "Holy Grail" of statistical thermodynamics]. . . .[pp.3 - 6]

. . . . S, called the information entropy, . . . correspond[s] to the thermodynamic entropy, with C = k, the Boltzmann constant, and yi an energy level, usually ei, while [BETA] becomes 1/kT, with T the thermodynamic temperature . . . A thermodynamic system is characterized by a microscopic structure that is not observed in detail . . . We attempt to develop a theoretical description of the macroscopic properties in terms of its underlying microscopic properties, which are not precisely known. We attempt to assign probabilities to the various microscopic states . . . based on a few . . . macroscopic observations that can be related to averages of microscopic parameters. Evidently the problem that we attempt to solve in statistical thermophysics is exactly the one just treated in terms of information theory. It should not be surprising, then, that the uncertainty of information theory becomes a thermodynamic variable when used in proper context [p. 7] . . . .

Jayne’s [summary rebuttal to a typical objection] is “. . . The entropy of a thermodynamic system is a measure of the degree of ignorance of a person whose sole knowledge about its microstate consists of the values of the macroscopic quantities . . . which define its thermodynamic state. This is a perfectly ‘objective’ quantity . . . it is a function of [those variables] and does not depend on anybody’s personality. There is no reason why it cannot be measured in the laboratory.” . . . . [p. 36.]

13 –> In the 1990′s Drs Dembski and Behe enter the picture, the latter focusing on the origins of complex, multi-part organisation, and the former on the associated information.

14 –> In effect, first, it becomes very hard to get to complex multi-part functionality without intentional creation of parts and/or deliberate adaptation of existing parts to interface and work together in a new whole. (And there is as a rule a core of parts that are necessary if a function is to work art all.)

15 –> This is a major — and (rhetoric notwithstanding) unsurmounted — hurdle for RV + NS based schemes of thought on origination of body-plan level and micro-functional life systems.

16 –> Dembski’s CSI models helped us to quantify the CSI, providing a metric. The universal probability bound puts up a conservative threshold where logically possible organisations become so improbable that they are unlikely to have formed on the scope of our observed cosmos by chance. the explanatory filter — especially when focused on ASPECTS of the entity under investigation, uses a reasonable extension of statistical inference to infer on best explanation across the three long since known causal factors: chance, necessity, intelligence.

17 –> In that general context, FSCI at one level is a simple way to look at the bottomline: if an OBSERVED function uses 500 – 1,000 bits or more of storage capcity, it is beyond reasonable doubt a case of being beyond the credible reach of chance on the gamut of our observed cosmos, so we may confidently infer to design as its best explanation. (That is, once we refuse to censor out the possibility that design can give rise to systems; i.e we refuse to go with Lewontinian a priori materialism as the NAS has now explicitly imposed for “science” in the US. this silences material facts and factors before they can speak, and subverts science from being an empirically based unfettered exploration of the truth about our world.)

18 –> And now, as pointed out at 133 above, several professional workers are giving a more formal approach to FSCI, and have now published a table of 35 measured values of FSC for proteins and related molecules.

____________

So, we can see for ourselves the true state of the balance on the merits, and it is plainly not in favour of the obfuscators.

GEM of TKI

re 128:FSCI is not a rigorous measurement that can be applied to biological systems, not least because it assumes creation ex nihilo. It does not take into consideration the known evolutionary mechanisms that build on previous success.

1 –> Could you kindly specifically document this claim?

2 –> For instance, kindly explain how the TA and Durston et al papers fail to address biological contexts, and fail to produce valid FSCI values; including how such a presumably gross error escaped the attention of the peer reviewers.

3 –> Also, please point out just how the FSCI concept — that we have empirically observed information that functions and so is functionally specific, and that is complex as it takes up a significant storage [and is not simply compressible or easily discoverable to a random walk search] — ASSUMES Creation Ex nihilo?

(FYI, a view of Creation that God used a big bang at 13.7 BYA and guided OOL and macroevoltuion thereafter qualifies as creation ex nihilo — i.e. it is a view that God creates the physical cosmos, and that matter (in whatever form) is not eternal. [Contrast, say the fairly common circa C1- 3 AD Gk concept of the Demiurge forming recalcitrant but eternally existing matter to crudely reflect the eternal forms; leading to a messed-up physical world, so that the body becomes the prison of the soul and salvation is the business of acquiring secret knowledge so we can escape being re-imprisoned. (Try Simon Magus and his First Thought, Helen the former slave and lady of the night.)])

4 –> Now, in general, once coded info of significant complexity is used, MOST configs are non-functional. E.g. words of 1,000 bits length or equivalent will specify a space of 10^301 configs, so that only a tiny fraction can be functional. Evolutionary mechanisms as proposed, deal with differential success of functional configs. But the first challenge is getting to an island of function in a pre-biotic context. So, how does a measure of the config space challenge that starts BEFORE evolutionary mechanisms may apply is failing to account for such?

5 –> Similarly, post OOL, we are looking at body-plan level transformations for macroevo. These credibly require increments in DNA — which is a functional, complex, digital data string — of order 10′s to 100′s of millions of bits, many dozens of times over. How, then, does pointing out that functional islands in such astonishingly large config spaces will be very hard to find, fail to address the capacity of evolutionary mechanisms — mechanisms [RV + NS etc, so we have differential reproductive success] that focus on incremental improvements WITHIN islands of function?

GEM of TKI