Complex Specified Information (CSI)

I think that this deserves a topic of it's own. I think this is the place where the definition has been started. I've copied this from Joralex's post:

And here was my questions about that: This suggests that "complexity" and "Shannon information" are identical. Is that true? I have to ask since there are separate attempts to define a quantifiable concept of "complexity". This is felt to be needed since the Shannon information content of a random string is very high but it is not intuitively felt to be what we are talking about in terms of "complexity".If "complex" is something more than Shannon information will you clearly distinguish the two?"Specified"
'Specified' as in correlating with a criterion that is independent of the event itself.As I read you example the sonnet is "specified" because it can be read using a set of rules that are "specified" separatly from the particular ink pattern.Is it true then that something may have "speicificity" quantified? How would I calculate the "specificity" of an particular pattern of words or letters? How about a particular pattern of ink which may or may not be close to letter forms?Can you know carry this over to where it applies to evolution? Does it? Or does it only apply to abiogenesis?

And Joralex replies:I see you have the two-step down to an art form, PaulK : Open mouth... Insert foot!I've read the Design Inference several times and I've corresponded with Bill Dembski on the matter. You shouldn't really speak when you don't know what you're talking about.To make things simple Dembski uses improbability as his measure of information (the base 2 logarithm of the inverse of the probability to get to "bits" - so 2 bits is a probability of 0.25). It is not the same as Shannon information at all.Have you read Shannon's original work? (I have - I even have my own copy). Do you know what you're talking about above? (It doesn't look like it).Probability measures may be transformed into complexity measures and, from Shannon's work on communication theory, this is done via the 'inverse log base 2' transformation. This, in turn, may be appropriately labeled as an 'information measure', keeping with all accepted protocols of complexity and information theory. This is what Dembski has done and your statement above is, thus, totally incorrect.Let's see a retraction, please. Not true but then since you've misunderstood the foundation it's easy to see why you cannot understand beyond this.Joralex

Joralex, I don't think I've left anything important out. If I have we could tidy that up in the next bit. You say "from Shannon's work on communitcation" "complexity measures" may be transformed from probability measures. However, I don't have any reference to Shannon talking about complexity. Could you give the formulat for "complexity" then.You say that Dembski has kept "with all accepted protocols of complexity and information theory". Does this mean that PaulK is wrong and that Dembski is using Shannon information but perhaps with a mathematical transformation applied? Could you show this transformation? This would demonstrate conclusively that PaulK is wrong when he says "It is not the same as Shannon information at all.
".Could you discuss the specification of DNA? All I have ever seen on the web discussion this seems to think that a specific sequence is the only one to be considered. I mentioned earlier that I believe this to be wrong in that many, many DNA sequences are acceptable outcomes.Once we have the definition of information, complexity and specificity that you are using it should be much clearer what CSI is. Thank you.(PS it does seem it will be hard to get the book, our city library doesn't have it, I will try the University later in the week )

Joralex hasn't moved to this thread yet so I am moving his response over.

It was this line that made me think you were saying Shannon was talking about Complexity too. It is an easy mistake to make I think.You suggest that complexity and probability are "correlated". This suggests to me that the complexity of a long random string is high. Is this correct?I do not understand you answer to this question:
"Does this mean that PaulK is wrong and that Dembski is using Shannon information but perhaps with a mathematical transformation applied?"You suggest that Shannon information is not to be used for the task at hand because of the need for semantics, etc. Therefore Dembski is not using Shannon information? Could you clarify?For all the rest you simply suggest that someone has answered the questions elsewhere. It would be helpful if you could refer to page numbers as you did in Dembski's book.In fact in the spirit of moving debate along I think you could lift a few paragraphs from the material or paraphrase them.You haven't yet actually defined what complexity is? So far it seems to simply be Shannon information with some algorithm applied to it that prodcuces a different number for the same thing.Specificity hasn't been defined at all yet. You analogy of mixing up King Lear doesn't begin to answer the question.To extend that analogy I have, on earth, 6 billion copies of King Lear with almost everysingle one of them different from all the others. They are all, more or less, acceptable copies in that I can follow the story. Some are pretty garbled in some places (genetic diseases) but the main story is still there. With this true of extant copies of human DNA how do I arrive at the specification since I have, obviously a huge number of "inadequately specified" possbilities and a dammed big number of "adequately specfied" possibilities and no way of determining which are adequate and which aren't.On the other thread I have asked how this applies to evolution of extant life separate from abiogenesis. Can you talk about that there?

Note: I am not supporting Joralex, but some things you have asked
have answers that can be inferred from previous responses

quote:

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This suggests to me that the complexity of a long random string is high. Is this correct?

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Joralex would have to answer 'Yes' since s/he states that
random ink blobs on paper form a complex pattern.My comments follow:Harping on complexity as a way of inferring design is incorrect.Many designed things have low complexity (no matter how you measure
it) so there cannot be a correlation between complexity and design.As to complexity correlated to information, again there is no
correlation since the random string is highly complex. In Shannon
terms it has low entropy because no amount of the sequence allows
one to predict the next part.If there is no semantic information in a random string, but the
string is complex then there can be no correlation between
complexity and information.If to be specified means that you need something else to understand
whether or not there is information -- then that's what information
means ... data which is interpreted in some way (by an intelligence).There is no actor capable of understanding within a cell, therefore
the illusion of information must be just that -- and hence
there is no specificity.My thoughts (summary):1) One cannot infer design from complexity.
2) Complexity does not correlated with (semantic) information.
3) Specificity cannot be inferred (at all).

quote:

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Specificity hasn't been defined at all yet.

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I'm glad someone finally called out the intelligent design creationists on one of their most glaring oversights. I fully agree with you that the profuse variation (in terms of species, genomes, and biological structures, among many other things) found in nature makes 'specificity' a useless concept. Daniel Dennett's analogy of the vast Library of Mendel (containing all possible genomes) points out this problem in the IDC mindset.Dembski is fond of using an archery metaphor for specificity: you have to shoot the arrow through the target, not paint a target around the spot where the arrow landed. Using that analogy, then, the IDC camp wants us to believe that everywhere the arrow sticks is a bull's-eye, we just can't see their target until we assume that it's there.------------------
The bear thought his son could talk in space about the time matter has to rotate but twisted heaven instead.
-Brad McFall

Doesn't the archery analogy sum-up the problem with ID?After the event how would an independent observer know whether
the arrow hit a bullseye or a bullseye was painted around it?

I think you have introduced "semantic information" as a new concept. Then comment on the correlation between complexity and "information". However you also suggest a random string has a high Shannon information content and complexity. It is necessary to be very careful when discussing this.I have read a bit on attempts to derive a defintion of the quantity of complexity. As I recall they attempt to give a low value to a random string or a simple repeating string. That is, to arrive at something quantifiable and calcuable will still being somewhat intuitive. This also distinguishs complexity from shannnon information. I don't recall that these attempts have been successful.I also note that Joralex has gone from a high rate of posting to zero. Perhaps this is temporary.

BumpStill waiting for Joralex to explain CSI.Since he felt he could post such things as: and would like us to believe he is an expert I think it is odd that he hasn't come back to explain this.

As mentioned earlier, Dembski apparently considers that the 1126-bit SETI prime sequence exhibits 1126 bits of SI. But, if this is so, it follows that an n-bit sequence would exhibit n bits of SI. So a computer program which outputs this sequence can produce as much SI as we like, simply by letting the program run for long enough. The SI of the output could run into millions of bits and easily exceed the SI of the program, no matter how big that program is. Perhaps the task of generating primes is too intractable for this to be a practical possibility. In that case we can just pick a simpler sequence, such as the Fibonacci sequence. If we take a really simple sequence like the Champernowne sequence, we can even program it in just a few machine code instructions and run it on a bare computer (with no operating system), so the total SI of the software is less than 500 bits, not even enough to constitute CSI.I give the following justification for asserting that the SI of a program is no greater than the length of the program. Consider a given program of length N bits. By analogy to Dembski's METHINKS example, I argue that I can take as my phase space the space of all programs of the same length as my given program. Then the probability of drawing any given program (i.e. sequence of bits) from a uniform distribution over this space is 1/2N, so the information of a particular program is -log2(1/2N) = N bits. The SI exhibited by the program may be less than this (if more than one program matches the same specification as the given program) but it cannot be more.Since the SI of the program is finite (N bits) but the SI of the output sequence is unlimited, the program can generate an unlimited amount of SI.Let me address all the objections that Dembski might make to this argument: [list]

"All the SI in the output was contained in the program." This means that the program contains an unlimited amount of SI. I doubt that Dembski would like to take the position that a trivial program can contain an unlimited amount of SI.

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"The SI of a mathematical sequence is limited to the length of the program that is needed to generate it." This is true of algorithmic information (Kolmogorov complexity), but not of Dembski's SI.

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"The output of a deterministic process does not exhibit SI because it is not contingent, as required by the Explanatory Filter." But this would be making the evaluation of SI dependent on the type of causal process leading to the phenomenon, the very issue that is in question. If the SETI sequence exhibits CSI when it is received from outer space, why should the same sequence not exhibit CSI when it is produced by a computer? And for all we know, the SETI sequence might also have been generated by a deterministic computer program (programmed by ETs). In any case, we can make our computer program non-deterministic if required, e.g. by starting the prime sequence from a randomly selected prime.

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The problem for Dembski is that highly patterned phenomena are tightly specified, giving them low probability and therefore high SI. Unlike algorithmic information (Kolmogorov complexity), which is a measure of incompressibility, SI correlates with compressibility. Highly compressible sequences like the SETI sequence exhibit high SI. Dembski seems quite happy with this fact:

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It is CSI that within the Chaitin-Kolmogorov-Solomonoff theory of algorithmic information identifies the highly compressible, nonrandom strings of digits...
So Dembski's information (SI) tends to vary inversely with algorithmic information. A highly compressible sequence can be high in SI but low in algorithmic information. Dembski leads us to believe that his CSI is equivalent to the term specified complexity as used by other authors, giving the following quotation from Paul Davies' book The Fifth Miracle no less than four times:

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Living organisms are mysterious not for their complexity per se, but for their tightly specified complexity. [p. 180]
Yet, if we read The Fifth Miracle, we find that Davies uses complexity in the sense of algorithmic information (Kolmogorov complexity), and not Dembski's probability-under-a-uniform-distribution sense. Davies also calls his measure specific randomness, whereas Dembski identifies CSI, in the quote above, with nonrandom strings.

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In a similar vein, Dembski quotes Leslie Orgel:

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"Living organisms are distinguished by their specified complexity. Crystals such as granite fail to qualify as living because they lack complexity; mixtures of random polymers fail to qualify because they lack specificity." [p. 229n5]
But, by Dembski's definition, crystals have high complexity, because the probability of obtaining a crystal shape by purely random combination of molecules is very small. Like Davies, Orgel defines complexity in terms of "the minimum number of instructions needed to specify the structure".

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So, contrary to Dembski's implications, his concept of specified complexity is quite different from that of Davies and Orgel.