
Once again, I don't know why this is so difficult, but here it is:
CSI Complex Specified Information.
Information see Shannon, Claude
(When Shannon developed his information theory he was not concerned about "specific effects":
The word information in this theory is used in a special mathematical sense that must not be confused with its ordinary usage. In particular, information must not be confused with meaning. Warren Weaver, one of Shannon's collaborators
And that is what separates
mere complexity (Shannon) from
specified complexity.)
Specified Information is Shannon Information with meaning/ function
Complex Specified Information is 500 bits or more of specified information
Complex specified information is a specified subset of Shannon information. That means that complex specified information is Shannon information of a specified nature, ie with meaning and/ or function, and with a specified complexity.
Shannon's tells us that since there are 4 possible nucleotides, 4 = 2^2 = 2 bits of information per nucleotide. Also there are 64 different coding codons (including STOP), 64 = 2^6 = 6 bits of information per amino acid, which, is the same as the three nucleotides it was translated from.
Take that and for example a 100 amino acid long functioning protein a protein that cannot tolerate any variation, which means it is tightly specified and just do the math 100 x 6 + 6 (stop) = 606 bits of specified information minimum, to get that protein. That means CSI is present and design is strongly supported.
Now if any sequence of those 100 amino acids can produce that protein then it isn't specified. IOW if every possible combo produced the same resulting protein, I would say that would put a hurt on the design inference.
The variational tolerance has to be figured in with the number of bits.
from Kirk K. Durston, David K. Y. Chiu, David L. Abel, Jack T. Trevors, “Measuring the functional sequence complexity of proteins,”
Theoretical Biology and Medical Modelling, Vol. 4:47 (2007):
[N]either RSC [Random Sequence Complexity] nor OSC [Ordered Sequence Complexity], or any combination of the two, is sufficient to describe the functional complexity observed in living organisms, for neither includes the additional dimension of functionality, which is essential for life. FSC [Functional Sequence Complexity] includes the dimension of functionality. Szostak argued that neither Shannon’s original measure of uncertainty nor the measure of algorithmic complexity are sufficient. Shannon's classical information theory does not consider the meaning, or function, of a message. Algorithmic complexity fails to account for the observation that “different molecular structures may be functionally equivalent.” For this reason, Szostak suggested that a new measure of information—functional information—is required.
Here is a formal way of
measuring functional information:
Robert M. Hazen, Patrick L. Griffin, James M. Carothers, and Jack W. Szostak, "Functional information and the emergence of biocomplexity,"
Proceedings of the National Academy of Sciences, USA, Vol. 104:8574–8581 (May 15, 2007).
See also:
Jack W. Szostak, “Molecular messages,”
Nature, Vol. 423:689 (June 12, 2003).