The Grammar of DNA

Similar to David Abel's(?) distinction of change contingent and choice contingent variation is Lila Gatlin's D1 and D2 distinctions.  D1 is "context-free" variation in the statistic Shannon sense and D2 is "context sensitive" in a grammatical sense.  Gatlin is/was a biochemist/biophycisist and is not using these terms in their specific computer science meanings, where a grammar-generated language is specifically referred to as context-free.

Gatlin referred to a Law of Conservation of Information, not too coincidently similar to Dembski's interpretation of Wolpert's.  Since at that time, the modern ID Movement had not taken shape, and the guardians of the light of science were only concerned with creation science based on religious axioms, Gatlin wasn't derided, marginalized, and silenced the way that ID proponents now are.  She was published in Nature and the Journal of Molecular Biology (as has Douglas Axe in the latter, if I recall).  Jeremy Campbell leaned quite a bit on her ideas for his Grammatical Man, which garnered praise from mathematician and Skeptic (in the anti-supernatural and anti-creationist sense) Martin Gardner.  Surely, Gardner would be expected to have a negative reaction to a speculative book that depended on pseudoscience.  Way back in the 70s, you must realize, these ideas weren't pseudoscience.

This gets back to my Conjecture:  The opposition leveled at an idea in biology will be in proportion to the perceived utility of the idea to supporting theistic belief.  Professional skeptics would not close ranks against this idea until it was associated with the idea of Intelligent Design.  Note here that all 2nd Law of  θ∆ics arguments are summarily dismissed with a "guilt by association" argument (you know, one of them there fallacies) of the Barbara Forrest kind.

Anyway, Gatlin recognizes that the statistical measure of information of the Shannon sort is insufficient to reason about the interrelatedness of grammatically organized information that has semantic value.  Where here analysis possibly falls short, is that "grammatically correct" strings, or strings that follow grammatical rules, do not necessarily convey meaning, and in fact, are usually nonsense.   But it is certainly a step  in the right direction.  There is a limited context-sensitivity in grammar that is relevant to protein structure.  Subject must have its predicate and vice versa.  In English, the choice of a plural noun will affect the form of the predicate verb.  In protein space, particular "choices" in one part of the protein will have consequences for residue choices in remote parts of the same protein (or possibly, in a part of a co-enzyme).  The context-freedom in grammar is that rules only get you so far:

Greenness is a square light bulb, that grazes in the death of fragrance.  A bus, on the other shoe, does not swim with the lug nuts, but flies in a river of boots.

Grammatically correct, more or less, but not likely to inform the hearer about much of anything.   And for every meaningful sentences, there are many combinatorially more that would be unlikely to be useful sentences in any context.  The birth of tables preceded the art of melting dishes by a trillion dollars.  My sofa isn't feeling well.

See far ogle.

Perakh and free lunch

http://dennisdjones.wordpress.com/2013/01/05/response-to-the-mark-perakh-essay-there-is-a-free-lunch-after-all-william-dembskis-wrong-answers-to-irrelevant-questions/

http://dennisdjones.wordpress.com/2013/04/18/does-evolution-alone-increase-information-in-a-genome/


http://www.infidels.org/library/modern/mark_vuletic/dembski.html

overview of the treatment of "information"

Excellent overview:
http://www.discovery.org/a/14251


I'm trying to remember which Dembski critique was claiming that genetic algorithms are a dark art.  And which was saying that genetic algorithms have a solid mathematical foundation in the work of Fisher.  Is the work of Fisher a red herring for the fact that genetic algorithms have to manipulated into provide specifc sorts of answers?

Update:  Ok... the first quote is actually Wein quoting Geoffrey Miller's "Techonological Evolution As Self-Fulfilling Prophecy" (intriguing title, n'est pas?):

The trick in genetic algorithms is to find schemes that do this mapping from a binary bit-string to an engineering design efficiently and elegantly, rather than by brute-force.... The genetic operators copy and modify the genotypes from one generation to the next.... Getting the right balance between mutation and selection is especially important.... Finally, the evolutionary parameters [such as population size and mutation rate] determine the general context for evolution and the quantitative details of how the genetic operators work.... Deciding the best values for these parameters in a given application remains a black art, driven more by blind intuition and communal tradition than by sound engineering principles.²⁴ 

which I quoted here on this blog.

The first quote, I must've been thinking of the "eandsdembski" paper.  Elsberry and Shallitt actually try to avoid the problematic claims about genetic algorithms and imply that we know that the amazing functional complexity we see in nature simply follows from the math:

Dembski asserts that \evolutionary algorithms" represent the mathematical underpinnings of Darwinian mechanisms of evolution [19, p. 180]. This claim is egregiously backward. A large body of scholarly work is completely ignored by Dembski in order to make this claim, including Ronald Fisher's 1930 book, The Genetical Theory of Natural Selection.[16]  It is evolutionary computation which takes its underpinnings from the robust mathematical formulations which were worked out in the literature of evolutionary biology.

They draw a distinction between genetic algorithms and artificial life.  They seem to be implying that none of the fine tuning done for genetic algorithms applies to evolutionary computing in artificial life, as it's general target of survival doesn't predispose it to solving particular, goal-directed problems (such as Schneider's ev program?).

Aside:
It occurs to me that the information going from the environment to the population in question should be represented as the logarithm of the decrease in probability of death before reproduction.  Given all the bits of information being absorbed by a population about property X, what would the signal to noise ratio be?

macroscopically describable

If we repeat an experiment 2^k times, and define an event to be “simply describable” (macroscopically describable) if it can be described in m or fewer bits (so that there are 2^m or fewer such events), and “extremely improbable” when it has probability 1/2^n or less, then the probability that any extremely improbable, simply describable event will ever occur is less than (2^(k+m))/(2^n). Thus we just have to make sure to choose n to be much larger than k + m. If we flip a billion fair coins, any outcome we get can be said to be extremely improbable, but we only have cause for astonishment if something extremely improbable and simply describable happens, such as “all heads,” or “every third coin is tails,” or “only every third coin is tails.” Since there are 10^23 molecules in a mole of anything, for practical purposes anything that can be described without resorting to an atom-by-atom accounting (or coin-by-coin accounting, if there are enough coins) can be considered “macroscopically” describable.
    Granville Sewell, "Entropy, Evolution and Open Systems", note 5