Re: Complex Specified Information - Pitman Formula

On Jul 26, 11:57 am, Seanpit <seanpitnos...@naturalselection.
0catch.com> wrote:
On Jul 25, 5:32 pm, hersheyh <hershe...@xxxxxxxxx> wrote:



The reference sequences are determined, by you, ahead of time before
you go out to analyze any other sequences. The reference sequences
are based on non-random strings that are known to be produced by
simple algorithms - like pi or like 0101010 . . .

IOW, you would know it if the SETI signal were repeated digits of pi
in base 10, but would not be able to recognize pi in base 2 or the
other reference you give. Using *your* idea, you would declare any
other signal as "random" and unrelated to the 'reference'. Is *that*
what you claim that SETI is doing?

As I've pointed out many times now, a match to a reference string, by
itself, is not enough to detect ET. A maximum Pitman CSI number does
NOT equal ET or ID for that matter. What it does indicate is non-
random bias. Try to remember this point this time.

'Non-random bias' is apparently nothing more than 'degree of
similarity'. The more similar the 'reference' and 'target' sequences
(with both being nothing other than an arbitrary choice on your part)
are to each other, the higher the Pitman CSI number. Of course, you
do muck it up with your first term, the size of total sequence space,
which is basically irrelevant and tells us nothing of any utility.

Now, is it possible for a set of reference strings to miss a non-
random sequence?

Huh? You aren't claiming that your set of reference strings are able
to *identify* a 'random' sequence at all unless your claim is that you
can identify and use all 'non-random' (whatever that means) sequences
as your reference set. I say whatever you mean by 'non-random'
because the numbers in the sequence for pi are about as random as they
come. Take any stretch of numbers in pi and see if they can predict
any other similarly sized non-overlapping stretch of numbers in pi.
There is no repeatablility in pi and thus the string of numbers in pi
is pretty much *random* despite being predictable by a simple
algorithm. And simple algorithms produce fractals. And a pattern of
random mutation and neutral drift over time would also not produce a
*predictable* determinative single result. If you ran the experiment
over again you would get a different result for such a process.

All your numerology can *really*do is identify whether or not a
sequence is reasonably close to one or another of the sequences you
chose to be 'reference' sequences; that is, it identifies degree of
similarity.

There actually are programs that can do this much better. And they
can handle more than a single 'reference' and a single 'target'. In
fact, they can arrange sequences in a nested hierarchy of similarity
on the assumption of the number of single event changes required to
produce a pattern. They come with terms like "maximal parsimony".
And these programs are actually used to identify the nature of changes
in actual proteins (usually controlled for function) in actual
organisms. Again, the problem for your brand of creationism is not,
in general, the rarity of 'novel' functions. Those are very rare
indeed. It is the vast amount of difference that is selectively
effectively neutral but which produces change in patterns *of
similarity* that are so closely related to each other that they
*cannot* be due to chance. The only non-chance explanations that make
sense are historical (and largely vertical) descent, which requires
the time-frames that geology gives us, and deliberate deception by a
designer, if the time-frame is too short.

Certainly! In fact, it is impossible to rule out
this possibility. No one can do it - not SETI scientists, not
anthropologists, biologist, chemists, physicists, or even IDists. No
one. It is impossible.

IOW, you will, in fact, generate *many* false negatives where you will
claim that some 'target' sequence cannot be derived from any of the
'reference' sequences you have tested because you have no idea what
'reference' sequences to use and are simply pulling them out or yer
arse in the first place. Let's try the reference sequence for pi in
base ten! No. I don't see any signal sufficiently close to that.
Let's try the reference sequence for pi to the base two. No. I don't
see any signals close to that. Let's try pi to the base seven...

After you have your set of reference strings, you can compare incoming
sequences to your set of reference sequences to see if the incoming
sequences is likely to be non-random in origin.

Again, you would only be able to detect 'targets' that were near
enough to your *biased* selection of 'reference' sequences to register
as 'sufficiently close'.

That's right . . .

And more importantly you are NOT, repeat NOT, determining anything
about the 'randomness' of the 'tested' sequences. You are ONLY
determining how close they are to one or another of your 'reference'
sequences. Closeness to a 'reference' sequence is NOT a measure of
the randomness of the 'target' sequence. It is ONLY a measure of
similarity between the two sequences.

Also, the selection of the reference string must be done without any
knowledge ahead of time of the test string. The choice must be
completely independent.

IOW, the *reference* string must be a *randomly* chosen sequence out
of total sequence space.

No. The reference string must be chosen based on knowledge that it is
not random - i.e., the reproducible product of a simple algorithm.

Fractals are generated by simple algorithms. So, for that matter, is
a pathway in which you have occasional random mutation and fixation of
the result as a second rarer event. But those are not
*reproducible*. Does that mean you are *specifically* ruling out
evolutionary algorithms *arbitrarily* by requiring a determinative
result rather than a probabilistic one? What would be the 'reference'
sequence for proteins. since, because the same functional protein in
different organisms have different sequences and sometimes
dramatically different sequences, you cannot claim that any particular
available sequence is a reproducibly determined product of a simple
algorithm? If your CSI calculation is going to have meaning for
evolution, you do have to tell us *which* sequence for, say, beta
globin of hemoglobin is the "reproducible result of a simple
algorithm", don't you? Is it the human gamma-G? gamma-A? Embryonic?
Adult?

Functional systems are known to be non-random because of their
functional properties - properties that cannot be produced by just any
randomly produced sequence.

Actually, and in fact, *functional* systems can be produced randomly.
A random sample of about 10^17 50-mer RNAs has, almost certainly, a
molecule with RNA ligase activity, one with polynucleotide kinase
activity, and undoubtedly several other activities. You will note
that 10^17 molecules is around a millimole of RNA. Proteinoids,
generated by heat, also have enzymatic activities.

What you mean is that not all sequences have utility to a particular
organism. That doesn't tell us anything about the 'randomness' of the
sequence as a sequence.

Beyond this, although a bit more complicated, a significant match for
a reference string that is "apparently random" does indicate non-
random production of one or the other or both the test string and the
reference string.

You mean like the fact that many *different* proteins and even more
proteins with *different* sequences serving different but related
functions (like myoglobin, alpha globin, beta globin) have some degree
of *sequence* homology beyond that expected by chance alone or that
many different proteins share structural moieties more than would be
expected by chance alone is evidence that they were produced from
common ancestors? [Again, there is more structural homology than
sequence homology.]

There is no "target". There are only test strings that you compare to
your reference strings. If the test strings match one of your
reference strings, to a high level of CSI, the hypothesis of non-
random origin is supported.

Then the result you get is entirely dependent on which sequences you
*arbitrarily* chose as your 'reference' strings. How can you be sure
that your choice of all the 'reference' strings you *arbitrarily*
chose to look at will catch the 'intelligently designed' sequence you
'test'.

First off, the CSI calculation isn't about detecting ET or ID. Let me
make that very clear once more. It is about detecting non-random
bias.

No, it is about identifying degree of similarity with a 'reference'
sequence. I have no idea of what sort of "randomness" or "non-random
bias" you are talking about. If I choose a random sequence as my
'reference', I may or may not find another sequence with some
similarity to that random sequence. How does the CSI number tell me
*** about "non-random bias" in such a case? It would only tell me
that the sequence I call the 'target' is or is not close to the
sequence I call the 'reference'.

But since you do say that 'reference' sequences are not *randomly*
chosen, but are only chosen on the basis of their being produced as a
single determinative *sequence* by a simple algorithm (which excludes
sequences produced by evolution by neutral drift, even though
evolution by neutral drift is a simple algorithm, because evolution
does not produce a single determinative sequence; it produces a
different sequence each time in a probabilistic fashion), I fail to
see the utility of this number for anything having to do with biology.

Beyond this, you can't be sure to catch all non-randomly produced
sequences. It is actually impossible to detect all such sequences as
noted above. That is the nature of science. Perfection cannot be
achieved. That is what makes science useful. If perfection could be
achieved, science would no longer be needed.

You need to convince me that your CSI is a better way of detecting
sequence similarity (which is all it seems to be able to do) than hd
alone is. And then you need to convince me that it is better at
identifying the patterns of protein sequence similarity than maximal
parsimony methods are.

And how will you, if you are too broad in your *arbitrary*
choices, prevent false positives?

You can't prevent false positives with absolute perfection - only with
a high degree of predictive value.

And if you are too narrow in your
*arbitrary* choices of 'references'
aren't you going to ensure many
false negatives?

You can't insure against false negatives with perfection either.
Again, that's impossible in science.

But since all you are measuring is degree of similarity between
sequences, the choice of 'reference' sequence is crucial. And making
extravagent claims that *evolutionary mechanisms* cannot work based on
such limited knowledge is a bit of intellectual arrogance.

How
would you choose the 'target' *after*
you have "independently chosen
the 'reference'?

You don't choose the test string. Any string could be tested by the
reference strings - any string at all.

You must *really* be brilliant if you can think of all the 'reference'
strings that not only a non-human ET might send as a signal, but also
all the protein 'reference' sequences that have *ever* existed.
Otherwise I cannot think of any way that your test would not wind up
being hit-or-miss and not much better than dumb luck.

Though not perfect, the hits are much better than dumb luck - and
that's the value of science. Science does not require perfection or
rule out the possibility of being wrong. As a "scientist" you should
know this already.

Yet you *regularly* reject the repeated evidence of sequence
similarity as evidence of *evolutionary* relatedness. Isn't that a
bit more than a double standard on your part? Isn't it delusional?

Sean Pitmanwww.DetectingDesign.com

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