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Incompleteness theorem, special relativity, STV.<br>
<br>
<br>
<br>
Albert Einstein valued classical deterministic physics because it
seemed to offer completeness of explanation. I mention this to link
completeness with determinism. This was why he ultimately objected,
to statistical physics, precisely because it did not offer
completeness of explanation. True, statistics is deterministic, but
it is incompletely deterministic, allowing for margins of error.
Within the Kurt Godel incompleteness theorem, this opens up the
possibility for statistics, as an incomplete determinism, to coexist
with consistency in a scientific theory.<br>
<br>
Special relativity was viewed as both a consistent and deterministic
theory. But like the other two theories, of Brownian motion and the
photoelectric effect, in the 1905 Einstein year of miracles, special
relativity is a statistical theory. High-energy physics creates a
geometric scale of motion, significantly approaching light speed,
whose formulas are, in effect, geometric means.<br>
<br>
Before special relativity, the Michelson-Morley experiment used the
arithmetic mean to calculate ranges of relative motion to light
speed. Had they used the geometric mean, their calculation would
have agreed with the experimental null result. [When I wrote of this
fact to a physicist, he vehemently censored this personal
observation, as he never had seen it done that way, in any text
book.]<br>
<br>
Classical physics appears to be deterministic but it is really
statistical. At classical speeds, not significantly approaching
light speed, the geometric scale of motion disappears, leaving only
an implicit average, to appear as a deterministic variable.<br>
<br>
So, special relativity really upholds the incompleteness theorem, of
statistics, considered as incomplete determinism, coexisting with
consistency in a theory.<br>
<br>
Likewise, Binomial STV is a consistent theory of a rational election
count and a rational exclusion count, together with the incomplete
determinism of statistical measurement of representation by
averages.<br>
<br>
Regards,<br>
<br>
Richard Lung.<br>
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