Rich AT EnglishLogicKernel DOT com
RC> Although the interesting algorithms aren't that
well defined -
> they're evolving like stem cells and
continue to this day.
I shouldn't have used the phrase "well-defined
because it is irrelevant whether you have a precisely
algorithm or a bunch of heuristics to do the data
method, formal or informal, that is limited to finding
in a fixed set of data cannot distinguish a law from a
So you consider data mining to be properly
Furthermore, it's irrelevant whether the pattern
finder is a
computer program, a human being, or an
An algorithm, a machine...
In terms of scientific method, any pattern found in
set of data is a *hypothesis*. Before it can be
scientific law or theory, it must make testable
about new cases that have never previously been
The stem cell - a hypothesis about the
Bode's law about the distances of planets from the sun
originally formulated by J. D. Titius on the basis of
from Mercury to Saturn. When Uranus was
discovered, the formula
had to be adjusted. But then Neptune
completely destroyed the
pattern and showed that the so-called "law"
had no predictive
Otherwise, the weak law of large numbers
That is an example of "data mining"
performed by humans based
on a fixed set of data. The pattern they found
was shown to
be a coincidence that had no predictive power.
JFS> Those patterns might be the result of
> or they might be accidental patterns that
could be violated
> by the next update to the database.
RC> Or they might be bound to the
conceptualizations in the observer's
> cranium, whether fundamental or preaproved
or officially not.
The issues have nothing to do with the nature of the
It is irrelevant whether it's human, alien, or
The question is whether the agent is limited to
finding patterns in
a fixed set of data (i.e., data mining) or whether it
patterns to be tentative hypotheses to be tested by
Scientific method depends critically on testing
determine whether they can make reliable
predictions. And the
nature of the agent is irrelevant.
JFS> Some additional analysis and testing is necessary
> distinguish principles from coincidences.
RC> Recursion does that very nicely. If you
can learn it once,
> you can learn it N times.
No. Recursive methods limited to a fixed set of
do anything to distinguish a law from a coincidence.
The critical issue is whether the hypothesis derived
mining from a given set of data can make testable
on new data. And the more varied the
circumstances in which the
data is obtained and verified, the more reliable the
and the more likely that it indicates something real,
a verbal (i.e., nominalist) formula.
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