Azamat and Rich, (01)
AA> Anti-realism to ontological entities, claiming about the
> non-reality of nonobservable and nontangible entities with
> human senses (currently abstract entities), is becoming
> fashionable intellectual style. (02)
Scientists tend to be realists about their subject matter.
Physicists, for example, usually believe that their laws
refer to something that really exists. They assume that
unobservable entities such as atoms and fields actually exist. (03)
The people who created the difficulties were the 19th century
positivists, such as Auguste Comte and Ernst Mach. They claimed
that all scientific laws should relate observable measurements
and avoid any assumptions or talk about unobservable entities. (04)
Mach, for example, believed that thermodynamics should be limited
to direct relationships between measurable quantities, such as
pressure, volume, and temperature. He fought a life-long battle
against Boltzmann's statistical mechanics, because Boltzmann
assumed unobservable atoms. (05)
With his famous papers of 1905, Einstein destroyed Mach's
assumptions: his paper on Brownian motion showed that atoms
and molecules could be observed by their effects on tiny particles
that could be seen through a microscope. His two papers about
relativity and photons assumed unobservable principles and
entities that violated all of Mach's rules about how theories
should be formulated. (06)
Unfortunately, the psychologists didn't have anyone of
Einstein's stature to protect them, and the positivists led
the behaviorists to change the name of the field to get rid
of any talk about a "psyche". That led to half a century
of sterile research, popularly known as "rat psychology." (07)
RC> A lovely, intuitive description of nominalism that makes
> it equivalent to entity tracking in concept space... (08)
I'm not sure what you mean by concept space, since concepts
are usually defined by intensional methods of definitions
and axioms. (09)
In any case, data mining is a good example of a nominalist
procedure: (010)
1. Data mining starts with a database of low-level facts. (011)
2. It applies well-defined algorithm(s) that analyze the
DB to discover patterns in the data. (012)
3. Those patterns might be the result of fundamental laws,
or they might be accidental patterns that could be
violated by the next update to the database. (013)
4. Some additional analysis and testing is necessary
to distinguish principles from coincidences. (014)
Points #3 and #4 are critical. Data mining is useful as
a method for discovering patterns, but it has no criteria
for distinguishing patterns that result from fundamental
principles from accidental patterns that result from
mere coincidence. That is the weakness of nominalism. (015)
John (016)
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