Herman Rubin <[EMAIL PROTECTED]> wrote:
> As we get more complex situations, like those happening
> in biology, and especially in the social sciences, it is
> necessary to consider that models may have substantial
> errors and still be "accepted", as one can only get some
> understanding by using models.
"All models are wrong. Some models are useful."
-- George Box
I think what a lot of people forget (or never realized in the first place)
is that a model is by definition an oversimplification of the state of
nature. A model that fit perfectly would be of no use, as it would be
just as complicated as the state of nature itself. As Stephen Jay Gould
pointed out in his discussion of factor analysis in _The Mismeasure of
Man_, when we build models we are *deliberately* throwing out
*information* (not just "noise") in the hopes that we can deal
conceptually with what remains. We really can't do otherwise simply
because our brains aren't infinitely powerful. But we have to remember
that that's what we're doing, and (again a major point of Gould's)
disabuse ourselves of the notion that we're discovering something that's
more real than the real world. Models are not Platonic ideals. They are
conceptual shortcuts, heuristics if you will. They help us cope with
uncertainty, but do not make it magically disappear.
(I find phraseology like "this data was generated by that model" extremely
offensive, as it subtly plays in to both the Platonic ideal notion and the
postmodern notion that reality is purely a social or linguistic
construct.)
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