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RE: [Swarm Modelling] Re: The "Art" of Modeling
From: |
Christopher Mackie |
Subject: |
RE: [Swarm Modelling] Re: The "Art" of Modeling |
Date: |
Sun, 16 Feb 2003 11:40:44 -0500 |
Jason; could you clarify the following bit for me? You write...
The weather scholar seems to be advocating an instrumentalist view over
a realist view. If the ultimate goal is saving lives, then one will use
any "black box" model which offers the best prediction of the future,
regardless of whether it helps us "understand" tornados (where
"understanding" a tornado means that we have an accurate description of
the general laws, mechanisms, and processes which serve to produce
tornados).
But we then face the standard problem of instrumentalism: it seems that
the only justification we can give for why a "black box" model should
offer accurate predictions is that it employs, in some way, a
description of the general laws, mechanisms, and processes which are
really at work in the world. If so, then the best way in which to cook
up the "black box" model is to just go out and try to identify the
general laws, mechanisms, and processes that really exist, i.e., we get
a call for realism.
Why is that automatically "the best way"? It would seem that this, too,
requires
an argument. Moreover, there are refutations available; for instance, the
current
issue of Scientific American reports on a GP project that has created at least
one
circuit that outperforms (outpredicts) anything designed by humans--and that
the
humans as yet don't understand it. Another article talks about using data
mining
to target pharmaceutical research in directions that are most likely to yield
high-return medications. The results have been tremendous cost savings and the
improved targeting of research--but the researchers don't necessarily
understand why
their new targets are "better" than their old ones. Aren't either one of those
applications better black boxes than we could presently build using the realist
approach?
It would seem that the decision whether to pursue general-law or black-box
strategies
should be made entirely on pragmatic, tactical grounds: when general-law
advances are
available, great; when black-box strategies can help to solve problems or to
target
attention effectively, more power to them. We can hope that scientists will
eventually
figure out why that GP circuit works better; if they do, we will have gained
some ground
on your general laws. But even if they don't, we will still have a better
black box than
anything we could design out of our own understandings. We can hope that the
data
mining insights are reverse-engineerable so that we can come to know why a
particular
strategy pays high returns; if so, we will have extended our general laws. But
even if
not, we'll have important new medications that have demonstrable beneficial
effects,
whether we understand exactly how we got them or not.
Am I missing something? --Chris
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