RE: [Swarm Modelling] Re: The "Art" of Modeling

[Christopher Mackie]

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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