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<SPAN
class=731302913-30102003>herman's ideas, while very valuable, weren't
mechanical enough for this area of my thinking. I wonder if anything mechanical
like this can be truly useful.
<SPAN
class=731302913-30102003>as I mentioned in my post to b, I also wonder why
focusing on nice-performing adjecent parameter values helps response to the
reality of the future, which is unpredictably changing data, not unpredictably
changing parameters.
<SPAN
class=731302913-30102003>
<SPAN
class=731302913-30102003>dave
<BLOCKQUOTE
>Presactly
...One of the problems !? for most is that checking for robustness
takes longer. Eons ago Herman posted some fairly decent tools for
this which are along the lines of my own likings i.e. surface area
plots. This is pretty simple to do with systems that have two
variables that are being optimized on and requires more effort for more
parameters but is still doable. You can look at CAR or MDD or MAR or
whatever you like this way and get a good picture of where the cliffs
are.This shows pretty quickly where the most robust i.e. most likely
to succeed points ARE as opposed to the where the most profitable points
WERE. It takes longer then just taking a gander at a list of numbers
but imho it's worth the trip.I liken the usage of where the most
profitable points WERE as chasing a constantly moving train that you'll
never catch up to.
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