Posted by
Phil Henshaw-2 on
URL: http://friam.383.s1.nabble.com/One-of-my-projects-tp523632p523640.html
Correct, and perhaps a better way of saying that same thing.
I have a bundle of natural system derived metrics for use in comparing
the behavior of models with individual instances of physical systems.
There's still a gap in making the system features I can extract from
nature connect with what statistical modelers will want to plug into
their models, however. The gap seems to represent a significant real
disconnect between the designs of individual physical systems and the
designs of computer models of them.
One of the features I think would help the most to make computer models
more similar to individual instances of physical systems is that every
sub-system act as an individual, and be given the behavior of
'exploring' it's domain. System 'exploration' is a property that I
think could be given mathematical definition, but has not yet as far as
I know. It basically means 'variation at the fringe' where the number
of experiments in the region of successful experiments is variously
self-controlled. The problem for modeling that I see is the question
'fringe of what??'. Because a computer model is iterative there are
loops of effects. In nature, loops of effects like that bundle into
individuals that act as wholes. I know how to identify them and a
little about how to explore them, but not how to write programs to
emulate them. Perhaps that's because I see their every feature to be
essential, and maybe that's not quite necessary for building somewhat
useful models that carry more of their authentic structures?
Phil Henshaw ????.?? ? `?.????
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
680 Ft. Washington Ave
NY NY 10040
tel: 212-795-4844
e-mail: pfh at synapse9.com
explorations: www.synapse9.com
> -----Original Message-----
> From: friam-bounces at redfish.com
> [mailto:friam-bounces at redfish.com] On Behalf Of Marcus G. Daniels
> Sent: Wednesday, April 04, 2007 6:04 AM
> To: The Friday Morning Applied Complexity Coffee Group
> Subject: Re: [FRIAM] One of my projects
>
>
> Phil Henshaw wrote:
> > It's missing the scientific reality component though, the step of
> > identifying how what we think is *different from* reality.
> If it's possible to identify how a model is thought be not
> representative of reality, then new mechanisms can be added. Then,
> differences in model behavior can be quantified both relative to the
> simpler model and relative to some set of data metrics that
> characterize
> known `reality'.
> > Still, don't you need some sort of method of a) validation
> of results
> e.g. Doug mentioned the 1918 pandemic flu:
>
http://www.mail-archive.com/friam at redfish.com/msg01646.html
and b) finding patterns in the discrepancy in the results found?
Suppose one posited that temperature had some role in the distribution
of a pathogen in some environment.
Data could be collected from actual cool and warm environments (by
experiment or from a historical account). Meanwhile a model could
implement the hypothesis about the role of temperature. If the
distribution of the pathogen in the model doesn't match the data, then
the model can be elaborated or changed. Or one can consider collecting
more precise data if the model suggests finer distinctions in outcomes
than could otherwise be witnessed. Rinse and repeat until the model
acts like reality. Now try predicting distributions from new datasets.
No earthshaking changes to scientific method here. It's just that the
predictions can be high dimensional if needed and model mechanisms
aren't constrained to be analytically tractable.
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