Glen,
> 
> Phil Henshaw on 12/06/2007 10:53 AM:
> > The hard part seems to be to take the first dark step to accepting 
> > there might be a shape of another form that the measures 
> are missing 
> > (like the whole tree or person).  It means looking for how to best 
> > extend and complete your image based on the limited cast of the 
> > measures at hand. Interpolation gone wild?? Free form projection 
> > perhaps??  Sort of... You just gotta do something to make 
> sense of the 
> > larger continuities that develop in natural complex 
> systems.  What I 
> > think we can see clearly is that our measures and models are highly 
> > incomplete.
> 
> I think we agree, which normally means there's nothing to 
> talk about! [grin]  But, I thought I'd throw out my term for 
> what you're describing:  "triangulation".
> 
> It's not really triangulation, of course.  But it's certainly 
> more like triangulation than, say, population sampling.  
> Perhaps we could call it "tuple-angulation"???  [grin]

PH: I guess I just call it filling in the gaps, understanding that as a
combination of analysis and synthesis.   So, if 'gaps' then become a raw
material for systems science part of what makes a model 'good' is if you
can see how it is also interestingly 'bad', since without having some
interest in the 'bad' you can't be tracking the usually moving and
significantly misrepresented targets of the physical system.. :-,)

I do come close to 'triangulation' in my derivative reconstruction
method, except I use 4 points to find a 5th rather than 2 points to find
a 3rd.  Given 5 points in time sequence it imputes a new value for the
middle one, based on the making the implied 3rd derivatives from right
and left the same (going forward and back in separate passes and
averaging).  If each point is considered a separate "bad" model for the
system one could impute an average value and a system having a single
fixed average state.   Using derivative reconstruction imputes a
continuous complex process without fixed definition instead.   That
seems to be a less distorting way of data smoothing, and more useful for
raising questions about the turning points within the changing
mechanisms producing it.



> Here's a paper in which "we" (i.e. my outrageous rhetoric is 
> reigned in and made coherent by the authors of the paper ;-) 
> try to describe it:
> 
   http://www.biomedcentral.com/1752-0509/1/14/abstract

>See Figure 1.  This particular example is just one sub-type of the
general method we're talking
>about, here, though.

PH:  I was impressed with the clarity of the abstract and their not
confusing biology, lab chemistry and computer model references.  Figure
1 puzzles me though.  I get your suggestion that this shows a way people
are using new visualization techniques to compare models.   I don't
understand how highly complex comparisons of test tube and computer
based things would make them look so very much alike unless both are
parametric data displays of a sort not described, though.   Comparing
hugely complicated systems does need visualization help, certainly, but
if that's what makes the images look so much alike it should be
mentioned.  Still, what I get from the picture is that they give
themselves an A+.  I don't see how their model recreates some features
of the natural process and interestingly leaves others out.  It's
importantly that art of making what you've failed to account for
interesting, rather than hiding it, that I find missing in lots of
studies.

So, here's to all 'bad' models...!  may we survive them...:-)

Best,

Phil
- --
glen e. p. ropella, 971-219-3846, http://tempusdictum.com
If this were a dictatorship, it would be a heck of a lot easier, just so
long as I'm the dictator. -- George W. Bush

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