Ventana software support forum

For Don Taylor:

If you have performed a "full experiment" that changes all the salient
input variables (including those that switch in alternative structures),
then you can produce a variety of statistical plots. Many times the
distribution of the output uncertainty for a specific variable is not
gaussian. Nonetheless, you can produce parametric and non-parametric
confidence intervals around the mean and reference results (they can be
different). The non-parametric analysis simply counts the runs and produces
the 1, 5, 25, 50 75, 90, 95 and 99 percent (dynamic) confidence intervals,
for example. You can do the same parametrically if you assume (or test for)
a gaussian distribution. (As noted in an earlier note. Andy Ford describes
all this in his work using our HYPERSENS package.)

One can also make a plot of the partial correlation coefficients over time
for those input parameters contributing a cumulative 99% impact on the
uncertainty, for example. You can them see how certain parameters affect
results with a different intensity over time (and behavioral mode). Many
parameters only come in to play for small intervals/conditions.

A scatter plot of results is actually one of the most informative. When you
notice an "unusual" run, you can retrieve it and dissect it to see what
happened. Often you realize that a structure is faulty or that a mechanism
must be missing. In some cases the effect is real and a specific set of
input conditions knock the system into a new operating regime. You then
either need to control the system to stay out of that mode or take
advantage of the situation by "forcing" the system to stay in that
operating mode (if that is a more preferable state).

Lastly, the scatter plot (if its in lots of colors) can also show if chaos
kicks in anywhere and what its impact on the over system performance is
(often negligible for the major output conditions of interest). Often, but
not always, this chaos is unrealistic. The real world has stayed away from
it by evolutionary system changes or the parameters really dont vary by
the degree necessary to cause it . In the later case of input parameter
variance, a re-think often will indicate the that some of the input
parameters are not independent and that the selection of the parameter
values must come from a sampling of the interdependent ellipsoid-space
where one sample from it tells the values of multiple related input
parameters. (Andy Ford again discusses this issue.) Most often the
"experimental" chaos we see is due to this incorrect assumption of input
parameter variance independence.

George

George Backus
gbackus@boulder.earthnet.ne
Policy Assessment Corporation
14604 West 62nd Place
Arvada, Colorado USA 80004-3621
Bus: +1-303-467-3566
Fax: +1-303-467-3576