Posted by
Marcus G. Daniels on
URL: http://friam.383.s1.nabble.com/One-of-my-projects-tp523632p523638.html
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.