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/[email protected]/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. ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
