Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Glen E. P. Ropella
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Marcus G. Daniels wrote: > Fine, but more models won't help that problem.The data is the > data.In contrast, Phil's example would be addressed by AIC. How so? I'll reformulate Phil's statement as: "Because understanding a referent requires mu

Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Marcus G. Daniels
Glen E. P. Ropella wrote: > To be clear, the process works this way: > > 1) casual observation and psychological induction leads to a (usually > mental) model > 2) an experiment is designed based on that model > 3) data are taken from the experiment > 4) a more rigorous model is derived from the da

Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Glen E. P. Ropella
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Marcus G. Daniels wrote: > Glen E. P. Ropella wrote: >> To be clear, the process works this way: >> >> 1) casual observation and psychological induction leads to a (usually >> mental) model >> 2) an experiment is designed based on that model >> 3) data

Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Marcus G. Daniels
Glen E. P. Ropella wrote: > Well, I suppose that begs the question of what we mean by "system". In > the case of the financial machinery, it is clear how that part of the > thing works. But, it is not at all clear how the whole system works. > If it were, then predictive algorithms would be reaso

Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Glen E. P. Ropella
-BEGIN PGP SIGNED MESSAGE- Hash: SHA1 Marcus G. Daniels wrote: > It's price and risk that matters to quantitative traders. Price and > risk are measurable on any historical timescale that might be of > interest. Bias in data collection takes the form of "How do I reduce > all this in

Re: [FRIAM] cognitive largess (was Re: reductionism)

2007-06-28 Thread Marcus G. Daniels
Glen E. P. Ropella wrote: > But you're focusing on extrapolation, right? It strikes me that you're > not talking about heuristic (a.k.a. explanatory) models but about > aggregative extrapolation. More like looking for exploitable, repeatable cause/effect inefficiencies in an ocean of activity.