Re: the use of a special term like "artifact" or "explanandum", I agree 
completely. "Model" is as good as any.

Re: the usefulness of obtuse models - I did give a description of how obtuse 
(indeed, totally opaque) models can be useful for science. It's possible you 
didn't receive that post. So, here is the archive: 
http://friam.471366.n2.nabble.com/description-explanation-metaphor-model-tp7594030p7594294.html

It's arguable how large N must be for this to work well. But with progress in 
big data, generative AI, proof assistants, automatic programming, HoTT, etc., I 
think we're getting pretty close to shutting down any critics of the method. My 
own work requires only N=3: 1) the referents (i.e. validation data observed 
from it/them), 2) the reference model (usually an equation-based phenomena-only 
model), and 3) a finer-grained component-based model relying on both unit and 
systemic V&V. None of these are totally opaque, because we rely solely on open 
source stacks. (Though you could say none of us understands processors, cache, 
memory, transistors, etc.) But the *method* we're using prescribes that we 
*treat* them as opaque and rely solely on observations of each [†]. So, any 
validation/falsification we do can be reduced to data validation.

[†] I'm a broken record curmudgeon to my colleagues who keep treating 
verification data as if it were validation data. Pffft. FWIW, they also keep 
trying to use Matlab instead of Octave or R ... Grrr.

On 1/17/20 5:28 AM, Eric Charles wrote:
> I mean... assuming I know what you mean by "obtuse"... which I'm not sure 
> of... an "obtuse model" could be useful for many, many things... but the more 
> obtuse it is, the less one can science with it... [...]
-- 
☣ uǝlƃ

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