Posted by
glen ep ropella on
URL: http://friam.383.s1.nabble.com/Seminal-Papers-in-Complexity-tp524047p524143.html
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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 multiple simplifying projections (models), the question of
which particular model is the correct one is confusing." Phil, if that
isn't a good paraphrase, please correct me.
But, if it is a good paraphrase, selectors like the AIC that assume some
ideal perfect (largest) description of the referent will NOT help. In
fact, they hinder understanding because they _imply_ that there is a
single, true, perfect, ideal, largest model, which is false.
Operationally, they do not help because the data are at least one
(probably many many more) modeling level(s) removed from the referent
system.
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 data (perhaps regulated by
the prior model)
5) repeat as necessary
Each data set is derived from a prior model. Hence, the best a
data-driven model selector can do is find the model(s) upon which the
data are based. It only targets the referent to the extent that the
original (usually mental) models target the referent.
And if those original models were induced by a perverse person with
perverse thoughts, then the original model is probably way off and
false. Hence, selectors like the AIC will only lead us to that false
model, not to the truth. Worse yet, because they used a rigorous
(hermeneutic) mathematical technique to find that false model, they will
be strongly inclined to believe in that false model. Just like the old
adage "Don't believe everything you read", we could state an analogous
adage in modeling and simulation: "Don't believe everything described
mathematically." Of course, those aphorisms are way too moderate.
[grin] In fact, to quote Sturgeon "Sure, 90% of science fiction is
crud. That's because 90% of everything is crud." So, we should change
the aphorism to "Don't believe 90% of the math you read."
> Phil Henshaw wrote:
>> It does confuse that we seem
>> to need to look at real systems with simplifying projections that
>> look different from each other. The answer as to which 2D
>> projection is the correct one is what seems most confusing.
>>
> Glen E. P. Ropella wrote:
>> My most heated was with a guy who claimed that model selectors (e.g.
>> AIC) that rely on a posited "perfect" or "optimal" model can actually
>> help one get at the true generator of whatever data set is being
>> examined. It took a lot of verbage on my part to draw a detailed enough picture for him to understand that the data were taken by a method that
>> presumed a model (because all observation requires a model and all
>> models require observations). And the best a selector can do is find
>> that occult model by which the data was collected.
- --
glen e. p. ropella, 971-219-3846,
http://tempusdictum.comShallow men believe in luck ... Strong men believe in cause and effect.
- -- Ralph Waldo Emerson
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