On 10/9/24 9:15 AM, glen wrote:
Uh oh. Originalism rears its head again! Are our aphorisms alive or dead?
I have been thinking about "compression" in the sense I think you (Glen)
use it often.
"All models are wrong, some are useful"
GPT 4o offers the following "compression" of my long-winded response here:
The (following) discussion argues that *aphorisms* are like
*compressed models* of reality, offering simplicity through
*abstraction* and *pattern recognition*, but sacrificing *precision*
and *detail* in the process. *Bisimulation* shows how this
simplification applies broadly to systems, not just finite ones. The
trade-off in models (and aphorisms) is that *loss of detail* is a
drawback, but utility remains through the patterns they reveal.
or not much shorter but perhaps more succinct:
The discussion compresses several ideas around *aphorisms* and
*models*, particularly focusing on how they function as *compressed
representations* of reality.
1.
*Aphorisms as Models*: The phrase "All models are wrong, some
are useful" is seen as a compressed model of a larger truth. The
*first part* ("All models are wrong") reflects the idea of
*dimension reduction* or *loss of detail* in models—when reality
is simplified, some accuracy is sacrificed. The *second part*
("some are useful") points to the utility of such models despite
their imperfections, a form of *discretization* or *limited
precision* where details are lost, but patterns remain recognizable.
2.
*Bisimulation*: This concept, initially thought to apply only to
*finite-state systems*, actually applies to *all
state-transition systems* as long as they adhere to an
abstraction that avoids issues of precision. This ties into the
idea that models, like aphorisms, rely on abstraction to remain
useful despite their inherent loss of detail.
3.
*Compression in Modeling*: The discussion suggests two types of
compression in models:
* *Abstraction and Pattern Recognition*: These are seen as
*features* or *goals* of modeling, focusing on extracting
useful insights.
* *Dimension Reduction and Loss of Detail*: These are
*utilitarian*, needed for practical reasons, but the loss of
detail is viewed more as an *inconvenience* than a valued trait.
The conversation touches on how these compressed forms of knowledge
(models and aphorisms) help with *pattern recognition* and
*simplification*, even though they inevitably sacrifice some
accuracy, which can be seen as a flaw but is sometimes necessary for
clarity and utility.
Now the original for the 0 or 2 people who might have endured this far:
The first clause (protasis?) seems to specifically invoke the
"dimension reduction" implications of "compression" but some of the
recent discussion here seems to invoke the "discretization" or more
aptly perhaps the "limited precision"? I think the stuff about
bisimulation is based on this difference?
The trigger for this flurry of "arguing about words" was Wilson's:
"We have Paleolithic emotions, medieval institutions, and
god-like technology."
to which there were varioius objections ranging from (paraphrasing):
"it is just wrong"
"this has been debunked"
to the ad-hominem:
"Wilson was once good at X but he should not be listened to for Y"
The general uproar *against* this specific aphorism seemed to be a
proxy for:
"it is wrong-headed" and "aphorisms are wrong-headed" ?
then Glen's objection (meat on the bones of "aphorisms are
wrong-headed"?) that aphorisms are "too short" which is what lead me
to thinking about aphorisms as models, models as a form or
expression of compression and the types of compression (lossy/not)
and how that might reflect the "bisimulation" concept
https://en.wikipedia.org/wiki/Bisimulation . At first I had the
"gotcha" or "aha" response to learning more about bisimulation that
it applied exclusively/implicitly to finite-state systems but in
fact it seems that as long as there is an abstraction that obscures
or avoids any "precision" issues it applies to all state-transition
systems.
This lead me to think about the two types of compression that models
(or aphorisms?) offer. One breakdown of the features of
compression in modeling are: Abstraction; Dimension Reduction; Loss
of Detail; Pattern Recognition. The first and last (abstraction
and pattern recognition) seem to be features/goals of modeling, The
middle two seem to be utilitarian while the loss of detail is more
of a bug, an inconvenience nobody values (beyond the utility of
keeping the model small and in the way it facilitates "pattern
recognition" in a ?perverse? way)
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