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>a verbal/symbolic model meant to account for the complexity of the world I think you and I have very different notions of the word "model." Rational spherical
by GeneralMayhem 11y ago
>a verbal/symbolic model meant to account for the complexity of the world
I think you and I have very different notions of the word "model." Rational spherical cows in a vacuum may not be precise or nuanced, but that doesn't make them inaccurate, and the simplified view is a useful model for learning about big-picture concepts. It's true that it won't catch every corner case, and when you're making decisions about human beings sometimes that's dangerous, but there's value at every level of granularity. Looking at each grain of sand and learning everything about the rock it came from will let you put it in the perfect place, but you'll never build a sandcastle that way, and if you want to reshape the whole beach you'll need yet another level of focus.
Generalization is a good thing. It's powerful. Humans got where we are by spotting patterns - not because everything matches the pattern perfectly all the time, or even ever, but because you can see general commonalities and start to abstract them away into something that can be reasoned about.
If there's one theory that explains more of the detail and variation in the world than another, at a finer granularity, without giving up generality, then yes, it's clearly a superior theory. But throwing out highly general theories just because they aren't perfect is intellectually lazy.
- ppod 11y agoExactly, I thought the original article was brilliantly written, but I'm surprised it didn't mention these related concepts: https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation https://en.wikipedia.org/wiki/On_Exactitude_in_Science https://en.wikipedia.org/wiki/On_Exactitude_in_Science https://en.wikipedia.org/wiki/Bonini%27s_paradox https://en.wikipedia.org/wiki/Bonini%27s_paradox
- x3n0ph3n3 11y agoSociology is full of chaotic systems, so slight deviations in the premises can have wide effects on the outcome of the system you are describing. I would replace your cow analogy with a 2-pendulum analogy. Depending on the nuances of what is being observed, a small change in the inputs of your system can create dramatically different behaviors. It's important to account for how you define your initial conditions and variables so that you don't end up with a model that matches early in a simulation but ultimately diverges into something that looks nothing like what can be observed in the world.
- karmakaze 11y agoI don't buy the 2-pendulum analogy. Nuance, as I read in the article, is about detection of ever decreasing observed quantities ad nauseam. It would be fine-grained discussion of the non-chaotic ranges of said pendulum. Basically a pissing contest, which is as good as any, but still just that. Once it goes beyond any practical use, has no more value.