I definitely agree that combining models—especially by averaging them in some way—is very blackboxy. The individual models being averaged can each be gears-level models, though.
Circling back to my main definition: it’s the top-level division which makes a model gearsy/non-gearsy. If the top-level is averaging a bunch of stuff, then that’s a black-box model, even if it’s using some gears-level models internally. If the top-level division contains gears, then that’s a gears-level model, even if the gears themselves are black boxes. (Alternatively, we could say that “gears” vs “black box” is a characterization of each level/component of the model, rather than a characterization of the model as a whole.)
I’m curious if you agree with the conception of gears being capital investments towards specific expertise, and black boxes being capital investments towards generalizable advantage.
I don’t think black boxes are capital investments towards generalizable advantage. Black box methods are generalizable, in the sense that they work on basically any system. But individual black-box models are not generalizable—a black-box method needs to build a new model whenever the system changes. That’s why black-box methods don’t involve an investment—when a black-box method encounters a new problem/system, it starts from scratch. Something like “learn how to do A/B tests” is an investment in learning how to apply a black-box method, but the A/B tests themselves are not an investment (or to the extent they are, they’re an investment which depreciates very quickly) - they won’t pay off over a very long time horizon.
So learninghow to apply a black-box method, in general, is a capital investment towards generalizable advantage. But actually using a black-box method—i.e. producing a black-box model—is usually not a capital investment.
(BTW, learning how to produce gears-level models is a capital investment which makes it cheaper to produce future capital investments.)
I definitely agree that combining models—especially by averaging them in some way—is very blackboxy. The individual models being averaged can each be gears-level models, though.
Circling back to my main definition: it’s the top-level division which makes a model gearsy/non-gearsy. If the top-level is averaging a bunch of stuff, then that’s a black-box model, even if it’s using some gears-level models internally. If the top-level division contains gears, then that’s a gears-level model, even if the gears themselves are black boxes. (Alternatively, we could say that “gears” vs “black box” is a characterization of each level/component of the model, rather than a characterization of the model as a whole.)
I don’t think black boxes are capital investments towards generalizable advantage. Black box methods are generalizable, in the sense that they work on basically any system. But individual black-box models are not generalizable—a black-box method needs to build a new model whenever the system changes. That’s why black-box methods don’t involve an investment—when a black-box method encounters a new problem/system, it starts from scratch. Something like “learn how to do A/B tests” is an investment in learning how to apply a black-box method, but the A/B tests themselves are not an investment (or to the extent they are, they’re an investment which depreciates very quickly) - they won’t pay off over a very long time horizon.
So learning how to apply a black-box method, in general, is a capital investment towards generalizable advantage. But actually using a black-box method—i.e. producing a black-box model—is usually not a capital investment.
(BTW, learning how to produce gears-level models is a capital investment which makes it cheaper to produce future capital investments.)