Probably, but not a very useful one. It’s better to just use natural levels of abstraction like “human behavior” and recognize that this is not a gears level model for that level, but a heuristic. I can’t really think of a natural abstraction where weirdness points is usefully gearsy, rather than a heursitic.
Gears level is defined by prediction power at a given level of abstraction, heuristic is defined by something like… “speed/prediction power” at a typical level of abstraction, or something. Whether you want gears or heuristics really depends on how much time you have and how much time you’re going to spending with the model (typically heuristics).
Probably, but not a very useful one. It’s better to just use natural levels of abstraction like “human behavior” and recognize that this is not a gears level model for that level, but a heuristic. I can’t really think of a natural abstraction where weirdness points is usefully gearsy, rather than a heursitic.
“gears level” is defined in terms of usefulness, and so is “heuristic”
Gears level is defined by prediction power at a given level of abstraction, heuristic is defined by something like… “speed/prediction power” at a typical level of abstraction, or something. Whether you want gears or heuristics really depends on how much time you have and how much time you’re going to spending with the model (typically heuristics).
Are they really different? Why would you want to use a high abstraction level if not to save Compute?
Yes.
You do want to use it to save compute.
So why are they different?