One aspect I didnt speak about that may be relevant here is the distinction between
irreducible uncertainty h (noise, entropy)
reducible uncertainty E (‘excess entropy’)
and forecasting complexity C (‘stochastic complexity’).
All three can independently vary in general.
Domains can be more or less noisy (more entropy h)- both inherently and because of limited observations
Some domains allow for a lot of prediction (there is a lot of reducible uncertainty E) while others allow for only limited prediction (eg political forecasting over longer time horizons)
And said prediction can be very costly to predict (high forecasting complexity C). Archeology is a good example: to predict one bit about the far past correctly might require an enormous amount of expertise, data and information. In other words it s really about the ratio between the reducible uncertainty and the forecasting complexity: E/C.
Some fields have very high skill ceiling but because of a low E/C ratio the net effect of more intelligence is modest. Some domains arent predictable at all, i.e. E is low. Other domains have a more favorable E/C ratio and C is high. This is typically a domain where there is a high skill ceiling and the leverage effect of addiitonal intelligence is very large.
[For a more precise mathematical toy model of h, E,C take a look at computational mechanics]
One aspect I didnt speak about that may be relevant here is the distinction between
irreducible uncertainty h (noise, entropy)
reducible uncertainty E (‘excess entropy’)
and forecasting complexity C (‘stochastic complexity’).
All three can independently vary in general.
Domains can be more or less noisy (more entropy h)- both inherently and because of limited observations
Some domains allow for a lot of prediction (there is a lot of reducible uncertainty E) while others allow for only limited prediction (eg political forecasting over longer time horizons)
And said prediction can be very costly to predict (high forecasting complexity C). Archeology is a good example: to predict one bit about the far past correctly might require an enormous amount of expertise, data and information. In other words it s really about the ratio between the reducible uncertainty and the forecasting complexity: E/C.
Some fields have very high skill ceiling but because of a low E/C ratio the net effect of more intelligence is modest. Some domains arent predictable at all, i.e. E is low. Other domains have a more favorable E/C ratio and C is high. This is typically a domain where there is a high skill ceiling and the leverage effect of addiitonal intelligence is very large.
[For a more precise mathematical toy model of h, E,C take a look at computational mechanics]