So, one simple model which I expect to be a pretty good approximation: IQ/g-factor is a thing and is mostly not trainable, and then skills are roughly-independently-distributed after controlling for IQ.
For selection in this model, we can select for a high-g-factor group as the first step, but then we still run into the exponential problem as we try to select further within that group (since skills are conditionally independent given g-factor).
This won’t be a perfect approximation, of course, but we can improve the approximation as much as desired by adding more factors to the model. The argument for the exponential problem goes through: select first for the factors, and then the skills will be approximately-independent within that group. (And if the factors themselves are independent—as they are in many factor models—then we get the exponential problem in the first step too.)
So, one simple model which I expect to be a pretty good approximation: IQ/g-factor is a thing and is mostly not trainable, and then skills are roughly-independently-distributed after controlling for IQ.
For selection in this model, we can select for a high-g-factor group as the first step, but then we still run into the exponential problem as we try to select further within that group (since skills are conditionally independent given g-factor).
This won’t be a perfect approximation, of course, but we can improve the approximation as much as desired by adding more factors to the model. The argument for the exponential problem goes through: select first for the factors, and then the skills will be approximately-independent within that group. (And if the factors themselves are independent—as they are in many factor models—then we get the exponential problem in the first step too.)