I think this is actually a general pattern that happens in most knowledge worker careers, not only late in careers. Certainly when I was a career coach one of the key things I did to help people move up in their careers was to help them move a level up in their thinking.
I think one of the reasons that the particular meta-level up move that you’re talking about happens late in careers is that at that point people who are at the top of their careers basically don’t have another meta-level up they can move to understand their field—they’ve already made that move. So the only meta-level they can move to next is to apply the move to itself.
Let’s say I have a set of students, and a set of learning materials for an upcoming test. My goal is to run an experiment to see which learning materials are correlated with better scores on the test via multiple linear regression. I’m also going to make the simplifying assumption that the effects of the learning materials are independent.
I’m looking for an experimental protocol with the following conditions:
I want to be able to give each student as many learning materials as possible. I don’t want a simple RCT, but a factorial experiment where students get many materials and the statistics tease out the linear regression.
I have a prior about which learning materials will do better, I’d like to utilize this prior by originally distributing these materials to more students.
(Bonus) Students are constantly entering this class, I’d love to be able to do some multi-armed bandit thingy where as I get more data I continually change this prior.
I’ve looked at most of the links going from https://en.wikipedia.org/wiki/Optimal_design but they mostly show the mathematical interpretation of each method, not a clear explanation of in which conditions you’d use that method.
Thanks!