For different take on very similar topic check this discussion between me and Ben Pace (my reasoning was based on the same Sinatra paper).
For practical purposes, in case of scientists, one of my conclusions was
Translating into the language of digging for gold, the prospectors differ in their speed and ability to extract gold from the deposits (Q). The gold in the deposits actually is randomly distributed. To extract exceptional value, you have to have both high Q and be very lucky. What is encouraging in selecting the talent is the Q seems relatively stable in the career and can be usefully estimated after ~20 publications. I would guess you can predict even with less data, but the correct “formula” would be trying to disentangle interestingness of the problems the person is working on from the interestingness of the results.
2.
For practical purposes, my impression is some EA recruitment efforts could be more often at risk of over-filtering by ex-ante proxies and being bitten by tails coming apart, rather than at risk of not being selective enough.
Also, often the practical optimization question is how much effort you should spend on on how extreme tail of the ex-ante distribution.
3.
Meta-observation is someone should really recommend more EAs to join the complex systems / complex networks community.
Most of the findings from this research project seem to be based on research originating in complex networks community, including research directions such as “science of success”, and there is more which can be readily used, “translated” or distilled.
1.
For different take on very similar topic check this discussion between me and Ben Pace (my reasoning was based on the same Sinatra paper).
2.
For practical purposes, my impression is some EA recruitment efforts could be more often at risk of over-filtering by ex-ante proxies and being bitten by tails coming apart, rather than at risk of not being selective enough.
Also, often the practical optimization question is how much effort you should spend on on how extreme tail of the ex-ante distribution.
3.
Meta-observation is someone should really recommend more EAs to join the complex systems / complex networks community.
Most of the findings from this research project seem to be based on research originating in complex networks community, including research directions such as “science of success”, and there is more which can be readily used, “translated” or distilled.