How would we tell the difference between ‘positive’ and ‘negative’ selection? If I imagine that I’ve got two scores x and y (00.9 AND y>0.9′ what you mean by ‘negative selection’, and ‘accept if x>0.995 OR y>0.995’ positive selection?
If I’m thinking along the right lines here, is there a general principle (like ‘acceptance set must be convex’?), or do I have the wrong end of some crucial stick?
To be a bit less abstract, if x and y are sportiness and intelligence, and they’re uniform, then the AND rule gives you bright, fit people (i.e. the sort you naturally like), with a few superstars, whereas the OR rule gives you uber-nerds who may or may not be sporty and uber-jocks who may or may not be bright.
In fact it also might depend on how x and y are distributed. If they’re uniform then the difference between the AND and the OR rule feels less pronounced than if they’re gaussian. I think that the gaussian/AND case is going to give you very few people who are good at both, whereas the uniform scores case gives you some.
Very thought-provoking. Thank you!
How would we tell the difference between ‘positive’ and ‘negative’ selection? If I imagine that I’ve got two scores x and y (00.9 AND y>0.9′ what you mean by ‘negative selection’, and ‘accept if x>0.995 OR y>0.995’ positive selection?
If I’m thinking along the right lines here, is there a general principle (like ‘acceptance set must be convex’?), or do I have the wrong end of some crucial stick?
To be a bit less abstract, if x and y are sportiness and intelligence, and they’re uniform, then the AND rule gives you bright, fit people (i.e. the sort you naturally like), with a few superstars, whereas the OR rule gives you uber-nerds who may or may not be sporty and uber-jocks who may or may not be bright.
In fact it also might depend on how x and y are distributed. If they’re uniform then the difference between the AND and the OR rule feels less pronounced than if they’re gaussian. I think that the gaussian/AND case is going to give you very few people who are good at both, whereas the uniform scores case gives you some.