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Sniffnoy comments on Negative and Positive Selection - Less Wrong
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I asked my father to read this and give his thoughts.
He says that positive selection only works well when you have a very good idea what you need to select for. If you're sending an athlete to the Olympics but the event he'll have to compete in will be chosen at random, you can't just choose the one with the best time on the 800 meter dash, because the event might end up being something like archery, fencing, or weightlifting. And you certainly wouldn't want to send a non-swimmer. If you need a generalist, seeing how well someone does at jumping through a wide variety of arbitrary hoops might really be the best test you can practically implement.
(Now I'm wondering just how good or bad the 800 meter dash actually is at predicting levels of success at unrelated sports. For example, could you tell the difference between an NHL-quality ice hockey player and one that plays on a minor league team just by looking at their times on the 800 meter dash?)
I don't think the right way to do this is not either positive or negative selection (those terms really suggest a false dichotomy, don't they?). As has been pointed out elsewhere, what's here being called "positive" is really "or", and what's here being called "negative" is really "and".
But there are lots more ways to combine the data into a single number then just "apply a cutoff to each one, and then apply some operation to the resulting booleans". The appropriate sort of selection is not positive or negative, but rather, whatever will be used in the actual competition. (And if it's unknown, apply expected utility, etc.)