I don’t know what’s the correct super-smartness cluster, so I cannot make objective predictive definition, at least yet. There’s no need to suffer from physics envy here—a lot of useful knowledge has this kind of vagueness. Nobody managed to define “pornography” yet, and it’s far easier concept than “super-smartness”. This kind of speculation might end up with something useful with some luck (or not).
Even defining by example would be difficult. My canonical examples would be Feynman and Einstein—they seem far smarter than the “normally smart” people.
Let’s say I collected a sufficiently large sample of “people who seem super-smart”, got as accurate information about them as possible, and did a proper comparison between them and background of normally smart people (it’s pretty easy to get good data on those, even by generic proxies like education—so I’m least worried about that) in a way that would be robust against even large number of data errors. That’s about the best I can think of.
Unfortunately it will be of no use as my sample will be not random super-smart people but those super-smart people who are also sufficiently famous for me to know about them and be aware of their super-smartness. This isn’t what I want to measure at all. And I cannot think of any reasonable way to separate these.
So the project is most likely doomed. It was interesting to think about this anyway.
I don’t know what’s the correct super-smartness cluster, so I cannot make objective predictive definition, at least yet. There’s no need to suffer from physics envy here—a lot of useful knowledge has this kind of vagueness. Nobody managed to define “pornography” yet, and it’s far easier concept than “super-smartness”. This kind of speculation might end up with something useful with some luck (or not).
Even defining by example would be difficult. My canonical examples would be Feynman and Einstein—they seem far smarter than the “normally smart” people.
Let’s say I collected a sufficiently large sample of “people who seem super-smart”, got as accurate information about them as possible, and did a proper comparison between them and background of normally smart people (it’s pretty easy to get good data on those, even by generic proxies like education—so I’m least worried about that) in a way that would be robust against even large number of data errors. That’s about the best I can think of.
Unfortunately it will be of no use as my sample will be not random super-smart people but those super-smart people who are also sufficiently famous for me to know about them and be aware of their super-smartness. This isn’t what I want to measure at all. And I cannot think of any reasonable way to separate these.
So the project is most likely doomed. It was interesting to think about this anyway.