Is the following a fair paraphrasing of your main hypothesis? (I’m leaving out some subtleties with conjunctive successes, but please correct the model in that way if it’s relevant.):
“”″ Each deleterious mutation multiplies your probability of succeeding at a problem/thought by some constant. Let’s for simplicity say it’s 0.98 for all of them.
Then the expected number of successes per time for a person is proportional to 0.98^num_deleterious_mutations(person).
So the model would predict that when Person A had 10 less deleterious mutations than person B, they would on average accomplish 0.98^10 ~= 0.82 times as much in a given timeframe. ”″”
I think this model makes a lot of sense, thanks!
In itself I think it’s insufficient to explain how heavytailed human intelligence is—there were multiple cases where Einstein seems to have been able to solve problems multiple times faster than the next runner ups. But I think if you use this model in a learning setting where success means “better thinking algorithms” then if you have 10 fewer deleterious mutations it’s like having 1⁄0.82 longer training time, and there might also be compounding returns from having better thinking algorithms to getting more and richer updates to them.
Not sure whether this completely deconfuses me about how heavytailed human intelligence is, but it’s a great start.
I guess at least the heavytail is much less significant evidence for my hypothesis than I initially thought (though so far I still think my hypothesis is plausible).
Thanks!
Is the following a fair paraphrasing of your main hypothesis? (I’m leaving out some subtleties with conjunctive successes, but please correct the model in that way if it’s relevant.):
“”″
Each deleterious mutation multiplies your probability of succeeding at a problem/thought by some constant. Let’s for simplicity say it’s 0.98 for all of them.
Then the expected number of successes per time for a person is proportional to 0.98^num_deleterious_mutations(person).
So the model would predict that when Person A had 10 less deleterious mutations than person B, they would on average accomplish 0.98^10 ~= 0.82 times as much in a given timeframe.
”″”
I think this model makes a lot of sense, thanks!
In itself I think it’s insufficient to explain how heavytailed human intelligence is—there were multiple cases where Einstein seems to have been able to solve problems multiple times faster than the next runner ups. But I think if you use this model in a learning setting where success means “better thinking algorithms” then if you have 10 fewer deleterious mutations it’s like having 1⁄0.82 longer training time, and there might also be compounding returns from having better thinking algorithms to getting more and richer updates to them.
Not sure whether this completely deconfuses me about how heavytailed human intelligence is, but it’s a great start.
I guess at least the heavytail is much less significant evidence for my hypothesis than I initially thought (though so far I still think my hypothesis is plausible).