I think this same basic formula is behind the argument for majoritarianism: the crowd’s consensus (average) squared-error, plus the variance in the crowd, equals the expected squared-error for a random person in the crowd. Therefore the crowd consensus view has a lower expected squared-error than the average squared error for individuals in the crowd. Hence a random participant will do better to substitute the crowd consensus for his own estimate.
I’m reading a book, “The Difference”, by Scott E. Page, which discusses how and when crowds do well, and he calls it the Diversity Prediction Theorem: Given a crowd of predictive models, Collective Error = Average Individual Error—Prediction Diversity.
I think this same basic formula is behind the argument for majoritarianism: the crowd’s consensus (average) squared-error, plus the variance in the crowd, equals the expected squared-error for a random person in the crowd. Therefore the crowd consensus view has a lower expected squared-error than the average squared error for individuals in the crowd. Hence a random participant will do better to substitute the crowd consensus for his own estimate.
I’m reading a book, “The Difference”, by Scott E. Page, which discusses how and when crowds do well, and he calls it the Diversity Prediction Theorem: Given a crowd of predictive models, Collective Error = Average Individual Error—Prediction Diversity.