Isn’t the whole point of the outside view, as laid out in Eliezer’s original post, that sometimes you can get a better prediction by deliberately ignoring relevant inside view evidence? We need an algorithm to determine which inside view evidence to ignore, and the optimal algorithm clearly can’t be either “all” or “none”.
This is exactly the right way to state it. The question is, when is it better to ignore evidence? More precisely, when is it better for a human to ignore evidence? What, if any, biases and limitations of the human mind make inside-view reasoning dangerous?
This is an empirical question, to be settled by a study of human cognition. It’s not an abstract epistemological question that can be settled by arm-chair reasoning.
This is exactly the right way to state it. The question is, when is it better to ignore evidence? More precisely, when is it better for a human to ignore evidence? What, if any, biases and limitations of the human mind make inside-view reasoning dangerous?
I failed to convey an idea of my answer in “Consider representative data sets”. Prototypes that come to mind in planning are not representative (they are about efficient if-all-goes-well plans and not their real-world outcomes), and so should either be complemented by more concepts to make up representative data sets (which doesn’t work in practice), or forcefully excluded from consideration. The deeper the inside view the better, unless you are arriving at an answer intuitively under the conditions of predictably tilted availability.
This is exactly the right way to state it. The question is, when is it better to ignore evidence? More precisely, when is it better for a human to ignore evidence? What, if any, biases and limitations of the human mind make inside-view reasoning dangerous?
This is an empirical question, to be settled by a study of human cognition. It’s not an abstract epistemological question that can be settled by arm-chair reasoning.
I failed to convey an idea of my answer in “Consider representative data sets”. Prototypes that come to mind in planning are not representative (they are about efficient if-all-goes-well plans and not their real-world outcomes), and so should either be complemented by more concepts to make up representative data sets (which doesn’t work in practice), or forcefully excluded from consideration. The deeper the inside view the better, unless you are arriving at an answer intuitively under the conditions of predictably tilted availability.