Yeah, I probably shouldn’t have said “bogus” there, since while I do think it’s overrated, it’s not the worst method. (Though arguably things can be bogus even if they aren’t the worst?)
Partitioning by any X lets you decide how much weight you give to X vs. not-X. My claim is that the bag of things people refer to as “outside view” isn’t importantly different from the other bag of things, at least not more importantly different than various other categorizations one might make.
I do think that people who are experts should behave differently than people who are non-experts. I just don’t think we should summarize that as “Prefer to use outside-view methods” where outside view = the things on the First Big List. I think instead we could say: --Use deference more —Use reference classes more if you have good ones (but if you are a non-expert and your reference classes are more like analogies, they are probably leading you astray) --Trust your models less —Trust your intuition less —Trust your priors less ...etc.
Yeah, I probably shouldn’t have said “bogus” there, since while I do think it’s overrated, it’s not the worst method. (Though arguably things can be bogus even if they aren’t the worst?)
Partitioning by any X lets you decide how much weight you give to X vs. not-X. My claim is that the bag of things people refer to as “outside view” isn’t importantly different from the other bag of things, at least not more importantly different than various other categorizations one might make.
I do think that people who are experts should behave differently than people who are non-experts. I just don’t think we should summarize that as “Prefer to use outside-view methods” where outside view = the things on the First Big List. I think instead we could say:
--Use deference more
—Use reference classes more if you have good ones (but if you are a non-expert and your reference classes are more like analogies, they are probably leading you astray)
--Trust your models less
—Trust your intuition less
—Trust your priors less
...etc.