I mean, depending on what you mean by “an okay approach sometimes… especially when you want to do something quick and dirty” I may agree with you! What I said was:
This is not Tetlock’s advice, nor is it the lesson from the forecasting tournaments, especially if we use the nebulous modern definition of “outside view” instead of the original definition.
I guess I was reacting to the part just after the bit you quoted
For an entire book written by Yudkowsky on why the aforementioned forecasting method is bogus
Which I took to imply “Daniel thinks that the aforementioned forecasting method is bogus”. Maybe my interpretation was incorrect. Anyway, seems very possible we in fact roughly agree here.
Re your 1, 2, 3, 4: It seems cool to try doing 4, and I can believe it’s better (I don’t have a strong view). Fwiw re 1 vs 2, my initial reaction is that partitioning by outside/inside view lets you decide how much weight you give to each, and maybe we think that for non-experts it’s better to mostly give weight to the outside view, so the partitioning performed a useful service. I guess this is kind of what you were trying to argue against and unfortunately you didn’t convince me to repent :).
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.
Nice, thanks for this!
I guess I was reacting to the part just after the bit you quoted
Which I took to imply “Daniel thinks that the aforementioned forecasting method is bogus”. Maybe my interpretation was incorrect. Anyway, seems very possible we in fact roughly agree here.
Re your 1, 2, 3, 4: It seems cool to try doing 4, and I can believe it’s better (I don’t have a strong view). Fwiw re 1 vs 2, my initial reaction is that partitioning by outside/inside view lets you decide how much weight you give to each, and maybe we think that for non-experts it’s better to mostly give weight to the outside view, so the partitioning performed a useful service. I guess this is kind of what you were trying to argue against and unfortunately you didn’t convince me to repent :).
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.