I agree different comparisons are relevant in different situations.
A comparison with the median is also helpful, since it e.g. tells us the gain that the people currently doing the bottom 50% of interventions could get if they switched.
Though I think the comparison to the mean is very relevant (and hasn’t had enough attention) since it’s the effectiveness of what the average person donates to, supposing we don’t know anything about them. Or alternatively it’s the effectiveness you end up with if you pick without using data.
I think you’d need to show why this mean-over-median approach is correct to apply to strategy selection but incorrect to apply to cause area selection. Couldn’t you equally argue that regression to the mean indicates we’ll make errors in thinking some cause areas are 1000x more important or neglected than others?
Yes absolutely.
I think regression to the mean is a bigger issue for cause selection than solution selection. I’ve tried to take this into account when thinking about between-cause differences, but could have underestimated it.
Basically, I think it’s easier to pick the top 1% of causes than the top 1% of solutions, and there’s probably also greater variance between causes.
(One way to get an intuition for this is that only <0.001% of world GDP goes into targeted xrisk reduction or ending factory farming, while ~10% of world GDP is spent on addressing social issues in rich countries.)
I agree different comparisons are relevant in different situations.
A comparison with the median is also helpful, since it e.g. tells us the gain that the people currently doing the bottom 50% of interventions could get if they switched.
Though I think the comparison to the mean is very relevant (and hasn’t had enough attention) since it’s the effectiveness of what the average person donates to, supposing we don’t know anything about them. Or alternatively it’s the effectiveness you end up with if you pick without using data.
Yes absolutely.
I think regression to the mean is a bigger issue for cause selection than solution selection. I’ve tried to take this into account when thinking about between-cause differences, but could have underestimated it.
Basically, I think it’s easier to pick the top 1% of causes than the top 1% of solutions, and there’s probably also greater variance between causes.
(One way to get an intuition for this is that only <0.001% of world GDP goes into targeted xrisk reduction or ending factory farming, while ~10% of world GDP is spent on addressing social issues in rich countries.)