Iām not sure I understand. I donāt think what I said above requires that it be the case that ā[most or all] different tasks within a promising cause area are generally goodā (it sounds like you were implying āmost or allā?). I think it just requires that the mean prioritisation-worthiness of tasks in some cause, or the prioritisation-worthiness of the identifiable positive outliers among tasks in some cause, are substantially better than the equivalent things for another cause area.
I think that phrasing is somewhat tortured, sorry. What Iām picturing in my head is bell curves that overlap, but one of which has a hump notably further to the right, or one of which has a tail that extends further. (Though Iām not claiming bell curves are actually the appropriate distribution; thatās more like a metaphor.)
E.g., I think that one will do more good if one narrows oneās search to ālongtermist interventionsā rather than āeither longtermist or present-day developed-world human interventionsā. And I more tentatively believe the same when it comes to longtermist vs global health & dev. But I think itās likely that some interventions one could come up with for longtermist purposes would be actively harmful, and that others would be worse than some unusually good present-day-developed-world human interventions.
Yea, sorry for trying to rush it and not being clear. The main point I took from what you said in the comment I replied to was something like āEarly on in oneās career, it is really useful to identify a cause area to work in and over time to filter the best tasks within that cause areaā. I think that it might be useful to understand better when that statement is true, and I gave two examples where it seems correct.
I think that there are two important cases where that is true:
If the cause area is one where generally working toward it will improve understanding of the whole cause area and improve oneās ability to identify and shift direction to the most promising tasks later on.
For example, Animal Welfare might arguably not be such a cause because it is composed of at least three different clusters which might not intersect much in their related expertise and reasons for prioritization (alternative proteins, animal advocacy and wild animal welfare). However, these clusters might score well on that factor as sub-cause areas.
If it is generally easy to find promising tasks within that cause area.
Here I mostly agree with the overlapping bell curves picture, but want to explicitly point out that we are talking about task-prioritization done by novices.
Iām not sure I understand. I donāt think what I said above requires that it be the case that ā[most or all] different tasks within a promising cause area are generally goodā (it sounds like you were implying āmost or allā?). I think it just requires that the mean prioritisation-worthiness of tasks in some cause, or the prioritisation-worthiness of the identifiable positive outliers among tasks in some cause, are substantially better than the equivalent things for another cause area.
I think that phrasing is somewhat tortured, sorry. What Iām picturing in my head is bell curves that overlap, but one of which has a hump notably further to the right, or one of which has a tail that extends further. (Though Iām not claiming bell curves are actually the appropriate distribution; thatās more like a metaphor.)
E.g., I think that one will do more good if one narrows oneās search to ālongtermist interventionsā rather than āeither longtermist or present-day developed-world human interventionsā. And I more tentatively believe the same when it comes to longtermist vs global health & dev. But I think itās likely that some interventions one could come up with for longtermist purposes would be actively harmful, and that others would be worse than some unusually good present-day-developed-world human interventions.
Yea, sorry for trying to rush it and not being clear. The main point I took from what you said in the comment I replied to was something like āEarly on in oneās career, it is really useful to identify a cause area to work in and over time to filter the best tasks within that cause areaā. I think that it might be useful to understand better when that statement is true, and I gave two examples where it seems correct.
I think that there are two important cases where that is true:
If the cause area is one where generally working toward it will improve understanding of the whole cause area and improve oneās ability to identify and shift direction to the most promising tasks later on.
For example, Animal Welfare might arguably not be such a cause because it is composed of at least three different clusters which might not intersect much in their related expertise and reasons for prioritization (alternative proteins, animal advocacy and wild animal welfare). However, these clusters might score well on that factor as sub-cause areas.
If it is generally easy to find promising tasks within that cause area.
Here I mostly agree with the overlapping bell curves picture, but want to explicitly point out that we are talking about task-prioritization done by novices.