I mostly agree with this post, but I take issue with the summary:
EA cause prioritisation frameworks use probabilities derived from belief which arenāt based on empirical evidence. This means EA cause prioritisation is highly uncertain, which means that the optimal distribution of career focuses for engaged EAs should be less concentrated amongst a small number of ātopā cause areas.
As a Bayesian, you have to assign some subjective probabilities to things, and sometimes there just isnāt empirical evidence. To argue that e.g. 80k doesnāt have enough uncertainty (even if you have reasons to believe that high uncertainty is warranted in general), itās necessary to argue that their methodology is not only subject to irrationality, itās subject to bias in the direction you argue (overconfidence rather than underconfidence in top causes).
E.g. if their numbers for I, T, and 1/āN are based on point estimates for each number rather than expected values, then you underrate causes that have a small chance to be big /ā highly tractable. (I donāt know whether 80k actually has this bias.)
The main body of the post does address this briefly, but I would want more evidence, and I think the summary does not have valid standalone reasoning.
To take the concern that āsome of them could easily be wrong by a couple of pointsā literally, this could mean that factory farming could easily be on par with AI, or land use reform could easily be more important than biosecurity.
minor point, but land use reform wouldnāt be more important (in the sense of higher scale) than biosecurity, since they differ by 3 points (30x) in overall score but 6 points (1000x) in scale.
Hmm, Iām not sure you need to make an argument about the direction of the bias. Maybe I should be specifically mentioning imprecision due to potential irrationality?
The way Iām thinking about it, is that 80K have used some frameworks to come up with quantitative scores for how pressing each cause area is, and then ranked the cause areas by the point estimates.
But our imagined confidence intervals around the point estimates should be very large and presumably overlap for a large number of causes, so we should take seriously the idea that the ranking of causes would be different in a better model.
This means we need to take more seriously the idea that the true top causes are different to those suggested by 80Kās model.
Also sorry I accidentally used the word important instead of pressing! Will correct this.
Agree that this methodology of point estimates can be overconfident in what the top causes are, but Iām not sure if thatās their methodology or if theyāre using expected values where they should. Probably someone from 80k should clarify, if 80k still believes in their ranking enough to think anyone should use it?
This means we need to take more seriously the idea that the true top causes are different to those suggested by 80Kās model.
Also agree with this sentence.
My issue is that the summary claims āprobabilities derived from belief which arenāt based on empirical evidence [...] means that the optimal distribution of career focuses for engaged EAs should be less concentrated amongst a small number of ātopā cause areas.ā This is a claim that we should be less confident than 80kās cause prio.
When someone has a model, you canāt always say we should be less confident than their model without knowing their methodology, even if their model is āprobabilities derived from belief which arenāt based on empirical evidenceā. Otherwise you can build a model where their model is right 80% of the time, and things are different in some random way 20% of the time, and then someone else takes your model and does the same thing, and this continues infinitely until your beliefs are just the uniform distribution over everything. So I maintain that the summary should mention something about using point estimates inappropriately, or missing some kinds of uncertainty; otherwise itās saying something thatās not true in general.
ā80K have a cause prioritisation model with wide confidence intervals around point estimates, but as individual EAs, our personal cause prioritisation models should have wider confidence around point estimates than in 80Kās model.ā
What I meant to communicate is:
ā80K have a cause prioritisation model with wide confidence intervals around point estimates, and individual EAs should 1) pay more attention to the wide confidence intervals in 80Kās model than they are currently and 2) have wide confidence intervals in their personal cause prioritisation model too.ā
I mostly agree with this post, but I take issue with the summary:
As a Bayesian, you have to assign some subjective probabilities to things, and sometimes there just isnāt empirical evidence. To argue that e.g. 80k doesnāt have enough uncertainty (even if you have reasons to believe that high uncertainty is warranted in general), itās necessary to argue that their methodology is not only subject to irrationality, itās subject to bias in the direction you argue (overconfidence rather than underconfidence in top causes).
E.g. if their numbers for I, T, and 1/āN are based on point estimates for each number rather than expected values, then you underrate causes that have a small chance to be big /ā highly tractable. (I donāt know whether 80k actually has this bias.)
The main body of the post does address this briefly, but I would want more evidence, and I think the summary does not have valid standalone reasoning.
minor point, but land use reform wouldnāt be more important (in the sense of higher scale) than biosecurity, since they differ by 3 points (30x) in overall score but 6 points (1000x) in scale.
Hmm, Iām not sure you need to make an argument about the direction of the bias. Maybe I should be specifically mentioning imprecision due to potential irrationality?
The way Iām thinking about it, is that 80K have used some frameworks to come up with quantitative scores for how pressing each cause area is, and then ranked the cause areas by the point estimates.
But our imagined confidence intervals around the point estimates should be very large and presumably overlap for a large number of causes, so we should take seriously the idea that the ranking of causes would be different in a better model.
This means we need to take more seriously the idea that the true top causes are different to those suggested by 80Kās model.
Also sorry I accidentally used the word important instead of pressing! Will correct this.
Agree that this methodology of point estimates can be overconfident in what the top causes are, but Iām not sure if thatās their methodology or if theyāre using expected values where they should. Probably someone from 80k should clarify, if 80k still believes in their ranking enough to think anyone should use it?
Also agree with this sentence.
My issue is that the summary claims āprobabilities derived from belief which arenāt based on empirical evidence [...] means that the optimal distribution of career focuses for engaged EAs should be less concentrated amongst a small number of ātopā cause areas.ā This is a claim that we should be less confident than 80kās cause prio.
When someone has a model, you canāt always say we should be less confident than their model without knowing their methodology, even if their model is āprobabilities derived from belief which arenāt based on empirical evidenceā. Otherwise you can build a model where their model is right 80% of the time, and things are different in some random way 20% of the time, and then someone else takes your model and does the same thing, and this continues infinitely until your beliefs are just the uniform distribution over everything. So I maintain that the summary should mention something about using point estimates inappropriately, or missing some kinds of uncertainty; otherwise itās saying something thatās not true in general.
I think youāre interpreting my summary as:
ā80K have a cause prioritisation model with wide confidence intervals around point estimates, but as individual EAs, our personal cause prioritisation models should have wider confidence around point estimates than in 80Kās model.ā
What I meant to communicate is:
ā80K have a cause prioritisation model with wide confidence intervals around point estimates, and individual EAs should 1) pay more attention to the wide confidence intervals in 80Kās model than they are currently and 2) have wide confidence intervals in their personal cause prioritisation model too.ā