I address the points you mention in my response to Carl.
It also doesn’t solve issues like Sam Bankman-Fried mentioned where according to some argument one cause area is 44 orders of magnitude more impactful, because even if the two causes are multiplicative, if I understand correctly this would imply a resource allocation of 1:10^44, which is effectively the same as going all in on the large cause area.
I don’t think this is understanding the issue correctly, but it’s hard to say since I am a bit confused what you mean by ‘more impactful’ in the context of multiplying variables. Could you give an example?
I guess when I say “more impactful” I mean “higher output elasticity”.
We can go with the example of x-risk vs poverty reduction (as mentioned by Carl as well). If we were to think that allocating resources to reduce x-risk has an output elasticity 100,000 higher than poverty reduction, but reducing poverty improves the future, and reducing x-risk makes reducing poverty more valuable, then you ought to handle them multiplicatively instead of additively, like you said.
If you’d have 100,001 resources to spend, that’d mean 100,000 units against x-risk and 1 unit for poverty reduction, as opposed to the 100,001 for x-risk and 0 for poverty reduction when looking at them independently(/additively). Sam implies the additive reasoning in such situations is erroneous, after mentioning an example with such a massive discrepancy in elasticity. I’m pointing out that this does not seem to really make a difference in such cases, because even with proportional allocation it is effectively the same as going all in on (in this example) x-risk.
Anyway, not claiming that this makes the multiplicative approach incorrect (or rather, less correct than additive), just saying that in this case which is mentioned as one of the motivations for this, it really doesn’t make much of a difference (though things like diminishing returns would). Maybe this would have been more fitting as a reply to Sam than you, though!
What you’re saying is correct if you’re assuming that so far zero resources have been spent on x-risk reduction and global poverty. (Though that isn’t quite right either: You can’t compute an output elasticity if you have to divide by 0.)
But you are supposed to compare the ideal output elasticity ratio with how resources are being spent currently, those ratios are supposed to be equal locally. So using your example, if there were currently more than 1mil times as many resources spent on x-risk than global poverty, global poverty should be prioritised.
When I was running the numbers, my impression was that global wellbeing increases had a much bigger output elasticity than x-risk reduction. I found it a bit tricky to find numbers for global (not just EA) x-risk reduction efforts, so I’m not confident and also not confident how large the gap in resource spending is. 80k quotes $500 billion per year for resources spent on global wellbeing increases.
I address the points you mention in my response to Carl.
I don’t think this is understanding the issue correctly, but it’s hard to say since I am a bit confused what you mean by ‘more impactful’ in the context of multiplying variables. Could you give an example?
I guess when I say “more impactful” I mean “higher output elasticity”.
We can go with the example of x-risk vs poverty reduction (as mentioned by Carl as well). If we were to think that allocating resources to reduce x-risk has an output elasticity 100,000 higher than poverty reduction, but reducing poverty improves the future, and reducing x-risk makes reducing poverty more valuable, then you ought to handle them multiplicatively instead of additively, like you said.
If you’d have 100,001 resources to spend, that’d mean 100,000 units against x-risk and 1 unit for poverty reduction, as opposed to the 100,001 for x-risk and 0 for poverty reduction when looking at them independently(/additively). Sam implies the additive reasoning in such situations is erroneous, after mentioning an example with such a massive discrepancy in elasticity. I’m pointing out that this does not seem to really make a difference in such cases, because even with proportional allocation it is effectively the same as going all in on (in this example) x-risk.
Anyway, not claiming that this makes the multiplicative approach incorrect (or rather, less correct than additive), just saying that in this case which is mentioned as one of the motivations for this, it really doesn’t make much of a difference (though things like diminishing returns would). Maybe this would have been more fitting as a reply to Sam than you, though!
What you’re saying is correct if you’re assuming that so far zero resources have been spent on x-risk reduction and global poverty. (Though that isn’t quite right either: You can’t compute an output elasticity if you have to divide by 0.)
But you are supposed to compare the ideal output elasticity ratio with how resources are being spent currently, those ratios are supposed to be equal locally. So using your example, if there were currently more than 1mil times as many resources spent on x-risk than global poverty, global poverty should be prioritised.
When I was running the numbers, my impression was that global wellbeing increases had a much bigger output elasticity than x-risk reduction. I found it a bit tricky to find numbers for global (not just EA) x-risk reduction efforts, so I’m not confident and also not confident how large the gap in resource spending is. 80k quotes $500 billion per year for resources spent on global wellbeing increases.