I’m not planning on continuing a long thread here, I mostly wanted to help address the questions about my previous comment, so I’ll be moving on after this.
Fair, as this is outside of the scope of the original post. I noticed you did not comment on RP’s neuron counts post. I think it would be valuable if you commented there about the concerns you expressed here, or did you already express them elsewhere in another post of RP’s moral weight project sequence?
First, this effect (computational scale) is smaller for chickens but progressively enormous for e.g. shrimp or lobster or flies.
I agree that is the case if one combines the 2 wildly different estimates for the welfare range (e.g. one based on the number of neurons, and another corresponding to RP’s median welfare ranges) with a weighted mean. However, as I commented above, using the geometric mean would cancel the effect.
Suppose we compared the mass of the human population of Earth with the mass of an individual human. We could compare them on 12 metrics, like per capita mass, per capita square root mass, per capita foot mass… and aggregate mass. If we use the equal-weighted geometric mean, we will conclude the individual has a mass within an order of magnitude of the total Earth population, instead of billions of times less.
Is this a good analogy? Maybe not:
Broadly speaking, giving the same weight to multiple estimates only makes sense if there is wide uncertainty with respect to which one is more reliable. In the example above, it would make sense to give negligible weight to all metrics except for the aggregate mass. In contrast, there is arguably wide uncertainty with respect to what are the best models to measure welfare ranges, and therefore distributing weights evenly is more appropriate.
One particular model on which we can put lots of weight on is that mass is straightforwardly additive (at least at the macro scale). So we can say the mass of all humans equals the number of humans times the mass per human, and then just estimate this for a typical human. In contrast, it is arguably unclear whether one can obtain the welfare range of an animal by e.g. just adding up the welfare range of its individual neurons.
Fair, as this is outside of the scope of the original post. I noticed you did not comment on RP’s neuron counts post. I think it would be valuable if you commented there about the concerns you expressed here, or did you already express them elsewhere in another post of RP’s moral weight project sequence?
I agree that is the case if one combines the 2 wildly different estimates for the welfare range (e.g. one based on the number of neurons, and another corresponding to RP’s median welfare ranges) with a weighted mean. However, as I commented above, using the geometric mean would cancel the effect.
Is this a good analogy? Maybe not:
Broadly speaking, giving the same weight to multiple estimates only makes sense if there is wide uncertainty with respect to which one is more reliable. In the example above, it would make sense to give negligible weight to all metrics except for the aggregate mass. In contrast, there is arguably wide uncertainty with respect to what are the best models to measure welfare ranges, and therefore distributing weights evenly is more appropriate.
One particular model on which we can put lots of weight on is that mass is straightforwardly additive (at least at the macro scale). So we can say the mass of all humans equals the number of humans times the mass per human, and then just estimate this for a typical human. In contrast, it is arguably unclear whether one can obtain the welfare range of an animal by e.g. just adding up the welfare range of its individual neurons.