“The alternative approach (which I argue is wrong) is to say that each of the n A voters is counterfactually responsible for 1/n of the $10bn benefit. Suppose there are 10m A voters. Then each A voter’s counterfactual social impact is 1/10m*$10bn = $1000. But on this approach the common EA view that it is rational for individuals to vote as long as the probability of being decisive is not too small, is wrong. Suppose the ex ante chance of being decisive is 1/1m. Then the expected value of Emma voting is a mere 1/1m*$1000 = $0.001. On the correct approach, the expected value of Emma voting is 1/10m*$10bn = $1000. If voting takes 5 minutes, this is obviously a worthwhile investment for the benevolent voter, as per common EA wisdom.”

I am not sure, whether anyone is arguing for discounting twice. The alternative approach using the shapley value would divide the potential impact amongst the contributors, but not additionally account for the probability. Therefore, in this example both approaches seem to assign the same counterfactual impact.

More generally, it seems like most disagreements in this thread could be resolved by a more charitable interpretation of the other side (from both sides, as the validity of your argument against rohinmshah’s counterexample seems to show)

Right now, a comment from someone more proficient with the shapley value arguing against

“Also consider the $1bn benefits case outlined above. Suppose that the situation is as described above but my action costs $2 and I take one billionth of the credit for the success of the project. In that case, the Shapely-adjusted benefits of my action would be $1 and the costs $2, so my action would not be worthwhile. I would therefore leave $1bn of value on the table.”

might be helpful for a better understanding.

Interesting Analysis! Since you already have confidence intervals for a lot of your models factors, using the guesstimate web tool to get a more detailed idea of the uncertainty in the final estimate might be helpful, since some bayesian discounting based on estimate’s uncertainty might be a sensible thing to do. (https://www.lesswrong.com/posts/5gQLrJr2yhPzMCcni/the-optimizer-s-curse-and-how-to-beat-it)

It might also make sense to make your ethical assumptions more explicit in the beginning (https://www.givewell.org/how-we-work/our-criteria/cost-effectiveness/comparing-moral-weights), especially since the case against aging seems to be less intuitive than most of givewells interventions.