axioman

Karma: 234

axioman May 29, 2018, 8:51 PM
1 point
0 ∶ 0
in reply to: Rohin Shah’s comment on: The counterfactual impact of agents acting in concert
At this point, i think that to analyze the $1bn case correctly, you’d have to substract everyone’s opportunity cost in the calculation of the shapley value (if you want to use it here). This way, the example should yield what we expect.

I might do a more general writeup about shapley values, their advantages, disadvantages and when it makes sense to use them, if i find the time to read a bit more about the topic first.

axioman May 29, 2018, 12:52 PM
2 points
0 ∶ 0
in reply to: Emanuele_Ascani’s comment on: Expected cost per life saved of the TAME trial
I think, it might be best to just report confidence intervals for your final estimates (guesstimate should give you those). Then everyone can combine your estimates with their own priors on general intervention’s effectiveness and thereby potentially correct for the high levels of uncertainty (at least in a crude way by estimating the variance from the confidence intervals).

The variance of X can be defined as E[X^2]-E[X]^2, which should not be hard to implement in Guesstimate. However, i am not sure, whether or not having the variance yields to more accurate updating, than having a confidence interval. Optimally you’d have the full distribution, but i am not sure, whether anyone will actually do the maths to update from there. (But they could get it roughly from your guesstimate model).

I might comment more on some details and the moral assumptions, if i find the time for it soon.

axioman May 28, 2018, 2:35 PM
1 point
0 ∶ 0
in reply to: Emanuele_Ascani’s comment on: Three levels of cause prioritisation
I disagree. If we are fairly certain, that the average intervention in Cause X is 10 times more effective than the average Intervention in Cause Y (For a comparision, 80000 hours currently believes, that AI-safety work is 1000 times as effective as global health), it seems like we should strongly prioritize Cause X. Even if there are some interventions in Cause Y, which are more effective, than the average intervention in Cause X, finding them is probably as costly as finding the most effective interventions in Cause X (Unless there is a specific reason, why evaluating cost effectiveness in Cause X is especially costly, or the distributions of Intervention effectiveness are radically different between both causes). Depending on how much we can improve on our current comparative estimates of cause effctiveness, the potential impact of doing so could be quite high, since it is essentially multiplies the effects of our lower level prioritization. Therefore it seems, like high to medium level prioritization in combination with low-level prioritization restricted to the best causes seems the way to go. On the other hand, it seems at least plausible, that we cannot improve our high-level prioritization significantly at the moment and should therefore focus on the lower level within the most effective causes.

axioman May 28, 2018, 2:09 PM
2 points
0 ∶ 0
on: The counterfactual impact of agents acting in concert
“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.

axioman May 28, 2018, 9:06 AM
3 points
0 ∶ 0
on: Expected cost per life saved of the TAME trial
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.