Comment author: Flodorner 29 May 2018 12:52:26PM *  1 point [-]

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.

Comment author: Emanuele_Ascani 29 May 2018 04:09:31PM *  0 points [-]

Thank you, I applied your suggestion by modifying the text. I just noticed that Guesstimate gives you the standard deviation. I guess I had to familiarise with the tool.

Comment author: Flodorner 28 May 2018 09:06:17AM 2 points [-]

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.

Comment author: Emanuele_Ascani 29 May 2018 11:13:54AM *  2 points [-]

Here how I would reason about moral weights in this case:

In this case the definition of a "life saved" is pretty different than what normally means. Normally a life saved means 30 to 80 DALYs averted, depending if the intervention is on adults or children. In this case we are talking about potentially thousands of DALYs averted, so a life saved should count more. On the other hand there's also to take into consideration that when saving, for example, children who would have died of malaria, you are also giving them a chance of reaching LEV. It's not a full chance as in the present evaluation, but something probably ranging from 30% to 70%.

Additional consideration: some people may want to consider children more important to save than adults. Introducing age weighting and time discounting could seem reasonable in this case, since even if you save 5000 DALYs you are only saving one person, so you might want to discount DALYs saved later in life. On the other hand there are reasons to disagree with this approach: Saving an old person and guaranteeing him/her to reach LEV means also "saving a library". A vast amount of knowledge and experience, especially future experience would have been otherwise completely destroyed. In fact I am not so sure I would apply time discounting myself for this reason.

Regarding bayesian discounting:

I just read how GiveWell would go about this (https://blog.givewell.org/2011/08/18/why-we-cant-take-expected-value-estimates-literally-even-when-theyre-unbiased/). To account for it I would need a prior distribution (or more than one?). I also have difficulty making the calculation, since Guesstimate doesn't let me calculate the variance of the random variables. I will try with other means... maybe with smaller data sets and proceeding by hand or using online calculators.

I would also like to introduce probability distributions in the whole analysis and turn some arguments made in the explanations of some variables in variables in their own right, and I would like to add some more informations (for example the safety profile and history of metformin and the value of information of the trial) based on feedback I'm receiving. This would mean rewriting many sections though, and this will require time.

For now I put an "Edit" at the beginning in order to warn readers not to take the numbers reached too seriously, but I invited them to delve in some more broadly applicable ideas I presented in the analysis that could be useful for evaluating many interventions in the cause area of aging.

Comment author: Flodorner 28 May 2018 09:06:17AM 2 points [-]

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.

Comment author: Emanuele_Ascani 28 May 2018 10:03:05PM *  1 point [-]

Thank you for the feedback!

I'm still learning and comments really help me to be more accurate and they steepen my learning curve. I set up a Guesstimate model (https://www.getguesstimate.com/models/10848). I didn't know about this tool, it is really helpful!

Tomorrow I will improve the guesstimate and get back to you with another comment regarding the bayesian discounting you proposed and the moral weights. I also might make other changes to the evaluation together with the ones you suggested, especially considering that Guesstimate lets me toy with probability distributions.

Comment author: Flodorner 28 May 2018 02:35:33PM 2 points [-]

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.

Comment author: Emanuele_Ascani 28 May 2018 08:41:48PM 0 points [-]

Yes, maybe I exaggerated saying "almost always" or at least I have been too vague. If you haven't any idea of specific interventions to evaluate, then a good way to go is to do superficial high level analyses first and then proceed with lower level ones. Sometimes the contrary could happen though, when a particular promising intervention is found without first investigating its cause area.

Comment author: Emanuele_Ascani 28 May 2018 07:49:55AM 1 point [-]

I want to add something: It probably has been discussed before, but it occurs to me that when thinking about prioritisation in general it's almost always better to think at the lowest level possible. That's because the impact per dollar is only evaluable for specific interventions, and because causes that at first don't appear particularly cost effective can hide particular interventions that are. And those particular interventions could be in principle even more cost effective than other interventions in causes that do appear cost effective overall. I think high-level cause prioritisation is mostly good for gaining a first superficial understanding of the promise of a particular class of altruistic interventions.

Comment author: Emanuele_Ascani 27 May 2018 08:54:57AM *  0 points [-]

If I understood the problem well enough, a possible solution could be setting up a database of donations that is shared between many EA charities, in which donors are obviously anonymous. In this way donors can't be counted twice. In reality the database wouldn't even need to keep track of single donors but only of dollars donated, since we want to estimate if the dollars devoted to advocacy and movement building are less than new donations. Do you think this is viable? Does this offer a solution or at least improve the situation?

8

Expected cost per life saved of the TAME trial

Edit:  Upon suggestion (see comments) I set up a Guesstimate model . There, you can find wider confidence intervals and probability distributions for my final estimates. You may want to combine my estimates with your own priors on general intervention's effectiveness and thereby potentially correct for the high levels of uncertainty in my model.... Read More
Comment author: oge 14 May 2018 10:03:57AM 1 point [-]

I estimate it'll cost at least $1,000/yr to preserve a brain. That's about the cost of maintaining a family at global poverty levels.

I should have posted such calculations first before posting the excerpts. Thanks for your comments.

Comment author: Emanuele_Ascani 14 May 2018 04:05:02PM 1 point [-]

Interesting! How did you arrive at the $1,000/yr figure?

Comment author: Denkenberger 10 May 2018 04:17:32PM *  1 point [-]

The main reason why I am glad I waited 12 years before donating in a big way is that I switched from being global poverty focused to animal welfare and then to long term future. So now I believe what I am donating to is many orders of magnitude more cost-effective than what I would have donated to 15 years ago.

Comment author: Emanuele_Ascani 13 May 2018 10:31:26AM 0 points [-]

Good point. Have you also saved/invested in order to grow the capital you devoted to donations? I plan to do just that, coupled with strategies to avoid value drift.

Comment author: Emanuele_Ascani 13 May 2018 10:26:39AM *  2 points [-]

One thing I find really helpful to remain consistent in my values is introspection followed by writing the results down in a note, both a physical one and in a text file in my pc. I observed that this strategy really works for me, both for figuring out who I am and for making my actions consistent with it for however long periods of time. I still have 70% of the notes I wrote 5 years ago, and 100% of the most important ones that are the core of all my values.

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