Hi Marcus thanks very helpful to get some numbers and clarification on this. And well done to you and Rethink for driving forward such important research.

(I meant to post a similar question asking for clarification on the rethink post too but my perfectionism ran away with me and I never quite found the wording and then ran out of drafting time, but great to see your reply here)

Hi Emily, Sorry this is a bit off topic but super useful for my end of year donations.

I noticed that you said that OpenPhil has supported "Rethink Priorities ... research related to moral weights". But in his post here Peter says that the moral weights work "have historically not had institutional support".

Do you have a rough very quick sense of how much Rethink Priorities moral weights work was funded by OpenPhil?

Thank you so much 

Hi, Debugging worked. It was a Chrome extension I had installed to hide cookie messages what was killing it. Thank you so much!!

Hi. I started drafting a reply but had to stop and now a week later I cannot find where I was drafting it. I would love to be able to see all the places where I have draft comments/replies autosaved. Thank you! 

Alternatively, one might adjust ambiguous probability assignments to reduce their variance. For example, in a Bayesian framework, the posterior expectation of some value is a function of both the prior expectation and evidence that one has for the true value. When the evidence is scant, the estimated value will revert to the prior.^[30] Therefore, Bayesian posterior probability assignments tend to have less variance than the original ambiguous estimate, assigning lower probabilities to extreme payoffs.^[31] 

Would you say this is being ambiguity averse in terms of aiming for a lower EV option, or would you say that this is just capturing the EV of the situation more precisely and still aiming for the highest EV option?
 

Eg. maybe in your story by picking option 2 Pat is not being truly ambiguity averse (aiming for a lower EV option) but has a prior that someone is more likely to be traying to scam her out of $5 than give her free money and is thus still trying to maximise EV.

Sorry to be annoying but after reading the post "Animals of Uncertain Sentience" I am still very confused about the scope of this work

My understanding is that any practical how to make decisions is out of the scope of that post. You are only looking at the question of whether the tools used should in theory be aiming to maximise true EV or not (even in the cases where those tools do not involve calculating EV).

If I am wrong about the above do let me know!

Basically I find phrases like"EV maximization decision procedure" and "using EV maximisation to make these decisions" etc confusing. EV maximisation is a goal that might or might not be best served with a EV calculation based decision procedure, or by a decision procedure that does not involve any EV calculations. I am sorry I know this is persnickety but thought I would flag the things I a finding confusing. I do think being a bit more concise about this would help readers understand the posts.

Thank you for the work you are doing on this.

"The team is only a few months old and the problems you're raising are hard"

Yes a full and thorough understanding of this topic and rigorous application to cause prioritisation research would be hard.

But for what it's worth I would expect there are easy some quick wins in this area too. Lots of work has been done outside the EA community just not applied to cause prioritisation decision making, at least that i have noticed so far...

Amazing. Super helpful to hear. Useful to understand what you are currently covering and what you are not covering and what the limits are. I very much hope that you get the funding for more and more research

I am very very excited to see this research it's the kind of thing that I think EAs should be doing a lot more of and it seems shocking that it takes us more than a decade to get round to such basic fundamental questions on cause prioritisation. Thank you so much for doing this.

I do however have one question and one potential concern.

Question: My understanding from reading the research agenda and plan here is that you are NOT looking into the topic of how best to make decisions under uncertainty (Knightian uncertainty, cluelessness, etc). It looks like you are focusing on resolving the question of WHAT exactly decision making should aim for (e.g. maximise true EV or not) but not the topic of HOW best to make those decisions (e.g. what decision tools to use, to what extent to rely on calculated EV as a tool versus other tools, when practically to satisfice or maximize, etc). It looks like you might touch on the HOW within the specific sub-question of uncertainty over time but not otherwise. Is this a correct reading of your research aims and agenda?

If so, this does puts limits on the conclusions you could draw.

I think that the majority (but by no means all) the people that I know in EA that have a carefully considered view that pushes them to focus on say global health above x-risk issues do so, not because they disagree on the WHAT because they disagree on the HOW. They are not avoiding maximising EV, or non-conseqentalist, or risk averse, they just put less weight on simple EV calculations as a decision tool and the set of tools that they do use to directs them away from x-risk work.

Such a conclusions or models built on just the WHAT question would be of limited use – not just because you need HOW to decide and WHAT too aim* for to make a decision – but specifically it is not hitting what, in my experience, is the primary (although not only) crux of people's actual disagreement here.

I'd be curious to hear if you agree with this analysis of the limits of the, still very very important, work you are doing.

. * As an aside I actually think in some cases it's possible to make do with the HOW but not the WHAT but not the other way round. For example you might believe that it has been shown empirically that in deep uncertainty situations a strategy of robust satisficing rather than maximizing allows players to win more war game scenarios or to feel more satisfied with their decision at a later point in time, and therefore believe that adopting such a strategy in situations deep uncertainty is optimal. You could believe this without taking a stance on or knowing whether or not such a strategy maximizes true EV, or is risk averse, etc.