Thanks for your comment Richard, I think the discussion is better for it. I agree with your clarification that there are key differences that distinguish EA from more traditional attitudes and that defending cause incommensurability and personal taste are two relevant dimensions.

Like you, it does seem to us that in the early days of EA, many people doing prioritisation of GHD interventions went beyond traditional intervention clusters (e.g. education) and did some cross-cause prioritisation (identifying the best interventions simpliciter). 

That said, the times feel different now and we think that, increasingly, people are doing within-cause prioritisation by only trying to identify the best interventions within a given area without it being clearly ‘done in service of the ultimate goal of “cross-cause prioritization”’ (e.g. because they are working for an institution or project with funds dedicated exclusively to be allocated within a certain cause).

Thank you Jack, yes it's absolutely about acting on it too, research is just step 1.

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I agree with you Oscar, and we've highlighted this in the summary table where I borrowed your 'contrasting project preferences' terminology. Still, I think it could still be worth drawing the conceptual distinctions because it might help identify places where bargains can occur.

I liked your example too! We tried to add a few (GCR-focused agent believes AI advances are imminent, while a GHD agent is skeptical; AI safety view borrows resources from a Global Health to fund urgent AI research; meat-eater; gun rights and another supporting gun control both fund a neutral charity like Oxfam...) but we could have done better in highlighting them. I've also added these to the table.

I found your last mathematical note a bit confusing because I originally read A,B,C as projects they might each support. But if it's outcomes (i.e. pairs of projects they would each support), then I think I'm with you!

Just to flag that Derek posted on this very recently. It's directly connected to both the present post and Michael's.

That's fair. The main thought that came to mind, which might not be useful, is developing the patience (eagerness to get to conclusions is often incompatible with the work required) and choosing your battles early. As you say, it can be hard and time-consuming. So people in the community asking narrower questions and focusing on one or two is probably the way to go. 

Thanks for looking through our work and for your comment, Deborah. We recognise that different parts of our models are often interrelated in practice. In particular, we’re concerned about the problem of correlations between interventions too, as we flag here. This is an important area for further work. That being said, it isn’t clear that the cases you have in mind are problems for our tools. If you think, for instance, that environmental interventions are particularly good because they have additional (quantifiable or non-quantifiable) benefits, you can update the tool inputs (including the cause or project name) to reflect that and increase the estimated impact of that particular cause area. We certainly don't mean to imply that climate change is an unimportant issue.

 I think another common pitfall is not working through things from first principles. I appreciate that it’s challenging and that any model is unrealistic. Still, BOTECs, pre-established boundaries between cause-areas/worldviews and our first instincts more broadly are likely to (and often do) lead us astray. Separately, I’m glad EA is so self-aware and worried about healthier epistemics, but I think we could do more to guard against echo-chamber thinking. 

I was personally struck by how sensitive portfolios are to even modest levels of risk aversion. I don’t know what “correct” level of risk aversion is, or what the optimal decision procedure is in practice (even though most of my theoretical sympathies lie with expected value maximisation). Even so, seeing how introducing bits of risk aversion, even when using parameters relatively generous towards x-risk, still points towards spending most resources on animals (and sometimes global health) has led me to believe that type of work is robustly better than I used to think. There are many uncertainties and I don't think EA should be reduced to any one of its cause-areas but, especially given this update, I would be sad to see the animal space shrink in relative size any more than it has.

Thanks for the question Carter! Would you mind saying a bit more about the kind of empirical work you have in mind? Are you thinking about empirical research into the inputs to the tools? Or are you thinking about using the tools to conduct research on people’s views about cause prioritization? Do you have any concrete empirical projects you’d like to see WIT do?