I am the Principal Research Director at Rethink Priorities. I lead our Surveys and Data Analysis department and our Worldview Investigation Team.
The Worldview Investigation Team previously completed the Moral Weight Project and CURVE Sequence / Cross-Cause Model. We're currently working on tools to help EAs decide how they should allocate resources within portfolios of different causes, and to how to use a moral parliament approach to allocate resources given metanormative uncertainty.
The Surveys and Data Analysis Team primarily works on private commissions for core EA movement and longtermist orgs, where we provide:
Formerly, I also managed our Wild Animal Welfare department and I've previously worked for Charity Science, and been a trustee at Charity Entrepreneurship and EA London.
My academic interests are in moral psychology and methodology at the intersection of psychology and philosophy.
Survey methodology and data analysis.
Some things you might want to do if you are making a weighted factor model
Weighted factor models are commonly used within EA (e.g. by Charity Entrepreneurship/AIM and 80,000 Hours). Even the formalised Scale, Solvability, Neglectedness framework can, itself, be considered a form of weighted factor model.
However, despite their wide use, weighted factor models often neglect to use important methodological techniques which could test and improve their robustness, which may threaten their validity and usefulness. RP's Surveys and Data Analysis team previously consulted for a project who were using a WFM, and helped them understand certain things that were confusing them about the behaviour of their model using these techniques, but we've never had time to write up a detailed post about these methods. Such a post would discuss such topics as:
How to interpret the EA Survey and Open Phil EA/LT Survey.
I think these surveys are complementary and each have different strengths and weaknesses relevant for different purposes.[1] However, I think what the strengths and weaknesses are and how to interpret the surveys in light of them is not immediately obvious. And I know that in at least some cases, decision-makers have had straightforwardly mistaken factual beliefs about the surveys which has mislead them about how to interpret them. This is a problem if people mistakenly rely on the results of only one of the surveys, or assign the wrong weights to each survey, when answering different questions.
A post about this would outline the key strengths and weaknesses of the different surveys for different purposes, touching on questions such as:
Reassuringly, they also seem to generate very similar results, when we directly compare them, adjusting for differences in composition, i.e. only looking at highly engaged longtermists within the EA Survey.
Yeh, I definitely agree that asking multiple questions per object of interest to assess reliability would be good. But also agree that this would lengthen a survey that people already thought was too long (which would likely reduce response quality in itself). So I think this would only be possible if people wanted us to prioritise gathering more data about a smaller number of questions.
Fwiw, for the value of hires questions, we have at least seen these questions posed in multiple different ways over the years (e.g. here) and continually produce very high valuations. My guess is that, if those high valuations are misleading, this is driven more by factors like social desirability than difficulty/conceptual confusion. There are some other questions which have been asked in different ways across years (we made a few changes to the wording this year to improve clarity, but aimed to keep the same where possible), but I've not formally assessed how those results differ.
Thanks Vasco!
This bullet plus the other I quoted above suggest typical junior and senior hires have lifetimes of 40.2 (= 2.04*10^6/(50.7*10^3)) and 16.1 roles (= 7.31*10^6/(455*10^3)), which are unreasonably long. For 3 working-years per junior hire, and 10 working-years per senior hire, they would correspond to working at junior level for 121 years (= 40.2*3), and at senior level for 161 years (= 16.1*10).
We took a different approach to this here, where we looked at the ratio between the value people assigned to a role being filled at all and the value of a person joining the community, rather than the value of the first vs second most preferred hire.
If we look at those numbers, we only get a ratio of ~5 (for both junior and senior hires), i.e. however valuable people think a role being filled is, they think the value of getting a 'hire-level' person to the community is approximately 5x this.
This seems more in line with the number of additional roles that we might imagine a typical hire goes onto after being hired for their first role. That said, people might also have been imagining (i) that people's value produced increases (perhaps dramatically) after their first role, (ii) that people create value for the community outside the roles they're hired to.
Thanks for the comment Jessica! This makes sense. I have a few thoughts about this:
Hey Manuel,
I think the public posts should start coming out pretty soon (within the next couple of weeks).
That said I would strongly encourage movement builders and other decision-makers to reach out to us directly and request particular results when they are relevant to your work. We can often produce and share custom analyses within a day (much faster than a polished public post).
Many people believe that AI will be transformative, but choose not to work on it due to factors such a (perceived) lack of personal fit or opportunity, personal circumstances, or other practical considerations.
There may be various other reasons why people choose to work on other areas, despite believing transformative AI is very likely, e.g. decision-theoretic or normative/meta-normative uncertainty.
I think the possibility that outreach to younger age groups[1] might be net negative is relatively neglected. That said, the two possible reasons suggested here didn't strike me as particularly conclusive.
The main reasons why I'm somewhat wary of outreach to younger ages (though there are certainly many considerations on both sides):
These questions seem very uncertain, but also empirically tractable, so it's a shame that more hasn't been done to try to address them. For example, it seems relatively straightforward to compare the success rates of outreach targeting different ages.
We previously did a little work to look at the relationship between the age when people first got involved in EA and their level of engagement. Prima facie, younger age of involvement seemed associated with higher engagement, though there's a relative dearth of people who joined EA at younger ages, making the estimates uncertain (when comparing <20s to early 20s, for example), and we'd need to spend more time on it to disentangle other possible confounds.
Or it might be that 'life stages' are the relevant factor rather than age per se, i.e. a younger person who's already an undergrad might have similar outcomes when exposed to EA as a typical-age undergrad, whereas reaching out to people while in high school (regardless of age) might be associated with negative outcomes.
I would like someone to write a post about almost every topic asked about in the Meta Coordination Forum Survey, e.g.
I'm primarily thinking about core EA decision-makers writing up their reasoning, but I think it would be valuable for general community members to do this.
Prima facie, it's surprising that more isn't written publicly about core EA strategic questions.