Thanks for your comment! I would suspect that these differences are largely being driven by the samples being significantly different. Here is the closest apples-to-apples comparison I could find related to sampling differences (please do correct me if you think there is a better one):
From your sample:
From our sample:
In words, I think your sample is significantly broader than ours: we were looking specifically for people actively involved (we defined as >5h/week) in a specific EA cause area, which would probably correspond to the non-profit buckets in your survey (but explicitly not, for example, ‘still deciding what to pursue’, ‘for profit (earning to give)’, etc., which seemingly accounts for many hundreds of datapoints in your sample).
In other words, I think our results do not support the claim that
[it] isn’t that EAs as a whole are lukewarm about longtermism: it’s that highly engaged EAs prioritise longtermist causes and less highly engaged more strongly prioritise neartermist causes.
given that our sample is almost entirely composed of highly engaged EAs.
Additional sanity checks on our cause area result are that the community’s predictions of the community’s views do more closely mirror your 2020 finding (ie, people indeed expected something more like your 2020 result)—but that the community’s ground truth views are clearly significantly misaligned with these predictions.
Note that we are also measuring meaningfully different things related to cause area prioritization between the 2020 analysis and this one: we simply asked our sample how promising they found each cause area, while you seemed to ask about resourced/funded each cause area should be, which may invite more zero-sum considerations than our questions and may in turn change the nature of the result (ie, respondents could have validly responded ‘very promising’ to all of the cause areas we listed; they presumably could not have similarly responded ‘(near) top priority’ to all of the cause areas you listed).
Finally, it is worth clarifying that our characterization of our sample of EAs seemingly having lukewarm views about longtermism is motivated mainly by these two results:
These results straightforwardly demonstrate that the EAs we sampled clearly predict that the community would have positive views of ‘longtermism x EA’ (what we also would have expected), but the group is actually far more evenly distributed with a slight negative skew on these questions (note the highly statistically significant differences between each prediction vs. ground truth distribution; p≈0 for both).
Finally, it’s worth noting that we find some of our own results quite surprising as well—this is precisely why we are excited to share this work with the community to invite further conversation, follow-up analysis, etc. (which you have done in part here, so thanks for that!).
Thanks for your comment! Agree that there are additional relevant axes to consider than just those we present here. We actually did probe geography to some extent in the survey, though we don’t meaningfully include this in the write-up. Here’s one interesting statistically significant difference between alignment researchers who live in urban or semi-urban environments (blue) vs. those who live everywhere else (suburban, …, remote; red):
Agree that this only scratches the surface of these sorts of questions and that there are other important sources of intellectual/psychological diversity that we are not probing for here.