I am currently a nuclear engineer with a focus in nuclear plant safety and probabilistic risk assessment. I am also an aspiring EA, interested in X-risk mitigation and the intersection of science and policy.
I don't have time for a long reply, but I think the perspective in this post would be good to keep in mind: https://forum.effectivealtruism.org/posts/FpjQMYQmS3rWewZ83/effective-altruism-is-a-question-not-an-ideology
By putting an answer (reduce AI risk) ahead of the question (how can we do the most good?) we would be selling ourselves short.
Some people, maybe a lot of people, should probably choose to focus fully on AI safety and stop worrying about cause prioritization. But nobody should feel like they're being pushed into that or like other causes are worthless. EA should be a big tent. I don't agree that it's easier to rally people around a narrow cause; on the contrary, single minded focus on AI would drive away all but a small fraction of potential supporters, and have an evaporative cooling effect on the current community too.
18 years is a marathon, not a sprint.
I tend to think diversification in EA is important even if we think there's a high chance of AGI by 2040. Working on other issues gives us better engagement with policy makers and the public, improves the credibility of the movement, and provides more opportunities to get feedback on what does or doesn't work for maximizing impact. Becoming insular or obsessive about AI would be alienating to many potential allies and make it harder to support good epistemic norms. And there are other causes where we can have a positive effect without directly competing for resources, because not all participants and funders are willing or able to work on AI.
Thanks for pointing this out. Research particularly seems to show that people who commute by bike are the most satisfied, followed by walking, then trains, then buses and carpools (which are almost as bad as driving):
Personally, I normally commute by a combo of bike and train, and I find that on days when I have to drive instead it does add stress, especially if traffic is bad.
The example used here is a stochastic process, which is a case where resilience of a subjective probability can be easily described with a probability distribution and Bayesian updates on observations. But the most important applications of the idea are one-off events with mainly epistemic uncertainty. Is there a good example we could include for that? Maybe a description of how you might express/quantify the resilience of a forecast for a past event whose outcome is not known yet?