My attempt at a TLDR for this: Bayesian assign a probability to each belief in order to represent uncertainty but this is insufficient because there are multiple kinds of uncertainty: vagueness, approximation, context-dependence, and sense vs nonsense, knightian. And sometimes we humans make logical errors when we try to do bayesian inference.
I wouldn't be surprised if this is already a thing so please let me know if it is. I have tried searching. I'd like a tool like this one for analyzing P(doom): https://projects.fivethirtyeight.com/2024-election-forecast/. Other precedents are this model of transformative AI timeline on less wrong and the use of bayesian networks for forecasting climate change.
The problem of estimating P(doom) is very complicated but it is not impossible. It is not metaphysical like estimating P(we live in a simulation) or P(we are a boltzman brain). P(doom) is a tangible thing based on human technology. Just very very complicated. It requires summing over many different possibilities. So maybe can we do better by factoring... (read 220 more words →)
Just my opinion: the concerns are valid but exaggerated.
Perhaps exaggeration is justified in order to get people's attention since most americans are still treating this as a normal election.