Is your uncertainty resolvable?

Minor clarification/nitpick: seems like it's not about whether the uncertainty is possible to resolve. Rather, it's about whether we currently see an easy path to resolve it, or whether we expect it to be resolved in the near future. If we don't have an easy path to resolving some uncertainty, then searching for such a path can be worthwhile - talking about whether the uncertainty is "possible" to resolve de-emphasizes that aspect.

I think I must be the odd one out here in terms of comfort using probabilities close to 1 and 0. Because 90% and 99% are not "near certainty" to me.

How sure are you that the English guy who you've been told helped invent calculus and did stuff with gravity and optics was called "Isaac Newton"? We're talking about probabilities like 99.99999% here. (Conditioning on no dumb gotchas from human communication, e.g. me using a unicode character from a different language and claiming it's no longer the same, which has suddenly become much more salient to you and me both. An "internal" probability, if you will.)

Maybe it would help to think of this as about 20 bits of information past 50%? Every bit of information you can specify about something means you are assigning a more extreme probability distribution about that thing. The probability of the answer being "Isaac Newton" has a very tiny prior for any given question, and only rises to 50% after lots of bits of information. And if you could get to 50%, it's not strange that you could have quite a few more bits left over, before eventually running into the limits set by the reliability of your own brain.

So when you say some plans require near certainty, I'm not sure if you mean what I mean but chose smaller probabilities, or if you mean some somewhat different point about social norms about when numbers are big/small enough that we are allowed to stop/start worrying about them. Or maybe you mean a third thing about legibility and communicability that is correlated with probability but not identical?

This feels highly related to the idea of Transparent, Opaque, and Knightian Uncertainty [LW · GW]. You can have object level certainty/uncertainty, and then you can have meta uncertainty about WHICH type of environment you're in, which changes what strategies you should be using to mitigate the risk associated with the uncertainty.

I'm confused. How does conservation of expected evidence come in here? When you say "90% sure" do you mean "assign 90% probability of truth to the hypothesis"? You can't expect that to change. Or do you mean "90% sure that my probability estimate of X% is correct"?

Your probability estimate ALREADY INCLUDES the probability distributions of future experiments (and of future research).

First link is meant to go to https://www.lesswrong.com/posts/SDELNzyboMpZpDwSg/fb-discord-style-reacts [LW · GW]

This feels like it's a specific instance of a more general thing around how fast I can converge on a guess about a distribution I'm sampling from. Imagine the scatterplot with data points added one at a time. There are both negative guesses (this point rules out these distributions) and positive guesses (it sorta looks like this will converge to a bimodal distribution). Depending on payoff structure and priors I might want to lean more heavily towards faster/sparser guesses. I'm not up on current ML but this has to be common enough to be a named thing.