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I used to be a creationist, and I have put some thought into this stumbling block. I came to the conclusion that it isn't worth leaving out analogies to evolution, because the style of argument that would work best for most creationists is completely different to begin with. Creationism is correlated with religious conservatism, and most religious conservatives outright deny that human extinction is a possibility.

The Compendium isn't meant for that audience, because it explicitly presents a worldview, and religious conservatives tend to strongly resist shifts to their worldviews or the adoption of new worldviews (moreso than others already do). I think it is best left to other orgs to make arguments about AI Risk that are specifically friendly to religious conservatism. (This isn't entirely hypothetical. PauseAI US has recently begun to make inroads with religious organizations.)

Yeah, if you were to use the neighbor method, the correct way to do so would involve post-processing, like you said. My guess, though, is that you would get essentially no value from it even if you did that, and that the information you get from normal polls would prrtty much screen off any information you'd get from the neighbor method.

The mistake you are making is assuming that "ZFC is consistent" = Consistent(ZFC) where the ladder is the Godel encoding for "ZFC is consistent" specified within the language of ZFC.

If your logic were valid, it would just as well break the entirety of the second incompleteness theorem. That is, you would say "well of course ZFC can prove Consistent(ZFC) if it is consistent, for either ZFC is consistent, and we're done, or ZFC is not consistent, but that is a contradiction since 'ZFC is consistent' => Consistent(ZFC)".

The fact is that ZFC itself cannot recognize that Consistent(ZFC) is equivalent to "ZFC is consistent".

@Morpheus [LW · GW] you too seem confused by this, so tagging you as well.

I think this just comes down to me having a narrower definition of a city.

Regarding the recent memes about the end of LLM scaling: David and I have been planning on this as our median world since about six months ago. The data wall has been a known issue for a while now, updates from the major labs since GPT-4 already showed relatively unimpressive qualitative improvements by our judgement, and attempts to read the tea leaves of Sam Altman's public statements pointed in the same direction too. I've also talked to others (who were not LLM capability skeptics in general) who had independently noticed the same thing and come to similar conclusions.

Our guess at that time was that LLM scaling was already hitting a wall, and this would most likely start to be obvious to the rest of the world around roughly December of 2024, when the expected GPT-5 either fell short of expectations or wasn't released at all. Then, our median guess was that a lot of the hype would collapse, and a lot of the investment with it. That said, since somewhere between 25%-50% of progress has been algorithmic all along, it wouldn't be that much of a slowdown to capabilities progress, even if the memetic environment made it seem pretty salient. In the happiest case a lot of researchers would move on to other things, but that's an optimistic take, not a median world.

(To be clear, I don't think you should be giving us much prediction-credit for that, since we didn't talk about it publicly. I'm posting mostly because I've seen a decent number of people for whom the death of scaling seems to be a complete surprise and they're not sure whether to believe it. For those people: it's not a complete surprise, this has been quietly broadcast for a while now.)

People generally assume those around them agree with them (even when they don't see loud support of their position - see "silent majority"). So when you ask what their neighbors think, they will guess their neighbors have the same views as themselves, and will report their own beliefs with plausible deniability.

This distinction reminds me of Evading Black-box Classifiers Without Breaking Eggs, in the black box adversarial examples setting. 

FWIW, the there are some people around the AI safety space, especially people who work on safety cases, who have that experience. E.g. UK AISI works with some people who are experienced safety analysts from other industries.

(discussed on the LessWrong discord server)

There seems to be an implicit fundamental difference in many people's minds between an algorithm running a set of heuristics to maximize utility (a heuristic system?) and a particular decision theory (e.g. FDT). I think the better way to think about it is that decision theories categorize heuristic systems, usually classifying them by how they handle edge cases.
Let's suppose we have a non-embedded agent A in a computable environment, something like a very sophisticated video game, and A has to continually choose between a bunch of inputs. A is capable of very powerful thought: it can do hypercomputation, RNG if needed, think as long as it needs between its choices, etc. In particular, A is able to do Solomonoff Induction. Let's also assume A is maximizing a utility function U, which is a computable function of the environment.

What happens if A find itself making a Newcomblike decision? Perhaps there is another agent in this environment that has a very good track record of predicting whether other agents in the environment will one-box or two-box, and A finds itself in the usual Newcomb scenario (million utility or a million+thousand utility or no utility) with their decision predicted by this agent. A can one-box by choosing one input and two-box by choosing another input.  Should A one-box? 
No. The agent in the environment would be unable to simulate A's decision, and moreover, A's decision is completely and utterly irrelevant to what's inside the boxes. If A randomly goes off-track and flips its decision at this point, nothing happens. Nothing could have happened, this other agent has no way to know or use this fact. Instead, A sums over P(x|input)U(x) for all states x of the computable environment, and chooses whichever input yields the maximum sum, which is probably two-boxing. If A one-boxes, it is due to not having enough information about the setup to determine that two-boxing is better.

You cannot use this logic when playing against Omega or a skilled psychologist. In these cases, your computation is actually accessible by the other agent, so you can get higher utility by one-boxing. Your decision theory is important because your thinking is not as powerful as A's! All of this points to looking at decision theories as classifying different heuristic systems.

I think this is post-worthy, but I want to (a) verify that my logic is correct (b) improve my wording (I am unsure if I am using a lot of terminology correctly here, but I am fairly confident that my idea can be understood.)

My impression is that e.g. the Catholic church has a pretty deeply thought out moral philosophy that has persisted across generations. That doesn't mean that every individual Catholic understands and executes it properly.