Comparing the average quality of participants might be misleading if impact on the field is dominated by the highest quality participants (and it very plausibly is).

A model that seems quite plausible to me is that early MATS participants, who were selected more for engagement with a then-niche field, turned out a bit worse on average than current MATS participants, who are selected for coding skills, but that the early MATS participants had higher variance, and so early MATS cohorts produced more people at the top end and had more overall impact.

(This is like 80% armchair reasoning from selection criteria and 20% thinking about what I've observed of different MATS cohorts.)

Here's some of how I think about it: plans are made of smaller plans, inside their steps to achieve 'em. And smaller plans have lesser plans, and so ad infinitum

Every plan has ends to cause, and steps to help it cause 'em.
And every step's a little plan, and that's the way ad nauseum.

Some people likely think

don't build ASI until it can be done safely > build ASI whenever but try to make it safe > never build ASI

Those people might give different prescriptions to the "never build ASI" people, like not endorsing actions that would tank the probability of ASI ever getting built. (Although in practice I think they probably mostly make the same prescriptions at the moment.)

I think "Will there be a crash?" is a much less ambiguous question than "Is there a bubble?"

Yeah, I think “training for transparency” is fine if we can figure out good ways to do it. The problem is more training for other stuff (e.g. lack of certain types of thoughts) pushes against transparency.

I often complain about this type of reasoning too, but perhaps there is a steelman version of it.

For example, suppose the lock on my front door is broken, and I hear a rumour that a neighbour has been sneaking into my house at night. It turns out the rumour is false, but I might reasonably think, "The fact that this is so plausible is a wake-up call. I really need to change that lock!"

Generalising this: a plausible-but-false rumour can fail to provide empirical evidence for something, but still provide 'logical evidence' by alerting you to something that is already plausible in your model but that you hadn't specifically thought about. Ideal Bayesian reasoners don't need to be alerted to what they already find plausible, but humans sometimes do.

But then we have to ask — why two ‘ marks, to make the quotation mark? A quotidian reason: when you only use one, it’s an apostrophe. We already had the mark that goes in “don’t”, in “I’m”, in “Maxwell’s”; so two ‘ were used to distinguish the quote mark from the existing apostrophe.

Incidentally I think in British English people normally do just use single quotes. I checked the first book I could find that was printed in the UK and that’s what it uses:

He'd be a fool to part with his vote for less than the amount of the benefits he gets.

Doesn't seem right. Even assuming the person buying his vote wants to use it to remove his benefits, that one vote is unlikely to be the difference between the vote-buyer's candidate winning and losing. The expected effect of the vote on the benefits is going to be much less than the size of the benefits.

An intuition you might be able to invoke is that the procedure they describe is like greedy sampling from an LLM, which doesn’t get you the most probable completion.

“A Center for Applied Rationality” works as a tagline but not as a name