Moore & Schatz (2017) made a similar point about different meanings of "overconfidence" in their paper The three faces of overconfidence. The abstract:

Overconfidence has been studied in 3 distinct ways. Overestimation is thinking that you are better than you are. Overplacement is the exaggerated belief that you are better than others. Overprecision is the excessive faith that you know the truth. These 3 forms of overconfidence manifest themselves under different conditions, have different causes, and have widely varying consequences. It is a mistake to treat them as if they were the same or to assume that they have the same psychological origins.

Though I do think that some of your 6 different meanings are different manifestations of the same underlying meaning.

Calling someone "overprecise" is saying that they should increase the entropy of their beliefs. In cases where there is a natural ignorance prior, it is claiming that their probability distribution should be closer to the ignorance prior. This could sometimes mean closer to 50-50 as in your point 1, e.g. the probability that the Yankees will win their next game. This could sometimes mean closer to 1/n as with some cases of your points 2 & 6, e.g. a 1/30 probability that the Yankees will win the next World Series (as they are 1 of 30 teams).

In cases where there isn't a natural ignorance prior, saying that someone should increase the entropy of their beliefs is often interpretable as a claim that they should put less probability on the possibilities that they view as most likely. This could sometimes look like your point 2, e.g. if they think DeSantis has a 20% chance of being US President in 2030, or like your point 6. It could sometimes look like widening their confidence interval for estimating some quantity.

You can go ahead and post.

I did a check and am now more confident in my answer, and I'm not going to try to come up with an entry that uses fewer soldiers.

Just got to this today. I've come up with a candidate solution just to try to survive, but haven't had a chance yet to check & confirm that it'll work, or to try to get clever and reduce the number of soldiers I'm using.

I will probably work on this some more tomorrow.

Is this calculation showing that, with a big causal graph, you'll get lots of very weak causal relationships between distant nodes that should have tiny but nonzero correlations? And realistic sample sizes won't be able to distinguish those relationships from zero.

Andrew Gelman often talks about how the null hypothesis (of a relationship of precisely zero) is usually false (for, e.g., most questions considered in social science research).

A lot of people have this sci-fi image, like something out of Deep Impact, Armageddon, Don't Look Up, or Minus, of a single large asteroid hurtling towards Earth to wreak massive destruction. Or even massive vengeance, as if it was a punishment for our sins.

But realistically, as the field of asteroid collection gradually advances, we're going to be facing many incoming asteroids which will interact with each other in complicated ways, and whose forces will to a large extent balance each other out.

Yet doomers are somehow supremely confident in how the future will go, foretelling catastrophe. And if you poke at their justifications, they won't offer precise physical models of these many-body interactions, just these mythic stories of Earth vs. a monolithic celestial body.

Try here or here or here.

They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your reputation. But I think criticism is good, because if I write a bad blog post, and someone tells me it was bad, I can learn from that, and do better next time.

I read this as saying 'a common view is that being criticized is bad because it hurts your reputation, but as a person with some knowledge of the secret lore of rationality I believe that being criticized is good because you can learn from it.'

And he isn't making a claim about to what extent the existing LW/rationality community shares his view.

Seems like the main difference is that you're "counting up" with status and "counting down" with genetic fitness.

There's partial overlap between people's reproductive interests and their motivations, and you and others have emphasized places where there's a mismatch, but there are also (for example) plenty of people who plan their lives around having & raising kids. 

There's partial overlap between status and people's motivations, and this post emphasizes places where they match up, but there are also (for example) plenty of people who put tons of effort into leveling up their videogame characters, or affiliating-at-a-distance with Taylor Swift or LeBron James, with minimal real-world benefit to themselves.

And it's easier to count up lots of things as status-related if you're using a vague concept of status which can encompass all sorts of status-related behaviors, including (e.g.) both status-seeking and status-affiliation. "Inclusive genetic fitness" is a nice precise concept so it can be clear when individuals fail to aim for it even when acting on adaptations that are directly involved in reproduction & raising offspring.