I don't think this is quite the LW norm. We might distinguish several different meanings of right to life:

1: The moral value I place on other peoples' lives. In this sense "right to life" is just the phrase I use to describe the fact that I don't want people to kill or die, and the details can easily vary from person to person. If LW users value sentience, this is a fact about demographics, not an argument that should be convincing. This is what we usually mean when we say something is "okay."

2: The norms that society is willing to enforce regarding the value of a life. Usually fairly well agreed upon, though with some contention (e.g. fertilized ova). This is the most common use of the word "right" by people who understand that rights aren't ontologically basic. Again, this is a descriptive definition, not a prescriptive one, but you can see how people might decide what to collectively protect based compromises between their own individual values.

3: Something we should protect for game-theoretic reasons. This is the only somewhat prescriptive one, since you can argue that it is a mistake in reasoning to, say, pollute the environment if you're part of a civilization of agents very similar to you. Although this still depends on individual values, it's the similarity of peoples' decisions that does the generalizing, rather than compromise between different people. Values derived in this way can be added to or subtracted from values derived in the other ways. It's unclear how much this applies to the case of abortion - this seems like an interesting argument.

My perspective on anthropics is somewhat different than many, but I think that in a probability theory textbook, anthropics should only be treated as a special case of assigning probabilities to events generated by causal systems. Which requires some familiarity with causal graphs. It might be worth thinking about organizing material like that into a second book, which can have causality in an early chapter.

I would include Savage's theorem, which is really pretty interesting. A bit more theorem-proving in general, really.

Solomonoff induction is a bit complicated, I'm not sure it's worthwhile to cover at a more than cursory level, but it's definitely an important part of a discussion about what properties we want priors to have.

On that note, a subject of some modern interest is how to make good inferences when we have limited computational resources. This both means explicitly using probability distributions that are easy to calculate with (e.g. gaussian, cauchy, uniform) , and also implicitly using easy distributions by neglecting certain details or applying certain approximations.

There's probably some books by Dan Dennett that the LW articles that deal with philosophy of mind drew from, but I've mostly been exposed to Dennett through articles, like Intentional Systems and Eliminate the Middletoad!

On evolution, essential reading is The Selfish Gene.

On heuristics and biases, Thinking Fast and Slow (the first half, at least), and Dan Ariely's books are good reads.

I'm not aware of anything similar to the sequences in terms of intersection of Bayesianism, heuristics and biases, and trying to teach how to think about confusing things. Unfortunately.

(Note: this is not actually true. But Good and Real recapitulates most of the points you'll see in the philosophy-related sequences, with less focus on the basics and more on elaborating philosophical arguments. If this is the content you want to share, it might be a good choice.)

Yes, it's entitled "Good and Real." The shadowy cabal behind LessWrong wrote it under one of their other pseudonyms, "Gary Drescher."

There's a lot of self-selection, and the classes and extracurricular resources are therefore allowed to be geared towards smarter students, and that's nice. You'll also get more opportunities to learn about current research in your chosen field, which improves your grad school chances.

A lot of the value is if you plan to get a job straight out of college, going to a top n school will have a name brand advantage (not without reason).

However, controlling for smartness and research experience, I think that where you did your undergrad doesn't matter all that much for grad school.

Argument thread!

You should probably stay at your big company job because the people who are currently startup founders are self-selected for, on average, different things than you're selecting yourself for by trying to jump on a popular trend, and so their success is only a weak predictor of your success.

Startups often cash out by generating hype and getting bought for ridiculous amounts of money by a big company. But they are very, very often, in more sober analysis, not worth this money. From a societal perspective this is bad because it's not properly aligning incentives with wealth creation, and from a new-entrant perspective this is bad because you likely fail if the bubble pops before you can sell.