Interesting technique, I'll need to remember that.

Reminds me of the several times I've thought I've disagreed with Eliezer on various issues here, spent a while understanding my objections so I could detail it in a reply, and ended up convincing myself of his orignal position by the time I finished writing.

Perhaps I phrased my template too formally. Though as I search for examples, I notice that different uses of the word "guy" would require various replacements ("person," "someone," or "the one") in order to sound natural.

Really, I begin to think it would be simpler to alter our culture so that nobody expects "guy" to imply "male".

Thanks for this important spot - I don't think it is a nitpick at all. I'm switching jobs at the moment, but I'll revise the post (and diagrams) in light of this. It might be a week though, sorry!

Great article overall. Regression to the mean is a key fact of statistics, and far too few people incorporate it into their intuition.

But there's a key misunderstanding in the second-to-last graph (the one with the drawn-in blue and red "outcome" and "factor"). The black line, indicating a correlation of 1, corresponds to nothing in reality. The true correlation is the line from the vertical tangent point at the right (marked) to the vertical tangent point at the left (unmarked). If causality indeed runs from "factor" (height) to "outcome" (skill), that's how much extra skill an extra helping of height will give you. Thus, the diagonal red line should follow this direction, not be parallel to the 45 degree black line. If you draw this line, you'll notice that each point on it has equal vertical distance to the top and bottom of the elliptical "envelope" (which is, of course, not a true envelope for all the probability mass, just an indication that probability density is higher for any point inside than any point outside).

Things are a little more complex if the correlation is due to a mutual cause, "reverse" causation (from "outcome" to "factor"), or if "factor" is imperfectly measured. In that case, the line connecting the vertical tangents may not correspond to anything in reality, though it's still what you should follow to get the "right" (minimum expected squared error) answer.

This may seem to be a nitpick, but to me, this kind of precision is key to getting your intuition right.

Thank you for the clarification; despite this, cardinal utility is difficult because it assumes that we care about different preferences the same amount, or definably different amounts.

Unless there is a commodity that can adequately represent preferences (like money) and a fair redistribution mechanism, we still have problems maximizing overall welfare.

(Edit, Later. This is related to the top level replies by CarlShulman and V-V, but I think it's a more general issue, or at least a more general way of putting the same issues.)

I'm wondering about a different effect: over-quantification and false precision leading to bad choices in optimization as more effort goes into the most efficient utility maximization charities.

If we have metrics, and we optimize for them, anything that our metrics distort or exclude will have an exaggerated exclusion from our conversation. For instance, if we agree that maximizing human health is important, and use evidence that shows that something like fighting disease or hunger in fact has a huge positive effect on human health, we can easily optimize towards significant population growth, then a crash due to later resource constraints or food production volatility, killing billions. (It is immaterial if this describes reality, the phenomenon of myopic optimization still stands.)

Given that we advocate optimizing, are we, as rationalists, likely to fall prey to this sort of behavior when we pick metrics? If we don't understand the system more fully, the answer is probably yes; there will always be unanticipated side-effects in incompletely understood systems, by definition, and the more optimized a system becomes, the less stable it is to shocks.

More diversity of investment to lower priority goals and alternative ideals, meaning less optimization, as currently occurs, seem likely to mitigate these problems.

Many of these issues seem related to arrow's impossibility theorem; if groups have genuinely different values, and we optimize for one set not another, ants get tiny apartments and people starve, or we destroy the world economy because we discount too much, etc.

To clarify, I think LessWrong thinks most issues are simple, because we know little about them; we want to just fix it. As an example, poverty isn't solved for good reasons; it's hard to balance incentives and growth, and deal with heterogeneity, there exist absolute limits on current wealth and the ability to move it around, and the competing priorities of nations and individuals. It's not unsolved because people are too stupid to give money to feed the poor charities. We underestimate the rest of of the world because we're really good at one thing, and think everyone is stupid for not being good at it - and even if we're right, we're not good at (understanding) many other things, and some of those things matter for fixing these problems.

To my mind, the worst thing about the EA movement are its delusions of grandeur. Both individually and collectively, the EA people I have met display a staggering and quite sickening sense of their own self-importance. They think they are going to change the world, and yet they have almost nothing to show for their efforts except self-congratulatory rhetoric. It would be funny if it wasn't so revolting.

Playing the devil's advocate is when Alice is arguing for some position, and Bob is arguing against it, even though he does not actually disagree with Alice (perhaps because he wants to help Alice strengthen her arguments, clarify her views, etc.).

Hypothetical apostasy is when Alice plays her own devil's advocate, in essence, with no Bob involved.

Your probability theory here is flawed. The question is not about P(A&B), the probability that both are true, but about P(A|B), the probability that A is true given that B is true. If A is "has cancer" and B is "cancer test is positive", then we calculate P(A|B) as P(B|A)P(A)/P(B); that is, if there's a 1/1000 chance of cancer and and the test is right 99/100, then P(A|B) is .99.001/(.001.99+.999.01) which is about 1 in 10.