All of Rockenots's Comments + Replies

This is a good point. I don't think it should make that much of a difference given how young LessWrong is on average, but it can't hurt to try. 

My two problem are 1) finding SAT statistics for nationally representative samples, and not just seniors that take the SAT (the latter are obviously selected) is difficult, and 2) I’d need more detailed data than just the SAT averages—I'd have to adjust each person’s SAT z-score based on the year they took the test.

By this logic, if rationalists are selected based on IQ and not height, and the average rationalist height is +1.85 SD, then we’d have to assume that rationalists’ IQ is +9.25 SD (assuming an IQ-height correlation of 0.2), which is, of course, impossible.

For another example of why this logic doesn’t work, consider this: if you have a variable that is uncorrelated with IQ (r = 0), and rationalists are just slightly above average for that variable, then we'd be forced to conclude that rationalists are infinitely smart (or, if they're below average, infinitel... (read more)

Your argument assumes a uniform prior, but a Gaussian prior is more realistic in this case. In practice, IQ scores are distributed normally, so it's more likely that someone with a high SAT score comes from a more common IQ range than from a very high outlier. For example, say the median rationalist has an SAT score of +2 SD (chosen for ease of computation), and the SAT-IQ correlation is 0.80. The IQ most likely to produce an SAT of +2 SD is 137.5 (+2.5 SD). However, IQs of 137.5 are rare (99.4%-ile). While lower IQs are less likely to achieve such a high ... (read more)

Eric Neyman is right. They are both valid! 

In general, if we have two vectors $X$ and $Y$ which are jointly normally distributed, we can write the joint mean $\mu$ and the joint covariance matrix $\Sigma$ as

The conditional distribution for $Y$ given $X$ is given by $Y|X\sim N\left({\mu }_{Y|X},K_{Y|X}\right)$,
defined by conditional mean 

${\mu }_{Y|X}={\mu }_Y+K_{YX}{K_{XX}}^{-1}(X-{\mu }_X)$

and conditional variance 

$K_{Y|X}=K_{YY}{K_{XX}}^{-1}{K}_{XY}$

Our conditional distribution for the IQ of the median rationalist, given their SAT score is $N(0+($... (read more)

You're missing the point. While I agree that we don't want to select too hard for personality traits, the bigger problem is that we're not able to robustly select for personality traits the way we're able to select for IQ. If you try to select for Extraversion, you may end up selecting for people particularly prone to social desirability bias. This isn't a Goodhart thing; the way our personality tests are currently constructed means that all the personality traits have fairly large correlations with social desirability, which is not what you want to select... (read more)

A key consideration when selecting for latent mental traits is whether a common pathway model holds for the latent variable under selection. In an ideal common pathway model, all covariance between indicators is mediated by a single underlying construct.
When this model fails, selecting for one trait can lead to unintended consequences. For instance, attempting to select for Openness might not reliably increase open-mindedness or creativity. Instead, such selection could inadvertently target specific parts of whatever went into the measurement, like liberal... (read more)

It turns out that in adults, BMI is negatively correlated with height. So, if human heights have been increasing over time, we'd actually expect BMI to decrease over time. 

Since molecular squiggle maximizers and paperclip maximizers both result in a universe-shard that's a boring wasteland, despite the fact that they maximize different things, what's the practical difference between talking about molecular squiggle maximizers instead of paperclip maximizers?