The Best of LessWrong

#4

There are many things that people are socially punished for revealing, so they hide them, which means we systematically underestimate how common they are. And we tend to assume the most extreme versions of those things are representative, when in reality most cases are much less extreme.

#7

How much do you believe your results?

When you encounter a study, always ask yourself how much you believe their results. In Bayesian terms, this means thinking about the correct amount for the study to update you away from your priors. For a noisy study, the answer may well be “pretty much not at all”

11transhumanist_atom_understander

It's great to have a LessWrong post that states the relationship between expected quality and a noisy measurement of quality: We previously had a popular post on this topic, the tails come apart post, but it actually made a subtle mistake when stating this relationship. It says: The example under discussion in this quote is the same as the example in this post, where quality and noise have the same variance, and thus R^2=0.5. And superficially it seems to be stating the same thing: the expectation of quality is half the measurement. But actually, this newer post is correct, and the older post is wrong. The key is that "Quality" and "Performance" in this post are not measured in standard deviations. Their standard deviations are 1 and √2, respectively. Elaborating on that: Quality has a variance, and standard deviation, of 1. The variance of Performance is the sum of the variances of Quality and noise, which is 2, and thus its standard deviation is √2. Now that we know their standard deviations, we can scale them to units of standard deviation, and obtain Quality (unchanged) and Performance/√2. The relationship between them is: E[Quality]=1√2⋅Performance√2 That is equivalent to the relationship stated in this post. More generally, notating the variables in units of standard deviation as Zx and Zy (since they are "z-scores"), E[Zy]=ρ⋅Zx where ρ is the correlation coefficient. So if your noisy measurement of quality is Zx standard deviations above its mean, then the expectation of quality is ρZx standard deviations above its mean. It is ρ2 that is variance explained, and is thus 1/2 when the signal and noise have the same variance. That's why in the example in this post, we divide the raw performance by 2, rather than converting it to standard deviations and dividing by 2. I think it's important to understand the relationship between the expected value of an unknown and the value of a noisy measurement of it, so it's nice to see a whole post about this relatio

10Eric Neyman

I think this isn't the sort of post that ages well or poorly, because it isn't topical, but I think this post turned out pretty well. It gradually builds from preliminaries that most readers have probably seen before, into some pretty counterintuitive facts that aren't widely appreciated. At the end of the post, I listed three questions and wrote that I hope to write about some of them soon. I never did, so I figured I'd use this review to briefly give my takes. 1. This comment from Fabien Roger tests some of my modeling choices for robustness, and finds that the surprising results of Part IV hold up when the noise is heavier-tailed than the signal. (I'm sure there's more to be said here, but I probably don't have time to do more analysis by the end of the review period.,) 2. My basic take is that this really is a point in favor of well-evidenced interventions, but that the best-looking speculative interventions are nevertheless better. This is because I think "speculative" here mostly refers to partial measurement rather than noisy measurement. For example, maybe you can only foresee the first-order effects of an intervention, but not the second-order effects. If the first-order effect is a (known) quantity X1 and the second-order effect is an (unknown) quantity X2, then modeling the second-order effect as zero (and thus estimating the quality of the intervention as X1) isn't a noisy measurement; it's a partial measurement. It's still your best guess given the information you have. 1. I haven't thought this through very much. I expect good counter-arguments and counter-counter-arguments to exist here. 3. 1. No -- or rather, only if the measurement is guaranteed to be exactly correct. To see this, observe that the variance of a noisy, unbiased measurement is greater than the variance of the quantity you're trying to measure (with equality only when the noise is zero), whereas the variance of a noiseless, partial measurement is less than the variance of the

#29

A Disneyland Without Children

Two astronauts investigate an automated planet covered in factories still churning out products, trying to understand what happened to its inhabitants.

#30

The Talk: a brief explanation of sexual dimorphism

Malmesbury explains why sexual dimorphism evolved. Starting with asexual reproduction in single-celled organisms, he traces how the need to avoid genetic hitch-hiking led to sexual reproduction, then the evolution of two distinct sexes, and finally to sexual selection and exaggerated sexual traits. The process was driven by a series of evolutionary traps that were difficult to escape once entered.

#34

The Parable of the King and the Random Process

When advisors disagree wildly about when the rains will come, the king tries to average their predictions. His advisors explain why this is a terrible idea – he needs to either decide which model is right or plan for both possibilities.

#37

Cultivating a state of mind where new ideas are born

Innovative work requires solitude, and the ability to resist social pressures. Henrik examines how Grothendieck and Bergman approached this, and lists various techniques creative people use to access and maintain this mental state.

#48

Book Review: Going Infinite

Zvi analyzes Michael Lewis' book "Going Infinite" about Sam Bankman-Fried and FTX. He argues the book provides clear evidence of SBF's fraudulent behavior, despite Lewis seeming not to fully realize it. Zvi sees SBF as a cautionary tale about the dangers of pursuing maximalist goals without ethical grounding.

#50

Why it's so hard to talk about Consciousness

Debates about consciousness often come down to two people talking past each other, without realizing their interlocutor is coming from a fundamentally different set of intuitions. What's up with that?

10Noosphere89

This is a very nice meta-level discussion of why consciousness discourse gets so bad, and I do genuinely appreciate trying to get cruxes and draw out the generators of a disagreement, which is useful in difficult situations. One factor that is not really discussed, but amplifies the problem of discourse around consciousness is that people use the word consciousness to denote a scientific and a moral thing, and people often want to know the answer to whether something is conscious because they want to use it to determine whether uploading is good, or whether to care about someone, and way too much discourse does not decouple these 2 questions. I actually slightly voted against the linked post below in the review, due to methodological problems, but I have a high prior that something like this is a huge contributor to consciousness discourse sucking, and this is an area where the science questions need to be decoupled from value questions: https://www.lesswrong.com/posts/KpD2fJa6zo8o2MBxg/consciousness-as-a-conflationary-alliance-term-for +9 for drawing out a generator on a very confusing topic, and should be in the LW canon for how to deal with difficult disagreements as a worked example. I'm not going to review the object level on what consciousness actually is, because I already did that in a different review linked below, but the sneak peek is that I'm in camp 1, though you could also call me a camp 2 person, but notably reductionist/computationalist rather than positing novel metaphysics: https://www.lesswrong.com/posts/FQhtpHFiPacG3KrvD/seth-explains-consciousness#7ncCBPLcCwpRYdXuG

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