Apollo Creed problems and Clubber Lang problems -- framing, and how to address the latter?

Dear CraigMichael,

I am by no means a guru. It seems like you prefer Apollo Creed problems to Clubber Lang problems because you're more able to *motivate* yourself to do Apollo Creed problems. I feel the same way. I find it exciting to start new projects, and grueling to continue my existing projects. My advice:

If you *need* to solve a Clubber Lang problem, then in moments of clarity, you should establish habits/systems to solve the Clubber Lang problem that *don't* require you to be motivated on any given day.

E.g. go for a jog *even when* you're not feeling ... (read more)

What Money Cannot Buy

Money should be able to guarantee that, over several periods of play, you perform not-too-much-worse than an actual expert. Here: https://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15859-f11/www/notes/lecture16.pdf is a paper about an idealized CS-version of this problem.

31y

Cool piece!
I don't think it's particularly relevant to the problems this post is talking
about, since things like "how do we evaluate success?" or "what questions should
we even be asking?" are core to the problem; we usually don't have lots of
feedback cycles with clear, easy-to-evaluate outcomes. (The cases where we do
have lots of feedback cycles with clear, easy-to-evaluate outcomes tend to be
the "easy cases" for expert evaluation, and those methods you linked are great
examples of how to handle the problem in those cases.)
Drawing from some of the examples:
* Evaluating software engineers is hard because, unless you're already an
expert, you can't just look at the code or the product. The things which
separate the good from the bad mostly involve long-term costs of maintenance
and extensibility.
* Evaluating product designers is hard because, unless you're already an
expert, you won't consciously notice the things which matter most in a
design. You'd need to e.g. a/b test designs on a fairly large user base, and
even then you need to be careful about asking the right questions to avoid
Goodharting.
* In the smallpox case, the invention of clinical trials was exactly what gave
us lots of clear, easy-to-evaluate feedback on whether things work. Louis XV
only got one shot, and he didn't have data on hand from prior tests.

How likely is it that SARS-CoV-2 originated in a laboratory?

With regard to the rootclaim link, I agree that it would be good to try to adapt what they've done to our own beliefs. However, I want to urge some caution with regard to the actual calculation shown on that website. The event to which they give a whopping 81% probability, "the virus was developed during gain-of-function research and was released by accident," is a conjunction of two independent theses. We have to be very cautious about such statements, as pointed out in the Rationality A-Z, here https://www.lesswrong.com/s/5g5TkQTe9rmPS5vvM/p/Yq6aA4M3JKWaQepPJ

How likely is it that SARS-CoV-2 originated in a laboratory?

I mean to include *all* the alternatives that involve the virus passing through a laboratory before spreading to humans; so all the options you list are included. There's nothing wrong with asking about the probability of a composite event.

The VARIANCE of a random variable seems like one of those ad hoc metrics. I would be

veryhappy for someone to come along and explain why I'm wrong on this. If you want to measure, as Wikipedia says, "how far a set of numbers is spread out from their average value," why use E[ (X - mean)^2 ] instead of E[ |X - mean| ], or more generally E[ |X - mean|^p ]? The best answer I know of is that E[ (X - mean)^2 ] is easier to calculate than those other ones.Variance has more motivation than just that it's a measure of how spread out the distribution is. Variance has the property that if two random variables are independent, then the variance of their sum is the sum of their variances. By the central limit theorem, if you add up a sufficiently large number of independent and identically distributed random variables, the distribution you get is well-approximated by a distribution that depends only on mean and variance (or any other measure of spreadout-ness). Since it is the variance of the distributions you we... (read more)