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 motivated to go for a jog, because you set out your jogging clothes and shoes the night before, & you made a program to pay your rival $5 on venmo if you don't log your jog on MapMyRun.com
Wishing you the best,
CTVKenney
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.
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
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 very happy 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.