Want to wade into the snowy surf of the abyss? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid.
Welcome to the Stubsack, your first port of call for learning fresh Awful youāll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cutānāpaste it into its own post ā thereās no quota for posting and the bar really isnāt that high.
The post Xitter web has spawned so many āesotericā right wing freaks, but thereās no appropriate sneer-space for them. Iām talking redscare-ish, reality challenged āculture criticsā who write about everything but understand nothing. Iām talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyāre inescapable at this point, yet I donāt see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldnāt be surgeons because they didnāt believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canāt escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this. What a year, huh?)
I know what it says and itās commonly misused. Aumannās Agreement says that if two people disagree on a conclusion then either they disagree on the reasoning or the premises. Itās trivial in formal logic, but hard to prove in Bayesian game theory, so of course the Bayesians treat it as some grand insight rather than a basic fact. That said, I donāt know what that LW post is talking about and I donāt want to think about it, which means that I might disagree with people about the conclusion of that post~
I think Aumannās theorem is even narrower than that, after reading the Wikipedia article. The theorem doesnāt even reference āreasoningā, unless you count observing that a certain event happened as reasoning.
I donāt think thatās an accurate summary. In Aumannās agreement theorem, the different agents share a common prior distribution but are given access to different sources of information about the random quantity under examination. The surprising part is that they agree on the posterior probability provided that their conclusions (not their sources) are common knowledge.