If you use the Bayes approach with a Beta(x,y) prior, all you do is for each post add x to the # of upvotes, add y to the # of downvotes, and then compute the % of votes which are upvotes. [1]

In my college AI class we used this exact method with x=y=1 to adjust for low sample size. Someone should switch out the clunky frequentist method reddit apparently uses with this Bayesian method!

[1] This seems to be what it says in the pdf.

I think that the answer to 2 is incorrect.

Yeah. According to Google, the experiment did find that people neglect base rates:

Kahneman and Tversky (1973) observed that the mean [estimate of the probability that Jack is an engineer] in the two groups, one receiving the base rate information 30 to 70, the other receiving 70 to 30, were for the most part the same

Ironically, when analyzing the experiment, the Vanity Fair writers failed Bayes theorem in the opposite way: neglecting evidence, thus making the posterior equal to the prior.

We conclude that people don't understand Bayes.

Yup.

Students manage conflicts by simply skipping class sessions. Last semester, I often skipped two thirds of my class sessions. As long as you read lecture notes, do the work, and show up to tests, you're fine.

At MIT, some students take 8+ classes over ~15 weeks. This involves lots of busywork and an expectation of getting the highest grade (an A). [They also often do side projects.]

Scott Young aims to complete classes at the same rate. But he's skipping much of the busywork and requiring merely passing grades. I wouldn't be surprised if he pulls it off.

I'm an MIT student and currently spend 60-100 hours/class. Taking Young's approach, I could probably average 30 hours/class, which for 33 classes might be doable in about 2 months... Maybe doing 33 MIT classes in 1 month is something for a Tim Ferriss.

So, if I were to make you a bet that #1-and-#2 is true [then you should rationally accept it if] gjmgain0.01>owengain0.99..

If you seriously made such a bet, then gjm would probably update on that evidence and revise his 99% probability upwards.

[But as gjm says the bet is impractical anyway because it's too hard to resolve]

nobody has bothered much with trying to steer [discussions] closer to reality

Feels like you have forbidden knowledge. Not coincidentally, I want to know what it is.

What is it roughly? That innate differences across the sexes play a strong role in causing statistically different mating behaviors to develop? That these differences end up somewhat resembling "females want high-value sex and a devoted father while males want sex and sexually faithful partners"? That females are often attracted to high value behavior (e.g. PUA stuff)? That many people have some, possibly very vague, estimate of how sexually valuable they are, and act upon this belief? Is there any way you can quench my curiosity? It seems obvious that if you answer in general terms you won't offend anyone, as meta thought doesn't really push the emotional buttons.

PS: It has been suggested that general statements can cause worse beliefs in a group, since they're very simplified. But there should be some way of pointing to an area of the map without degrading that region of the map.

Previous discussion on the same video: http://lesswrong.com/lw/2xe/interesting_talk_on_bayesians_and_frequentists/

Simon Cowell is known as the brutally honest [singing contest] judge. He worked himself up in the music industry and doesn't seem to be a nutjob. Personally, I think his name is good for the reputation of cryonics.