No One Knows Stuff

People don't seem to vote posts up or down with the same enthusiasm as they vote on comments. Why? I do not know.

Done, thanks for the feedback!

I made the mistake I'm talking about---assuming certain things were well-known.

Seconded! Those names didn't ring a bell for me either, though I'm familiar with the results from Prespect Theory (I probably read about them on OB), and that's probably what talisman was refering to.

I strongly recommend python for doing this, it is the best language to begin programming with, I have several programs I did by myself, I can collaborate with the project.

Not disagreeing with you here, but you seem to have missed the implication; the reason Python was mentioned is because LessWrong is written in it.

I actually think Liron's slideshow needs a lot of work, but it seems very much like the kind of thing LWers should be trying to do out in the world.

I would agree.

OK. Can you improve it or do better?

Possibly, but I have little reason to do so since this sort of thing is not particularly applicable to my life.

Of note, I am not trying to be a jerk or make this a big deal. My comment really has little to do with Liron's post. It has everything to do with you telling me to upvote something. I just, politely, want you to not do that again. I had typed up more details on why I downvoted but they are irrelevant for what I wanted to say to you.

Or better yet, somewhere other than LW.

no, we definitely don't require that, but we are a LOT more selective than, say Goldman or D.E. Shaw in other ways and we do require that you be able to function as part of a team.

It is - but that's OB and LW.

Strongly seconded. Generally, it seems to me that Eliezer frequently seriously confuses people by mixing literal statements with hyperbole like this or "shut up and do the impossible". I definitely see the merit of the greater emotional impact, but I hope there's some way to get it without putting off the unusually literal-minded (which I expect most people who will get anything out of OB or The Book are).

Yeah, that is kind of tricky. Let me try to explain what Eliezer_Yudkowsky meant in terms of my preferred form of the Bayes Theorem:

O(H|E) = O(H) * P(E|H) / P(E|~H)

where O indicates odds instead of probability and | indicates "given".

In other words, "any time you observe evidence, amplify the odds you assign to your beliefs by the probability of observing the evidence if the belief were true, divided by the probabily of observing it if the belief were false."

Also, keep in mind that Eliezer_Yudkowsky has written about how you should treat very low probability events as being "impossible", even though you have to assign a non-zero probability to everything.

Nevertheless, his statement still isn't literally true. The strength of the evidence depends on the ratio P(E|H)/P(E|~H), while the quoted statement only refers to the denominator. So there can be situations where you have 100:1 odds of seeing E if the hypothesis were true, but 1:1000 odds (about a 0.1% chance) of seeing E if it were false.

Such evidence is very strong -- it forces you to amplify the odds you assign to H by a factor of 100,000 -- but it's far from evidence you "couldn't possibly find", which to me means something like 1:10^-10 odds.

Still, Eliezer_Yudkowsky is right that, generally, strong evidence will have a very small denominator.

EDIT: added link

Strong evidence is evidence that, given certain premises, has no chance of arising.

Of course, Eliezer has also claimed that nothing can have no chance of arising (probability zero), so it's easy to see how one might be confused about his position.

Traditionally, evidence that has less than a particular value of arising given the truth of a hypothesis (usually 5%) is considered to be strong, but that's really an arbitrary decision.

First, you have to explain why relying on external math, rather than on a hunch, is a good idea. Second, you need to present a case for why shutting up and multiplying in this particular way is a good idea.

The wikipedia reference is a bit patchy. This Introduction to Choice under Risk and Uncertainy is pretty good if you have a bit more time, and can handle the technical parts.

Perhaps check my references here:

http://timtyler.org/expected_utility_maximisers/