How are you defining territory here? If the territory is 'reality' the only place where quantum mechanics connects to reality is when it tells us the outcome of measurements. We don't observe the wavefunction directly, we measure observables.

I think the challenge of MWI is to make the probabilities a natural result of the theory, and there has been a fair amount of active research trying and failing to do this. RQM side steps this by saying "the observables are the thing, the wavefunction is just a map, not territory."

I haven't read the comments yet, so apologies if this has already been said or addressed:

If I am watching others debate, and my attention is restricted to the arguments the opponents are presenting, then my using the "one strong argument" approach may not be a bad thing.

I'm assuming that weak arguments are easy to come by and can be constructed for any position, but strong arguments are rare.

In this situation I would expect anybody who has a strong argument to use it, to the exclusion of weaker ones: if A and B both have access to 50 weak arguments, and A also has access to 1 strong argument, then I would expect the debate to come out looking like (50weak) vs. (1strong) - even though the underlying balance would be more like (50weak) vs. (50weak + 1strong).

(By "having access to" an argument, I mean to include both someone's knowing an argument, and someone's having the potential to construct or come across an argument with relatively little effort.)

So then this initial probability estimate, 0.5, is not repeat not a "prior".

This really confuses me. Considering the Universe in your example, which consists only of the urn with the balls, wouldn't one of the prior hypotheses(e.g. case 2) be a prior and have all the necessary information to compute the lookup table?

In other words aren't the three following equivalent in the urn-with-balls universe?

1. Hypothesis 2 + bayesian updating
2. Python program 2
3. The lookup table generated from program 2 + Procedure for calculating conditional probability(e.g. if you want to know the probability that the third ball is red, given that the first two balls drawn were white.)

One of my favorite lessons from Bayesianism is that the task of calculating the probability of an event can be broken down into simpler calculations, so that even if you have no basis for assigning a number to P(H) you might still have success estimating the likelihood ratio.

And current mathematician have them with infinitesimally small numbers ;)

So, should I seek for reasonableness or rationality to prevail, whenever the rational is outside the Overton window? My dilemma is that I find more pleasure on being rational, so rationality stands I should seek for rationality, whereas the reasonable thing to do would be to stand with reasonableness and shut up.

The point is: whenever I can't decide on one over the other, which criterion should I use to make the decission, since each seems to point towards itself? This is fun.

What Do We Mean When We Say Bias?

Eliezer talked about cognitive bias, statistical bias, and inductive bias in a series of posts only the first of which made it directly into the sequences as currently organized (unless I've missed them!)

We also use the word bias when we speak about how funding sources can bias a researcher.

But even if those do dominate, his involvement in math is still (weak) Bayesian evidence for my claim.

Can you quantify what you mean with "weak"?

Is there any particular reason to focus on anecdotes and not focus on the percentage of millionaires who have specific degrees?

http://time100.time.com/2013/11/11/these-are-the-most-popular-college-degrees-earned-by-millionaires/ gives for example a list.

I got 6 as the answer, basing it on 1. presence of inner circle 2. outer box apparently following a pattern.

But there's a high chance i'm privileging my observations.