Actually, no, improper priors such as you suggest are not part of the foundations of Bayesian probability theory. It's only legitimate to use an improper prior if the result you get is the limit of the results you get from a sequence of progressively more diffuse priors that tend to the improper prior in the limit. The Marginalization Paradox is an example where just plugging in an improper prior without considering the limiting process leads to an apparent contradiction. My analysis (http://ksvanhorn.com/bayes/Papers/mp.pdf) is that the problem there ultimately stems from non-uniform convergence.
I've had some email discussions with Scott Aaronson, and my conclusion is that the Dice Room scenario really isn't an appropriate metaphor for the question of human extinction. There are no anthropic considerations in the Dice Room, and the existence of a larger population from which the kidnap victims are taken introduces complications that have no counterpart when discussing the human extinction scenario.
You could formalize the human extinction scenario with unrealistic parameters for growth and generational risk as follows:
Let n be the number of generations for which humanity survives.
The population in each generation is 10 times as large as the previous generation.
There is a risk 1/36 of extinction in each generation. Hence, P(n=N+1 | n >= n) = 1/36.
You are a randomly chosen individual from the entirety of all humans who will ever exist. Specifically, P(you belong to generation g) = 10^g / N, where N is the sum of 10^t for 1 <= t <= n.
Analyzing this problem, I get
P(extinction occurs in generation t | extinction no earlier than generation t) = 1/36
P(extinction occurs in generation t | you are in generation t) = about 9/10
That's a vast difference depending on whether or not we take into account anthropic considerations.
The Dice Room analogy would be if the madman first rolled the dice until he got snake-eyes, then went out and kidnapped a bunch of people, randomly divided them into n batches, each 10 times larger than the previous, and murdered the last batch. This is a different process than what is described in the book, and results in different answers.
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I think this shows how the whole "language independent up to a constant" thing is basically just a massive cop-out. It's very clever for demonstrating that complexity is a real, definable thing, with properties which at least transcend representation in the infinite limit. But as you show it's useless for doing anything practical.
My personal view is that there's a true universal measure of complexity which AIXI ought to be using, and which wouldn't have these problems. It may well be unknowable, but AIXI is intractable anyway so what's the difference? In my opinion, this complexity measure could give a real, numeric answer to seemingly stupid questions like "You see a number. How likely is it that the number is 1 (given no other information)?". Or it could tell us that 16 is actually less complex than, say, 13. I mean really, it's 2^2^2, spurning even a need for brackets. I'm almost certain it would show up in real life more often than 13, and yet who can even show me a non-contrived language or machine in which it's simpler?
Incidentally, they "hell" scenario you describe isn't as unlikely as it at first sounds. I remember an article here a while back lamenting the fact that left unmonitored AIXI could easily kill itself with exploration, the result of which would have a very similar reward profile to what you describe as "hell". It seems like it's both too cautious and not cautious enough in even just this one scenario.