As I said, be careful about using Bayes' theorem in the case where the agent's mind is being meddled with by amnesia-inducing drugs. If Beauty had not had her mind addled by drugs, your formula would work - and p(H|D) would be equal to 1/2 on her first awakening. As it is, Beauty has lost some information that pertains to the answer she gives to the problem - namely the knowledge of whether she has been woken up before already - or not. Her uncertainty about this matter is the cause of the problem with plugging numbers into Bayes' theorem.

The theorem models her update on new information - but does not model the drug-induced deletion from her mind of information that pertains to the answer she gives to the problem.

If she knew it was Monday, p(H|D) would be about 1/2. If she knew it was Tuesday, p(H|D) would be about 0. Since she is uncertain, the value lies between these extremes.

Is over-reliance on Bayes' theorem - without considering its failure to model the problem's drug-induced amnesia - a cause of people thinking the answer to the problem is 1/2, I wonder?

I don't see how that analysis is useful. Beauty is awake at the start and the end of the experiment, and she updates accordingly, depending on whether she believes she is "inside" the experiment or not. So, having D mean: "Sleeping Beauty is awake" does not seem very useful. Beauty's "data" should also include her knowledge of the experimental setup, her knowledge of the identity of the subject, and whether she is facing an interviewer with amnesia. These things vary over time - and so they can't usefully be treated as a single probability.

You should be careful if plugging values into Bayes' theorem in an attempt to solve this problem. It contains an amnesia-inducing drug. When Beauty updates, you had better make sure to un-update her again afterwards in the correct manner.

Subjective probabilities are traditionally analyzed in terms of betting behavior. Bets that are used for elucidating subjective probabilities are constructed using "scoring rules". It's a standard way of revealing such probabilities.

I am not sure what you mean by "abandoning Bayes' Law", or using "bizarre" interpretations of probability. In this case, the relevant data includes the design of the experiment - and that is not trivial to update on, so there is scope for making mistakes. Before questioning the integrity of your tools, is it possible that a mistake was made during their application?

Bayesians should not answer ½. Nobody should answer ½: that's the wrong answer.

If your interpretation of the word "credence" leads you to answer ½, you are fighting with the rest of the community over the definition of the concept of subjective probability.

Most of what you said here has already been said, and rebutted, in the comments on the Sleeping Beauties post, and in the followup post by Jonathan Lee. It would be polite, and helpful, to address those rebuttals. Simply restating arguments, without acknowledging counterarguments, could be a big part of why we don't seem to be getting anywhere.

The Sleeping Beauty problem and the other "paradoxes" of probability are problems that have been selected (in the evolutionary sense) because they contain psychological features that cause people's reasoning to go wrong. People come up with puzzles and problems all the time, but the ones that gain prominence and endure are the ones that are discussed over and over again without resolution: Sleeping Beauty, Newcomb's Box, the two-envelope problem.

So I think there's something valuable to be learned from the fact that these problems are hard. Here are my own guesses about what makes the Sleeping Beauty problem so hard.

First, there's ambiguity in the problem statement. It usually asks about your "credence". What's that? Well, if you're a Bayesian reasoner, then "credence" probably means something like "subjective probability (of a hypothesis H given data D), defined by p(H|D) = p(D|H) p(H) / p(D)". But some other reasoners take "credence" to mean something like "expected proportion of observations consistent with data D in which the hypothesis H was confirmed".

In most problems these definitions give the same answer, so there's normally no need to worry about the exact definition. But the Sleeping Beauty problem pushes a wedge between them: the Bayesians should answer ½ and the others ⅓. This can lead to endless argument between the factions if the underlying difference in definitions goes unnoticed.

Second, there's a psychological feature that makes some Bayesian reasoners doubt their own calculation. (You can try saying "shut up and calculate" to these baffled reasoners but while that might get them the right answer, it won't help them resolve their bafflement.) The problem somehow persuades some people to imagine themselves as an instance of Sleeping Beauty selected uniformly from the three instances {(heads,Monday), (tails,Monday), (tails,Tuesday)}. This appears to be a natural assumption that some reasoners are prepared to make, even though there's no justification for it in the problem description.

Maybe it's the principle of indifference gone wrong: the three instances are indistinguishable (to you) but that doesn't mean the one you are experiencing was drawn from a uniform distribution.

Brave New World is definitely dystopian, not post-utopian. Nancy's suggestion for post-utopian is exactly right. I definitely agree that we can meaningfully classify cultural production, though.

You write, “suppose your postmodern English professor teaches you that the famous writer Wulky Wilkinsen is actually a ‘post-utopian’. What does this mean you should expect from his books? Nothing.”

I’m sympathetic to your general argument in this article, but this particular jibe is overstating your case.

There may be nothing particularly profound in the idea of ‘post-utopianism’, but it’s not meaningless. Let me see if I can persuade you.

Utopianism is the belief that an ideal society (or at least one that's much better than ours) can be constructed, for example by the application of a particular political ideology. It’s an idea that has been considered and criticized here on LessWrong. Utopian fiction explores this belief, often by portraying such an ideal society, or the process that leads to one. In utopian fiction one expects to see characters who are perfectible, conflicts resolved successfully or peacefully, and some kind of argument in favour of utopianism. Post-utopian fiction is written in reaction to this, from a skeptical or critical viewpoint about the perfectibility of people and the possibility of improving society. One expects to see irretrievably flawed characters, idealistic projects turn to failure, conflicts that are destructive and unresolved, portrayals of dystopian societies and argument against utopianism (not necessarily all of these at once, of course, but much more often than chance).

Literary categories are vague, of course, and one can argue about their boundaries, but they do make sense. H. G. Wells’ “A Modern Utopia” is a utopian novel, and Aldous Huxley’s “Brave New World” is post-utopian.