Ken G said:
An answer can always be changed by doing something different.
You assume that there is something there that we can be different from.
You could change the odds of poker by assuming every hand has equal probability, but that's no way to win poker.
I agree that many books pose poker problems and omit to explicitly state that all deals are to be regarded as equally probable. A traditional interpretation is for the reader to assume the author of the problem means that to be the case. However, it's not traditional (except perhaps among ultra-Bayesians) that the existence of N possibilities in an arbitrary probability word problem can be taken to imply that each possibility has probability 1/N.
As I said before a person can take the position that the author of the Sleeping Beauty problem intended to say (or for us to assume) that the situation "When Sleeping Beauty is awakened" has an equal probability of being any of the 3 possible situations. Or a person can take the position that we are permitted to use a Bayesian approach and explicitly assume each situation has as equal probability.
To design an empirical test to decide whether the "halfer" or "thirder" or some other answer is correct, the problem gives enough information to simulate many runs of the experiment. In those runs, the events C =(tails, Monday, awake) and D = (tails, Tuesday, awake) are not mutually exclusive events. In fact P(C|D) = 1. Both events occur in the experiment when the coin lands tails.
However, when we contemplate how to implement a distribution F to simulate the event "Sleeping Beauty is awakened", we must have a procedure that stochastically selects a single situation when she is awakened. In implementing that procedure A and B are
mutually exclusive events. This makes it clear that simulating the situation when Sleeping Beauty is awakened does not treat events in the same way as the probability space for the experiment, where A an B are not mutually exclusive events.
I think you take it for granted that the way to to implement F would be to pick a situation at random from those that occurred in the runs of the trials, giving each the same probability. Or we could step through each situation that did occur and compute some frequencies, which , I think, amounts to the same assumption. I agree these are reasonable approaches. However, they aren't specified in the statement of the problem.