In the FDT paper there is this footnote:

7. In the authors’ preferred formalization of FDT, agents actually iterate over policies (mappings from observations to actions) rather than actions. This makes a difference in certain multi-agent dilemmas, but will not make a difference in this paper.

And it does seem that using FDT, but as a function that returns a policy rather than an action, solves this problem. So this is not an intrinsic problem with FDT that UDT doesn't have, it's a problem that arises in simpler versions of both theories and can be solved in both with the same modification.

This problem doesn't seem to be about trust at all, it seems to be about incomplete sharing of information. It seems weird to me to say Carla doesn't completely trust Bob's account if she is 100% sure he isn't lying.

The sensitivity of the test - that aliens actually abduct people, given someone is telling her aliens abducted him - is 2.5% since she doesn't really know his drug habits and hasn't ruled out there's a LARP she's missing the context for.

I would describe this not as Carla not trusting Bob, but as her not having all of Bob's information - Bob could just tell her that he doesn't use drugs, or that he isn't referring to a LARP, or any other things he knows about himself that Carla doesn't that are causing her sensitivity to be lower, until their probabilities are the same. And, of course, if this process ends with Carla having the same probabilities as Bob, and Carla does the same with Dean, he will have the same probabilities as Bob as well.

I think this satisfies Aumann's Agreement Theorem.

Well, if it does then Bob and Carla definitely have the same probabilities; that's what the problem says, after all.

I had the same confusion when I first heard those names. It's called little-endian because "you start with the little end", and the term comes from an analogy to Gulliver

You're mixing up big-endian and little-endian. Big-endian is the notation used in English: twelve is 12 in big-endian and 21 in little-endian. But yes, 123.456 in big-endian would be 654.321 and with a decimal point, you couldn't parse little-endian numbers in the way described by lsusr.

Katapayadi does seem to be little endian, but the examples I found on Wikipedia of old Indian numerals and their predecessor, Brahmi numerals, seem to be big-endian.