I think you've pretty much got it. Basically, instead of trying to figure out a universal morality across humans, you just say 'okay, fine, people are black boxes whose behavior you can predict, let's build a system to deal with that black box.'
However, instead of trying to get T to be immune to wireheading, I suggested that we require reflexive consistency -- i.e. the model-as-it-is-now should be given a veto vote over predicted future states of itself. So, if the AI is planning to turn you into a barely-sapient happy monster, your model should be able to look at that future and say 'no, that's not me, I don't want to become that, that agent doesn't speak for me,' replacing the value of T with zero utility.
EDIT: There's almost certainly a better way to do it than naively asking the question, but that will suffice for this discussion.
OK, I think I see.
So, one can of course get arbitrarily fussy about this sort of thing in not-very-interesting ways, but I guess the core of my question is: why in the world should the judge (AI or whatever) treat its model of me as a black box? What does that add?
For example, if the model of me-as-I-am-now rejects wireheading, the judge presumably knows precisely why it rejects wireheading, in the sense that it knows the mechanisms that lead to that rejection. After all, it created those mechanisms in its model, and is executing them. They aren't mysteri...
I've been reading through this to get a sense of the state of the art at the moment:
http://lukeprog.com/SaveTheWorld.html
Near the bottom, when discussing safe utility functions, the discussion seems to center on analyzing human values and extracting from them some sort of clean, mathematical utility function that is universal across humans. This seems like an enormously difficult (potentially impossible) way of solving the problem, due to all the problems mentioned there.
Why shouldn't we just try to design an average bounded utility maximizer? You'd build models of all your agents (if you can't model arbitrary ordered information systems, you haven't got an AI), run them through your model of the future resulting from a choice, take the summation of their utility over time, and take the average across all the people all the time. To measure the utility (or at least approximate it), you could just ask the models. The number this spits out is the output of your utility function. It'd probably also be wise to add a reflexive consistency criteria, such that the original state of your model must consider all future states to be 'the same person.' -- and I acknowledge that that last one is going to be a bitch to formalize. When you've got this utility function, you just... maximize it.
Something like this approach seems much more robust. Even if human values are inconsistent, we still end up in a universe where most (possibly all) people are happy with their lives, and nobody gets wireheaded. Because it's bounded, you're even protected against utility monsters. Has something like this been considered? Is there an obvious reason it won't work, or would produce undesirable results?
Thanks,
Dolores