By bounded, I simply meant that all reported utilities are normalized to a universal range before being summed. Put another way, every person has a finite, equal fraction of the machine's utility to distribute among possible future universes. This is entirely to avoid utility monsters. It's basically a vote, and they can split it up however they like.
Also, the reflexive consistency criteria should probably be applied even to people who don't exist yet. We don't want plans to rely on creating new people, then turning them into happy monsters, even if it doesn't impact the utility of people who already exist. So, basically, modify the reflexive utility criteria to say that in order for positive utility to be reported from a model, all past versions of that model (to some grain) must agree that they are a valid continuation of themselves.
I'll need to think harder about how to actually implement the approval judgements. It really depends on how detailed the models we're working with are (i.e. cable of realizing that they are a model). I'll give it more thought and get back to you.
how to actually implement the approval judgements
This matters more for initial conditions. A mature "FAI" might be like a cross between an operating system, a decision theory, and a meme, that's present wherever sufficiently advanced cognition occurs; more like a pervasive culture than a centralized agent. Everyone would have a bit of BAUM in their own thought process.
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