There's a pretty neat and general way of abstracting learning systems out into agent, ultimate values and environment using the framework of reinforcement learning.
Interesting. I'd appreciate references or links. To me, the interesting and still open question is how these "ultimate" values relate to the outcome of rational reflection and experimentation by the individual.
I just mean the cybernetic agent-environment framework with a reward/utility signal. For example, see page 1 of Hibbard's recent paper, page 5 of UNIVERSAL ALGORITHMIC INTELLIGENCE A mathematical top!down approach, or page 39 of Machine Super Intelligence.
To me, the interesting and still open question is how these "ultimate" values relate to the outcome of rational reflection and experimentation by the individual
So: changes to ultimate values can potentially happen when there are various kinds of malfunction. Memetic hijacking illustrates on...
The jacket text for Keith Stanovich's The Robot's Rebellion sums up the book well:
The book is an excellent introduction to the first stage of Yudkowskian philosophy: We are robots in a mechanistic universe running on a swiss army knife of cognitive modules. But at least we finally noticed we're robots, and we can use the skills of rationality to hop off our habit treadmills and pursue our values instead. These values are complex and often arbitrary, but we can use our reflective capacities to extrapolate our values based on "higher-order" desires, a desire for preference consistency, and other considerations. All this is argued for at length in Stanovich's book. The only thing missing is a discussion of what to do about all this when AI arrives.