I usually imagine the problems of embedded agency (at least when I’m reading LW/AF), where the central issue is that the agent is a part of its environment (in contrast to the Cartesian model, where there is a clear, bright line dividing the agent and the environment). Afaict, “embedded Naive Bayes” is something that makes sense in a Cartesian model, which I wasn’t expecting.
It’s not that big a deal, but if you want to avoid that confusion, you might want to change the word “embedded”. I kind of want to say “The Intentional Stance towards Naive Bayes”, but that’s not right either.
Ok, that’s what I was figuring. My general position is that the problems of agents embedded in their environment reduce to problems of abstraction, i.e. world-models embedded in computations which do not themselves obviously resemble world-models. At some point I’ll probably write that up in more detail, although the argument remains informal for now.
The immediately important point is that, while the OP makes sense in a Cartesian model, it also makes sense without a Cartesian model. We can just have some big computation, and pick a little chunk of it at random, and say “does this part here embed a Naive Bayes model?” In other words, it’s the sort of thing you could use to detect agenty subsystems, without having a Cartesian boundary drawn in advance.