All of zac_kenton's Comments + Replies

Thanks for the comment Michael. Firstly, just wanted to clarify the framing of this literature review - when considering strengths and weaknesses of each threat model, this was done in light of what we were aiming to do: generate and prioritise alignment research projects -- rather than as an all-things-considered direct critique of each work (I think that is best done by commenting directly on those articles etc). I'll add a clarification of that at the top. Now to your comments:

To your 1st point: I think the lack of specific assumptions about the AGI dev... (read more)

On the 2nd point, the whole discussion of mu^prox vs. mu^dist is fundamentally about goal (mis)generalization. My position is that for a very advanced agent, point estimates of the goal (i.e. certainty that some given account of the goal is correct) would probably really limit performance in many contexts. This is captured by Assumptions 2 and 3. An advanced agent is likely to entertain multiple models of what their current understanding of their goal in a familiar context implies about their goal in a novel context. Full conviction in mu^dist does indeed imply non-wireheading behavior, and I wouldn't even call it misgeneralization; I think that would be a perfectly valid interpretation of past rewards. So that's why I spend so much time discussing relative credence in those models.

I haven't considered this in great detail, but if there are  variables, then I think the causal discovery runtime is .  As we mention in the paper (footnote 5) there may be more efficient causal discovery algorithms that make use of certain assumptions about the system. 

On adoption, perhaps if one encounters a situation where the computational cost is too high, one could coarse-grain their variables to reduce the number of variables.  I don't have results on this at the moment but I expect that the presence of agency (no... (read more)