I like this post very much and in general I think research like this is on the correct lines towards solving potential problems with Goodheart’s law—in general Bayesian reasoning and getting some representation of the agent’s uncertainty (including uncertainty over our values!) seems very important and naturally ameliorates a lot of potential problems. The correctness and realizability of the prior are very general problems with Bayesianism but often do not thwart its usefulness in practice although they allow people to come up with various convoluted counterexamples of failure. The key is to have sufficiently conservative priors such that you can (ideally) prove bounds about the maximum degree of goodhearting that can occur under realistic circumstances and then translate these into algorithms which are computationally efficient enough to be usable in practice. People have already done a fair bit of work on this in RL in terms of ‘cautious’ RL which tries to take into account uncertainty in the world model to avoid accidentally falling into traps in the environment.
People have already done a fair bit of work on this in RL in terms of ‘cautious’ RL which tries to take into account uncertainty in the world model to avoid accidentally falling into traps in the environment.
I like this post very much and in general I think research like this is on the correct lines towards solving potential problems with Goodheart’s law—in general Bayesian reasoning and getting some representation of the agent’s uncertainty (including uncertainty over our values!) seems very important and naturally ameliorates a lot of potential problems. The correctness and realizability of the prior are very general problems with Bayesianism but often do not thwart its usefulness in practice although they allow people to come up with various convoluted counterexamples of failure. The key is to have sufficiently conservative priors such that you can (ideally) prove bounds about the maximum degree of goodhearting that can occur under realistic circumstances and then translate these into algorithms which are computationally efficient enough to be usable in practice. People have already done a fair bit of work on this in RL in terms of ‘cautious’ RL which tries to take into account uncertainty in the world model to avoid accidentally falling into traps in the environment.
I would appreciate some pointers to resources