A theory of physics is mathematically quite similar to a cellular automaton. This theory will usually be incomplete, something that we can represent in infra-Bayesianism by Knightian uncertainty. So, the "cellular automaton" has underspecified time evolution.
What evidence is there that incomplete models with Knightian uncertainty are a way to turn rough models of physics into loss functions? Can the ideas behind it be applied to regular Bayesianism?
https://arxiv.org/abs/2211.06738 is related
Sure, I mean that it is an implementation of what you mentioned in the third-to-last paragraph.
Congratulations, you discovered [Active Inference]!
Solomonoff Induction and Machine Learning. How would you formulate this in terms of a machine that can only predict future observations?
I want to provide feedback, but can't see the actual definition of the objective function in either of the cases. Can you write down a sketch of how this would be implemented using existing primitives (SI, ML) so I can argue against what you're really intending?
Some preliminary thoughts: