I think that your reasoning here is essentially the same thing I was talking about before:
...the usual philosophical way of thinking about decision theory assumes that the model of the environment is given, whereas in our way of thinking, the model is learned. This is important: for example, if AIXI is placed in a repeated Newcomb’s problem, it will learn to one-box, since its model will predict that one-boxing causes the money to appear inside the box. In other words, AIXI might be regarded as a CDT, but the learned “causal” relationships are not the same as physical causality
Since then I evolved this idea into something that wins in counterfactual mugging as well, using quasi-Bayesianism.
There are some minor differences; your approach learns the whole model, whereas mine assumes the model is given, and learns only the “acausalish” aspects of it. But they are pretty similar.
One problem you might have, is learning the acausal stuff in the mid-term. If the agent learns that causality exists, and then that in the Newcomb problem is seems to have a causal effect, then it may search a lot for the causal link. Eventually this won’t matter (see here), but in the mid-term it might be a problem.
Well, being surprised by Omega seems rational. If I found myself in a real life Newcomb problem I would also be very surprised and suspect a trick for a while.
Moreover, we need to unpack “learns that causality exists”. A quasi-Bayesian agent will eventually learn that it is part of a universe ruled by the laws of physics. The laws of physics are the ultimate “Omega”: they predict the agent and everything else. Given this understanding, it is not more difficult than it should be to understand Newcomb!Omega as a special case of Physics!Omega. (I don’t really have an understanding of quasi-Bayesian learning algorithms and how learning one hypothesis affects the learning of further hypotheses, but it seems plausible that things can work this way.)
I think that your reasoning here is essentially the same thing I was talking about before:
Since then I evolved this idea into something that wins in counterfactual mugging as well, using quasi-Bayesianism.
There are some minor differences; your approach learns the whole model, whereas mine assumes the model is given, and learns only the “acausalish” aspects of it. But they are pretty similar.
One problem you might have, is learning the acausal stuff in the mid-term. If the agent learns that causality exists, and then that in the Newcomb problem is seems to have a causal effect, then it may search a lot for the causal link. Eventually this won’t matter (see here), but in the mid-term it might be a problem.
Or not. We need to test more ^_^
Well, being surprised by Omega seems rational. If I found myself in a real life Newcomb problem I would also be very surprised and suspect a trick for a while.
Moreover, we need to unpack “learns that causality exists”. A quasi-Bayesian agent will eventually learn that it is part of a universe ruled by the laws of physics. The laws of physics are the ultimate “Omega”: they predict the agent and everything else. Given this understanding, it is not more difficult than it should be to understand Newcomb!Omega as a special case of Physics!Omega. (I don’t really have an understanding of quasi-Bayesian learning algorithms and how learning one hypothesis affects the learning of further hypotheses, but it seems plausible that things can work this way.)