Another problem with this is that it isn’t clear how to form the hypothesis “I have control over X”.
You don’t. I’m using talk about control sometimes to describe what the agent is doing from the outside, but the hypothesis it believes all have a form like “The variables such and such will be as if they were set by BDT given such and such inputs”.
One problem with this is that it doesn’t actually rank hypotheses by which is best (in expected utility terms), just how much control is implied.
For the first setup, where its trying to learn what it has control over, thats true. But you can use any ordering of hypothesis for the descent, so we can just take “how good that world is” as our ordering. This is very fragile of course. If theres uncountably many great but unachievable worlds, we fail, and in any case we are paying for all this with performance on “ordinary learning”. If this were running in a non-episodic environment, we would have to find a balance between having the probability of hypothesis decline according to goodness, and avoiding the “optimistic humean troll” hypothesis by considering complexity as well. It really seems like I ought to take “the active ingredient” of this method out, if I knew how.
I’m using talk about control sometimes to describe what the agent is doing from the outside, but the hypothesis it believes all have a form like “The variables such and such will be as if they were set by BDT given such and such inputs”.
Right, but then, are all other variables unchanged? Or are they influenced somehow? The obvious proposal is EDT—assume influence goes with correlation. Another possible answer is “try all hypotheses about how things are influenced.”
Right, but then, are all other variables unchanged? Or are they influenced somehow? The obvious proposal is EDT—assume influence goes with correlation.
I’m not sure why you think there would be a decision theory in that as well. Obviously when BDT decides its output, it will have some theory about how its output nodes propagate. But the hypothesis as a whole doesn’t think about influence. Its just a total probability distribution, and it includes that some things inside it are distributed according to BDT. It doesn’t have beliefs about “if the output of BDT were different”. If BDT implements a mixed strategy, it will have beliefs about what each option being enacted correlates with, but I don’t see a problem if this doesn’t track “real influence” (indeed, in the situations where this stuff is relevant it almost certainly won’t) - its not used in this role.
You don’t. I’m using talk about control sometimes to describe what the agent is doing from the outside, but the hypothesis it believes all have a form like “The variables such and such will be as if they were set by BDT given such and such inputs”.
For the first setup, where its trying to learn what it has control over, thats true. But you can use any ordering of hypothesis for the descent, so we can just take “how good that world is” as our ordering. This is very fragile of course. If theres uncountably many great but unachievable worlds, we fail, and in any case we are paying for all this with performance on “ordinary learning”. If this were running in a non-episodic environment, we would have to find a balance between having the probability of hypothesis decline according to goodness, and avoiding the “optimistic humean troll” hypothesis by considering complexity as well. It really seems like I ought to take “the active ingredient” of this method out, if I knew how.
Right, but then, are all other variables unchanged? Or are they influenced somehow? The obvious proposal is EDT—assume influence goes with correlation. Another possible answer is “try all hypotheses about how things are influenced.”
I’m not sure why you think there would be a decision theory in that as well. Obviously when BDT decides its output, it will have some theory about how its output nodes propagate. But the hypothesis as a whole doesn’t think about influence. Its just a total probability distribution, and it includes that some things inside it are distributed according to BDT. It doesn’t have beliefs about “if the output of BDT were different”. If BDT implements a mixed strategy, it will have beliefs about what each option being enacted correlates with, but I don’t see a problem if this doesn’t track “real influence” (indeed, in the situations where this stuff is relevant it almost certainly won’t) - its not used in this role.