CDT does not avoid this issue by “setting its priors to the delta function”. CDT deals with this issue by being a theory where your course of action only depends on your posterior distribution. You can base your actions only on what the universe actually looks like rather than having to pay attention to all possible universes. Given that it’s basically impossible to determine anything about what Kolmogorov priors actually say, being able to totally ignore parts of probability space that you have ruled out is a big deal.
… And this whole issue with not being able to self-modify beforehand. This only matters if your initial code affects the rest of the universe. To be more precise, this is only an issue if the problem is phrased in such a way that the universe you have to deal with depends on the code you are running. If we instantiate the Newcomb’s problem in the middle of the decision, UDT faces a world with the first box full while CDT faces a world with the first box empty. UDT wins because the scenario is in its favor before you even start the game.
If you really think that this is a big deal, you should try to figure out which decision theories are only created by universes that want to be nice to them and try using one of those.
Actually thinking about it this way, I have seen the light. CDT makes the faulty assumption that your initial state in uncorrelated with the universe that you find yourself in (who knows, you might wake up in the middle of Newcomb’s problem and find that whether or not you get $1000000 depends on whether or not your code is such that you would one-box in Newcomb’s problem). UDT goes some ways to correct this issue, but it doesn’t go far enough.
I would like to propose a new, more optimal decision theory. Call it ADT for Anthropic Decision Theory. Actually, it depends on a prior, so assume that you’ve picked out one of those. Given your prior, ADT is the decision theory D that maximizes the expected (given your prior) lifetime utility of all agents using D as their decision theory. Note how agents using ADT do provably better than agents using any other decision theory.
Note that I have absolutely no idea what ADT does in, well, any situation, but that shouldn’t stop you from adopting it. It is optimal after all.
CDT does not avoid this issue by “setting its priors to the delta function”. CDT deals with this issue by being a theory where your course of action only depends on your posterior distribution. You can base your actions only on what the universe actually looks like rather than having to pay attention to all possible universes. Given that it’s basically impossible to determine anything about what Kolmogorov priors actually say, being able to totally ignore parts of probability space that you have ruled out is a big deal.
… And this whole issue with not being able to self-modify beforehand. This only matters if your initial code affects the rest of the universe. To be more precise, this is only an issue if the problem is phrased in such a way that the universe you have to deal with depends on the code you are running. If we instantiate the Newcomb’s problem in the middle of the decision, UDT faces a world with the first box full while CDT faces a world with the first box empty. UDT wins because the scenario is in its favor before you even start the game.
If you really think that this is a big deal, you should try to figure out which decision theories are only created by universes that want to be nice to them and try using one of those.
Actually thinking about it this way, I have seen the light. CDT makes the faulty assumption that your initial state in uncorrelated with the universe that you find yourself in (who knows, you might wake up in the middle of Newcomb’s problem and find that whether or not you get $1000000 depends on whether or not your code is such that you would one-box in Newcomb’s problem). UDT goes some ways to correct this issue, but it doesn’t go far enough.
I would like to propose a new, more optimal decision theory. Call it ADT for Anthropic Decision Theory. Actually, it depends on a prior, so assume that you’ve picked out one of those. Given your prior, ADT is the decision theory D that maximizes the expected (given your prior) lifetime utility of all agents using D as their decision theory. Note how agents using ADT do provably better than agents using any other decision theory.
Note that I have absolutely no idea what ADT does in, well, any situation, but that shouldn’t stop you from adopting it. It is optimal after all.