What probability do you give the simulation hypothesis?
Some extremely low prior based on its necessary complexity.
This is true - and I do think the probability of this is negligible.
No, you have no information about that probability. You can assign a complexity prior to it and nothing more.
Why do those conflict at all? I feel like you may be talking about a nonstandard use of occam's razor.
They conflict because you have two perspectives, and therefore two different sets of information, and therefore two very different distributions. Assume the scenario happens: the person running the simulation from outside has information about the simulation. They have the evidence necessary to defeat the low prior on "everything So and So experiences is a simulation". So and So himself... does not have that information. His limited information, from sensory data that exactly matches the real, physical, lawful world rather than the mutable simulated environment, rationally justifies a distribution in which, "This is all physically real and I am in fact not going to a tropical paradise in the next minute because I'm not a computer simulation" is the Maximum a Posteriori hypothesis, taking up the vast majority of the probability mass.
So, the standard Bayesian analogue of Solomonoff induction is to put a complexity prior over computable predictions about future sensory inputs. If the shortest program outputting your predictions looks like a specification of a physical world, and then an identification of your sensory inputs within that world, and the physical world in your model has both a meatspace copy of you and a simulated copy of you, the only difference in this Solomonoff-analogous prior between being a meat-person and a chip-person is the complexity of identifying your sensory inputs. I think it is unfounded substrate chauvinism to think that your sensory inputs are less complicated to specify than those of an uploaded copy of yourself.
I
When preferences are selfless, anthropic problems are easily solved by a change of perspective. For example, if we do a Sleeping Beauty experiment for charity, all Sleeping Beauty has to do is follow the strategy that, from the charity's perspective, gets them the most money. This turns out to be an easy problem to solve, because the answer doesn't depend on Sleeping Beauty's subjective perception.
But selfish preferences - like being at a comfortable temperature, eating a candy bar, or going skydiving - are trickier, because they do rely on the agent's subjective experience. This trickiness really shines through when there are actions that can change the number of copies. For recent posts about these sorts of situations, see Pallas' sim game and Jan_Ryzmkowski's tropical paradise. I'm going to propose a model that makes answering these sorts of questions almost as easy as playing for charity.
To quote Jan's problem: