Well, rather then modelling the price at time t by exp(B t) were B is a Brownian motion, you could model it by exp(B t—c*t) for some constant c.
On a more basic level instead of saying that after a day the price will be multiplied by either 0.9 or 1.111 with equal probability, you could have the price multiplied by either 0.9 or 1.1 with equal probability. In the later case, the expected value tomorrow is exactly the value today. On the other hand, because 0.9*1.1 < 1, this later process will end up at 0 in the long run almost surely. Then again, this model would probably only work as a first order approximation to the behavior anyway. If bitcoin ever does manage to become a competitive major currency, its volatility would almost have to decrease drastically.
So what happens when AIXI determines that there’s this large computer, call it BRAIN whose outputs tend to exactly correlate with its outputs? AIXI may then discover the hypothesis that the observed effects of AIXI’s outputs on the world are really caused by BRAIN’s outputs. It may attempt to test this hypothesis by making some trivial modification to BRAIN so that it’s outputs differ from AIXI’s at some inconsequential time (not by dropping an anvil on BRAIN, because this would be very costly if the hypothesis is true). After verifying this, AIXI may then determine that various hardware improvements to BRAIN will cause its outputs to more closely match the theoretical Solomonoff Inductor, thus improving AIXI’s long term payoff.
I mean, AIXI is waaaay too complicated for me to actually properly predict, but is this scenario actually so unreasonable?