The conclusion follows (I think) because the Solomonoff agent is computing the posterior probability of all algorithms, including the one that implements the same computation the human implements. So after updating, the Solomonoff agent’s posterior probability for that algorithm should be higher than that of any other algorithm, and it draws the same conclusion the human does.
You lost a level of indirection in there; computing the output of an algorithm does not mean believing that the output of that algorithm is true or even plausible. So the agent will correctly predict what the human will say, and believe that the human is mistaken.
The level of indirection isn’t necessary: the Solomonoff agent’s distribution is a weighted mixture of the outputs of all possible Turing machines, weighted according to the posterior probability of that Turing machine being the one that is generating the observations. Any Turing machine that predicts that the putative halting oracle gets one wrong on a particular trial gets downweighted to zero when that fails to occur.
You lost a level of indirection in there; computing the output of an algorithm does not mean believing that the output of that algorithm is true or even plausible. So the agent will correctly predict what the human will say, and believe that the human is mistaken.
The level of indirection isn’t necessary: the Solomonoff agent’s distribution is a weighted mixture of the outputs of all possible Turing machines, weighted according to the posterior probability of that Turing machine being the one that is generating the observations. Any Turing machine that predicts that the putative halting oracle gets one wrong on a particular trial gets downweighted to zero when that fails to occur.