While the model is interesting, it is almost irremediably ruined by this line: "since by definition P(A) = Ta/n", which substantially conflates probability with frequency. From this point of view, the conclusion:

It is clear from Pearl's work that probability theory provides normatively correct rules which an AI system must use to reason optimally about uncertain events. It is equally clear that AI systems (like all other physical systems) are unavoid- ably subject to a certain degree of random variation and noise in their internal workings. As we have seen, this random variation does not produce a pattern of reasoning in which probability estimates vary randomly around the correct value; instead,it produces systematic biases that push probability estimates in certain directions and so will produce conservatism, subadditivity, and the conjunction and disjunction errors in AI reasoning.

does not follow (because the estimation is made by the prior and not by counting).
BUT the issue of noise in AI is interesting per se: if we a have stable self-improving friendly AI, could it faultily copies/update itself into an unfriendly version?