 Belief revision and learning theory Belief revision in repeated games bears natural resemblance to limit learning of  formal learning theory  (Kelly 1996). Baltag, Gierasimczuk, and Smets (2011) analyze learning in terms of initial epistemic-doxastic models over which finite histories of signals trigger the learner to revise beliefs, represented as changes in epistemic accessibility or plausibility order. It turns out that both iterated public announcement and iterated radical upgrade as discussed above are universal learning methods, though only the latter maintains this property in the presence of (finitely many) errors in the input stream.