Pseudo-Bayesian updating

I propose an axiomatic framework for belief revision when new information is qualitative, of the form ``Event $A$ is at least as likely as event $B$.'' My decision maker need not have beliefs about the joint distribution of the signal she will receive and the payoff-relevant states. I propose three axioms, Exchangeability, Stationarity, and Reduction, to characterize the class of pseudo-Bayesian updating rules. The key axiom, Exchangeability, requires that the order in which the information arrives does not matter if the different pieces of information neither reinforce nor contradict each other. I show that adding one more axiom, Conservatism, which requires that the decision maker adjust her beliefs just enough to embrace new information, yields Kullback-Leibler minimization: The decision maker selects the posterior closest to her prior in terms of Kullback-Leibler divergence from the probability measures consistent with newly received information. I show that pseudo-Bayesian agents are susceptible to recency bias, which may be mitigated by repetitive learning.