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Inertial Updating with General Information
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Inertial Updating with General Information
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We study belief revision when information is represented by a set of probability distributions, or general information. General information extends the standard event notion while including qualitative information (A is more likely than B), interval information (A has a ten-to-twenty percent chance), and more. We behaviorally characterize Inertial Updating: the decision maker's posterior is of minimal subjective distance from her prior, given the information constraint. Further, we introduce and characterize a notion of Bayesian updating for general information and show that Bayesian agents may disagree. We also behaviorally characterize f-divergences, the class of distances consistent with Bayesian updating.
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Cited by 1 Pith paper
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Learning from an Unknown DGP: Experimental Evidence on Belief Updating with AI Recommendations
With qualitative AI advice and an unknown DGP, belief updates show confirmation inertia at extremes, large reaction to contradiction, and weaker intermediate moves, better fit by CR/wIU than quasi-Bayes.
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