BusinessDecision SciencesDecision AnalysisDecision TheoryProbability TheoryBayesian Decision TheoryChoice Under UncertaintyDecision Under AmbiguityDecision Under UncertaintyDecision-making Under UncertaintyUtility TheoryConsistencyDecision Analysis Under AmbiguityDecision Rules Under AmbiguityDecision Rules Under UncertaintyDecision Theory Under UncertaintyMultiple Priors ModelPreference ConsistencyPreference Under Uncertainty
We propose and axiomatically characterize dynamically consistent update rules for decision making under ambiguity. These rules apply to the preferences with multiple priors of Gilboa and Schmeidler (1989), and are the first, for any model of preferences over acts, to be able to reconcile typical behavior in the face of ambiguity (as exemplified by Ellsberg’s paradox) with dynamic consistency for all non-null events. Updating takes the form of applying Bayes’ rule to subsets of the set of priors, where the specific subset depends on the preferences, the conditioning event, and the choice problem (i.e., a feasible set of acts together with an act chosen from that set).
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A Smooth Model of Decision Making under Ambiguity
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