Choquet integral and fuzzy measures on locally compact space
Fuzzy Sets and Systems
Regular fuzzy measure and representation of comonotonically additive functional
Fuzzy Sets and Systems
Extension and representation of comonotonically additive functionals
Fuzzy Sets and Systems
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Inner and outer representation by Choquet integral
Fuzzy Sets and Systems
Extreme events and entropy: A multiple quantile utility model
International Journal of Approximate Reasoning
Do Bayesians Learn Their Way Out of Ambiguity?
Decision Analysis
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This paper explains the empirical phenomenon of persistent ''fifty-fifty'' probability judgments through a model of Bayesian updating under ambiguity. To this purpose I characterize an announced probability judgment as a Bayesian estimate given as the solution to a Choquet expected utility maximization problem with respect to a neo-additive capacity that has been updated in accordance with the Generalized Bayesian update rule. Only for the non-generic case, in which this capacity degenerates to an additive probability measure, the agent will learn the event's true probability if the number of i.i.d. data observations gets large. In contrast, for the generic case in which the capacity is not additive, the agent's announced probability judgment becomes a persistent ''fifty-fifty'' probability judgment after finitely many observations.