show that ambiguity-averse decision functionals matched with the multiple-prior learning model are more robust to model misspecification than the standard expected utility with Bayesian learning. However, these criteria may fail to deliver robust decisions because the multiple-prior learning model inherits the same fragility of Bayesian learning. There are misspecified learning problems in which an ambiguity-averse DM optimally chooses a sequence of ambiguous acts over a sequence of risky acts that would deliver a strictly higher average utility
markdownabstractWe develop a tractable method to estimate multiple prior models of decision-making u...
the prior support is finite, long-run ambiguity is known to be a possible outcome only if the learni...
Popular models for decision making under ambiguity assume that people use not one but multiple prior...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
This paper considers learning when the distinction between risk and ambigu-ity (Knightian uncertaint...
This paper considers learning when the distinction between risk and ambigu-ity (Knightian uncertaint...
This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty...
We develop a tractable method to estimate multiple prior models of decisionmaking under ambiguity. ...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
The existing models of Bayesian learning with multiple priors by Marinacci (Stat Pap 43:145–151, 200...
Contains fulltext : 162256pre.pdf (preprint version ) (Open Access) ...
markdownabstractWe develop a tractable method to estimate multiple prior models of decision-making u...
the prior support is finite, long-run ambiguity is known to be a possible outcome only if the learni...
Popular models for decision making under ambiguity assume that people use not one but multiple prior...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one pr...
This paper considers learning when the distinction between risk and ambigu-ity (Knightian uncertaint...
This paper considers learning when the distinction between risk and ambigu-ity (Knightian uncertaint...
This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty...
We develop a tractable method to estimate multiple prior models of decisionmaking under ambiguity. ...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
Over the past two decades, the growing literature on ambiguity aversion has shed light on a number o...
The existing models of Bayesian learning with multiple priors by Marinacci (Stat Pap 43:145–151, 200...
Contains fulltext : 162256pre.pdf (preprint version ) (Open Access) ...
markdownabstractWe develop a tractable method to estimate multiple prior models of decision-making u...
the prior support is finite, long-run ambiguity is known to be a possible outcome only if the learni...
Popular models for decision making under ambiguity assume that people use not one but multiple prior...