4.6 Article

Dual-Self Representations of Ambiguity Preferences

Journal

ECONOMETRICA
Volume 90, Issue 3, Pages 1029-1061

Publisher

WILEY
DOI: 10.3982/ECTA17502

Keywords

Ambiguity; multiple priors; dual-self models; belief updating

Funding

  1. NSF [SES-1824324]
  2. Deutsche Forschungsgemeinschaft [CRC TRR 190]

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We propose a model of multiple-prior representations of preferences, where decision-makers evaluate uncertain prospects based on beliefs that are the outcome of a game between Pessimism and Optimism. The model allows for different degrees of ambiguity aversion and can capture the co-existence of negative and positive ambiguity attitudes within individuals. The baseline representation, dual-self expected utility, provides a new representation of the class of invariant biseparable preferences, while extensions of this representation allow for more general departures from independence. We also provide foundations for a generalization of prior-by-prior belief updating to our model.
We propose a class of multiple-prior representations of preferences under ambiguity, where the belief the decision-maker (DM) uses to evaluate an uncertain prospect is the outcome of a game played by two conflicting forces, Pessimism and Optimism. The model does not restrict the sign of the DM's ambiguity attitude, and we show that it provides a unified framework through which to characterize different degrees of ambiguity aversion, and to represent the co-existence of negative and positive ambiguity attitudes within individuals as documented in experiments. We prove that our baseline representation, dual-self expected utility (DSEU), yields a novel representation of the class of invariant biseparable preferences (Ghirardato, Maccheroni, and Marinacci (2004)), which drops uncertainty aversion from maxmin expected utility (Gilboa and Schmeidler (1989)), while extensions of DSEU allow for more general departures from independence. We also provide foundations for a generalization of prior-by-prior belief updating to our model.

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