A Bayesian approach to uncertainty aversion

Yoram Halevy*, Vincent Feltkamp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

The Ellsberg paradox demonstrates that people's beliefs over uncertain events might not be representable by subjective probability. We show that if a risk averse decision maker, who has a well defined Bayesian prior, perceives an Ellsberg type decision problem as possibly composed of a bundle of several positively correlated problems, she will be uncertainty averse. We generalize this argument and derive sufficient conditions for uncertainty aversion.

Original languageAmerican English
Pages (from-to)449-466
Number of pages18
JournalReview of Economic Studies
Volume72
Issue number2
DOIs
StatePublished - Apr 2005
Externally publishedYes

Bibliographical note

Funding Information:
Kenneth Hendricks, Peter Klibanoff, George Mailath, Mark Machina, Steven Matthews, Stephen Morris, Nicola Persico, Andrew Postlewaite, Rafi Rob, Ariel Rubinstein, David Schmeidler, Hans Schneeweiss, Uzi Segal, Dan Silverman, Becky Stein, Guofu Tan, Peter Wakker and John Weymark. We also thank seminar participants at the University of Pennsylvania, Cornell, Duke, University of British Columbia, Hebrew University, Tel Aviv University, Haifa University, Ben Gurion University, Workshop on “New Themes in Decision Theory Under Uncertainty” held in Paris on June 1999, Université de Montréal, The Canadian Economic Theory Conference held at UBC on June 2000, UC-Berkeley and Stanford University. Yoram Halevy thanks the Department of Economics at the University of Pennsylvania for its hospitality while working on this paper. This paper was written during Vincent Feltkamp’s stay at Departmento de Fundamentos del Análisis Económico, Alicante University, Spain. Funding from the TMR project ERB FMRX CT96 0055 and project UPV-93632/HA078/97 which made his stay at the University of Basque Country possible is gratefully acknowledged by the second author.

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