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Ambiguity aversion under maximum-likelihood updating

Daniel Heyen ()
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Daniel Heyen: London School of Economics

Theory and Decision, 2018, vol. 84, issue 3, 373-386

Abstract: Abstract Maximum-likelihood updating (MLU) is a well-known approach for extending static ambiguity sensitive preferences to dynamic set-ups. This paper develops an example in which MLU induces an ambiguity averse maxmin expected utility (MEU) decision-maker to (1) prefer a bet on an ambiguous over a risky urn and (2) be more willing to bet on the ambiguous urn compared to an (ambiguity neutral) subjective expected utility (SEU) decision-maker. This is challenging, since prior to observing (symmetric) draws from the urns, the MEU decision-maker (in line with the usual notion of ambiguity aversion) actually preferred the risky over the ambiguous bet and was less willing to bet on the ambiguous urn than the SEU decision-maker. The identified switch in betting preferences is not due to a violation of dynamic consistency or consequentialism. Rather, it results from MLU’s selection of extreme priors, causing a violation of the stability of set inclusion over the course of the updating process.

Keywords: Learning under ambiguity; Maxmin expected utility; Ambiguity aversion; Maximum-likelihood updating; Dynamic decision-making; Belief dynamics (search for similar items in EconPapers)
Date: 2018
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