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Dynamically Consistent Updating of Multiple Prior Beliefs: An Algorithmic Approach, International Journal of Approximate Reasoning

Abstract

This paper develops algorithms for dynamically consistent updating of ambiguous beliefs in the maxmin expected utility model of decision making under ambiguity. Dynamic consistency is the requirement that ex-ante contingent choices are respected by updated preferences. Such updating, in this context, implies dependence on the feasible set of payoff vectors available in the problem and/or on an ex-ante optimal act for the problem. Despite this complication, the algorithms are formulated concisely and are easy to implement, thus making dynamically consistent updating operational in the presence of ambiguity.

Type

Article

Author(s)

Eran Hanany, Peter Klibanoff, Erez Marom

Date Published

2011

Citations

Hanany, Eran, Peter Klibanoff, and Erez Marom. 2011. Dynamically Consistent Updating of Multiple Prior Beliefs: An Algorithmic Approach. International Journal of Approximate Reasoning. 52(8): 1198-1214.

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