Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

Eran Hanany; Peter Klibanoff; Erez Marom

doi:10.1016/j.ijar.2011.06.010

Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

Eran Hanany^*, Peter Klibanoff, Erez Marom

^*Corresponding author for this work

Managerial Economics, Decision Sciences and Operations

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1198-1214
Number of pages	17
Journal	International Journal of Approximate Reasoning
Volume	52
Issue number	8
DOIs	https://doi.org/10.1016/j.ijar.2011.06.010
State	Published - Nov 2011

Keywords

Ambiguity
Risk analysis
Uncertainty modeling
Updating beliefs
Utility theory

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

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title = "Dynamically consistent updating of multiple prior beliefs - An algorithmic approach",

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.",

keywords = "Ambiguity, Risk analysis, Uncertainty modeling, Updating beliefs, Utility theory",

author = "Eran Hanany and Peter Klibanoff and Erez Marom",

note = "Funding Information: We thank Yigal Gerchak, Tal Raviv, two anonymous referees, the area editor and Thierry Denoeux, the Editor-in-Chief, for helpful discussions and comments. This research was partially supported by Grant No. 2006264 from the United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel. All responsibility for the contents of the paper lies solely with the authors.",

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AU - Marom, Erez

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