Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

Eran Hanany; Peter Klibanoff; Erez Marom

doi:https://doi.org/10.1016/j.ijar.2011.06.010

Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

Eran Hanany, Peter Klibanoff, Erez Marom

Department of Industrial Engineering

Tel Aviv University

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

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
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

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

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https://doi.org/10.1016/j.ijar.2011.06.010

Cite this

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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.",

year = "2011",

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doi = "https://doi.org/10.1016/j.ijar.2011.06.010",

language = "الإنجليزيّة",

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T1 - Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

AU - Hanany, Eran

AU - Klibanoff, Peter

AU - Marom, Erez

N1 - 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.

PY - 2011/11

Y1 - 2011/11

N2 - 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.

AB - 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.

KW - Ambiguity

KW - Risk analysis

KW - Uncertainty modeling

KW - Updating beliefs

KW - Utility theory

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U2 - https://doi.org/10.1016/j.ijar.2011.06.010

DO - https://doi.org/10.1016/j.ijar.2011.06.010

M3 - مقالة

SN - 0888-613X

VL - 52

SP - 1198

EP - 1214

JO - International Journal of Approximate Reasoning

JF - International Journal of Approximate Reasoning

IS - 8

ER -

Dynamically consistent updating of multiple prior beliefs - An algorithmic approach

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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