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Axioms 2014, 3(1), 31-45; doi:10.3390/axioms3010031

Second-Order Risk Constraints in Decision Analysis

1,* , 1
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Received: 8 November 2013; in revised form: 24 December 2013 / Accepted: 27 December 2013 / Published: 17 January 2014
(This article belongs to the Special Issue Axioms of Decision Support System)
Download PDF [301 KB, uploaded 17 January 2014]
Abstract: Recently, representations and methods aimed at analysing decision problems where probabilities and values (utilities) are associated with distributions over them (second-order representations) have been suggested. In this paper we present an approach to how imprecise information can be modelled by means of second-order distributions and how a risk evaluation process can be elaborated by integrating procedures for numerically imprecise probabilities and utilities. We discuss some shortcomings of the use of the principle of maximising the expected utility and of utility theory in general, and offer remedies by the introduction of supplementary decision rules based on a concept of risk constraints taking advantage of second-order distributions.
Keywords: decision analysis; second-order information; risk analysis; risk constraints decision analysis; second-order information; risk analysis; risk constraints
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Ekenberg, L.; Danielson, M.; Larsson, A.; Sundgren, D. Second-Order Risk Constraints in Decision Analysis. Axioms 2014, 3, 31-45.

AMA Style

Ekenberg L, Danielson M, Larsson A, Sundgren D. Second-Order Risk Constraints in Decision Analysis. Axioms. 2014; 3(1):31-45.

Chicago/Turabian Style

Ekenberg, Love; Danielson, Mats; Larsson, Aron; Sundgren, David. 2014. "Second-Order Risk Constraints in Decision Analysis." Axioms 3, no. 1: 31-45.

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