Dempster-Shafer theory (Dempster[ 3 ], Sharer[ 9 ], [10] ) represents knowledge about events through the use of a generally non-additive set-function, termed a lower probability function (Dempster) or a belief function (Shafer). As shown in [3 ], there exists a wide class of uncertainty situations in which the objective information available naturally takes this form.[ 9 ]emphasizes the usefulness Of belief functions in the describing of subjective judgments. Dempster-Shafer theory is of little interest for decision analysts in the absence of a complementary decision model. This paper proposes that the mode] resulting from the application of von Neumann-Morgenstern linear utility theory to belief functions be adopted in such cases. The terminology and notations of Dempster-Shafer theory as they are presented in this paper are roughly those found in[ 9 ]. Proofs of the main properties of belief functions can be found in[ 3 ], [9 ], or in Chateauneuf and Jaffray[ 2 ]. For linear utility theory we have followed Fishburn [5 ].
[1]
Glenn Shafer,et al.
A Mathematical Theory of Evidence
,
2020,
A Mathematical Theory of Evidence.
[2]
J. Milnor,et al.
AN AXIOMATIC APPROACH TO MEASURABLE UTILITY
,
1953
.
[3]
J. Neumann,et al.
Theory of games and economic behavior
,
1945,
100 Years of Math Milestones.
[4]
Abraham Wald,et al.
Statistical Decision Functions
,
1951
.
[5]
Niels-Erik Jensen.
An Introduction to Bernoullian Utility Theory: I. Utility Functions
,
1967
.
[6]
D. Ellsberg.
Decision, probability, and utility: Risk, ambiguity, and the Savage axioms
,
1961
.
[7]
Jean-Yves Jaffray,et al.
Approximations of rational criteria under complete ignorance and the independence axiom
,
1983
.