Approaches to decision-making under uncertainty in the belief function framework are
reviewed. Most methods are shown to blend criteria for decision under ignorance with the …

262 Lev V. Utkin and Frank PA Coolen sources. Some of them may be objective measures
based on relative frequencies or on well-established statistical models. A part of the …

Various ways for decision making with imprecise probabilities—admissibility, maximal
expected utility, maximality, E-admissibility, Γ-maximax, Γ-maximin, all of which are well …

Практически во всех прикладных областÿх экономики и техники принÿтие оптимальных
решений и анализ риска ÿвлÿютсÿ одними из важнейших этапов реализации проектов …

We introduce three different approaches for decision making under uncertainty, if (I) there is
only partial (both cardinal and ordinal) information on an agent's preferences and (II) the …

This paper analyzes concepts of independence and assumptions of convexity in the theory
of sets of probability distributions. The starting point is Kyburg and Pittarelli's discussion of …

Robust statistics is concerned with statistical methods that still lead to reliable conclusions if
an ideal model is only approximately true. More recently, the theory of imprecise …

This paper presents new insights and novel algorithms for strategy selection in sequential
decision making with partially ordered preferences; that is, where some strategies may be …

We present a new approach for constructing regression and classification models for interval-
valued data. The risk functional is considered under a set of probability distributions …

The quality of consequences in a decision making problem under (severe) uncertainty must
often be compared among different targets (goals, objectives) simultaneously. In addition …