Reasons and Means to Model Preferences as Incomplete

Cailloux, Olivier; Destercke, Sébastien

doi:10.1007/978-3-319-67582-4_2

Cited by 1 publication

(2 citation statements)

References 42 publications

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“…The decision maker can then provide some assessment of how certain she/he is about this information by providing a value α. For instance, if the DM is certain to choose a student with grades (0, 8, 5) over one with grades (8,4,5), then α should be close to 1. Yet if the DM is quite uncertain about this choice, then α should be closer to 0.…”

Section: The Basic Schemementioning

confidence: 99%

“…This would require at least two things: consider incomparability not as a lack of knowledge about preferences, but as a knowledge of lack of preferences, and to build up elicitation protocol allowing for incomparability. Although quite interesting, we will not consider this aspects here, given that it would be a research topic in itself (some elements can be found in [15,8]). Some noteworthy properties of Equations (5)-(6) are the following:…”

Section: 3mentioning

confidence: 99%

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A generic framework to include belief functions in preference handling and multi-criteria decision

Destercke

2018

International Journal of Approximate Reasoning

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Modelling the preferences of a decision maker about alternatives having multiple criteria usually starts by collecting preference information (comparisons of alternatives, importance of criteria, . . . ), which are then used to fit a preference model issued from some set of hypothesis (weighted average, CP-net, lexicographic orderings, . . . ). In practice, this process may often lead to inconsistencies that may be due to inaccurate information provided by the decision maker, who can be unsure about the provided information, or to a poor choice of hypothesis set, which can be too restrictive or not well adapted to the decision process. In this paper, we propose to use belief functions as a way to quantify and resolve such inconsistencies, notably by allowing the decision maker to express her/his certainty about the provided preferential information. Our framework is generic, in the sense that it does not assume a given set of hypothesis a priori, and is consistent with precise methods, in the sense that in the absence of uncertainty and inconsistencies in the information, precise models are ultimately retrieved.

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Section: The Basic Schemementioning

confidence: 99%

Section: 3mentioning

confidence: 99%