Descripción
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Processing information is a key issue for decision-making. In most decision-making situations, information is distributed across various sources that are more or less reliable or, more or less accurate. Various sources and kinds of uncertainty are encountered in the same decision situation. "Information imperfections" is a general designation that encompasses all kinds of "deficiencies" (such as uncertainty, imprecision, ambiguity, incompleteness...) that may affect the quality of information at hand. Moreover, decision-making alternatives, options or consequences are often assessed according to heterogeneous and conflicting criteria. Information used to assess these alternatives can also be imperfect. It is rather natural, in such a context, to seek additional information to reduce these imperfections. The question then is how to assess the value of additional information to be acquired in order to improve the quality of existing one. In classical statistical decision analysis (mono-criterion context), the expected value of information is a well-known and used concept where the value of information is assessed regarding sources of uncertainty that are normally considered one at a time. This has not been the case concerning the multi-criterion analysis where several sources of uncertainty in relation to several attributes can be admitted. This stage aims at extending the Bayesian model to multi-criterion analysis in a context of imperfect information. | |
Internacional
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Si |
Lugar
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Ottawa, Canada |
Tipo
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Miembros en el extranjero |
Fecha inicio
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25/07/2013 |
Fecha fin
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04/12/2013 |