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Decision Making in a Context where Uncertainty is Represented by Belief Functions

  • Philippe Smets
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 88)

Abstract

A quantified model to represent uncertainty is incomplete if its use in a decision environment is not explained. When belief functions were first introduced to represent quantified uncertainty, no associated decision model was proposed. Since then, it became clear that the belief functions meaning is multiple. The models based on belief functions could be understood as an upper and lower probabilities model, as the hint model, as the transferable belief model and as a probability model extended to modal propositions. These models are mathematically identical at the static level, their behaviors diverge at their dynamic level (under conditioning and/or revision). For decision making, some authors defend that decisions must be based on expected utilities, in which case a probability function must be determined. When uncertainty is represented by belief functions, the choice of the appropriate probability function must be explained and justified. This probability function does not represent a state of belief, it is just the additive measure needed to compute the expected utilities. Other models of decision making when beliefs are represented by belief functions have also been suggested, some of which are discussed here.

Keywords

Probability Function Actual World Basic Belief Belief Function Prob Ability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Philippe Smets
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBruxellesBelgium

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