Generalized Information Theory Based on the Theory of Hints

  • Marc Pouly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6717)


The aggregate uncertainty is the only known functional for Dempster-Shafer theory that generalizes the Shannon and Hartley measures and satisfies all classical requirements for uncertainty measures, including subadditivity. Although being posed several times in the literature, it is still an open problem whether the aggregate uncertainty is unique under these properties. This paper derives an uncertainty measure based on the theory of hints and shows its equivalence to the pignistic entropy. It does not satisfy subadditivity, but the viewpoint of hints uncovers a weaker version of subadditivity. On the other hand, the pignistic entropy has some crucial advantages over the aggregate uncertainty. i.e. explicitness of the formula and sensitivity to changes in evidence. We observe that neither of the two measures captures the full uncertainty of hints and propose an extension of the pignistic entropy called hints entropy that satisfies all axiomatic requirements, including subadditivity, while preserving the above advantages over the aggregate uncertainty.


Generalized Information Theory Theory of Hints Dempster-Shafer Theory Pignistic Entropy Hints Entropy 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marc Pouly
    • 1
  1. 1.Interdisciplinary Centre for Security, Reliability and TrustUniversity of LuxembourgLuxembourg

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