International Journal of General Systems 739 shortcomings have been presented in publications by Klir et al. and can be expressed in the following way 1. Computing complexity 2. Concealment of the two types of uncertainty coexisting in the evidence theory conflict and non-specificity 3. Insensitivity to changes in evidence We consider Klir et al.'s considerations about the behaviour of a total uncertainty measure (TU) in DST to be very important, because a TU in DST makes no sense if it verifies all the basic properties(P1-P6)but its calculation is unfeasible. A TU in DST 二Ez∞N一 should also give us information about the quantification of the two types of uncertainty coexisting in DST. Finally, a TU should be sensitive to changes in evidence directly or via its parts of conflict or non-specificity since it is possible for an increase in conflict to cause a decrease in non-specificity, and vice versa, and we could have two situations with similar total uncertainty values but with different conflict and non-specificity parts of the ncertainty This set of shortcomings found in certain total uncertainty measures could therefore be used to present a set of requirements of behaviour of a TU in DST. P1-P6 can be considered as requirements of properties that a total uncertainty measure in DST must satisfy. The set of requirements of behaviour(RB)of a tU in DST could be expressed in the following way (RB1)The calculation of a TU should not be too complex. (RB2)A TU must not conceal the two types of uncertainty(conflict and non-specificity) co-existing in the evidence theory (RB3)A TU must be sensitive to changes in evidence either directly or via its parts of conflict and non-specificity There are certain situations where the information available is more suitable for being mathematically quantified with more general models than the dsT. In such cases, we are talking about a Generalised Information Theory'[see Klir(2006)] and take into account Klir's following principle of Klir Principle of uncertainty invariance: the s. the pr (and information) must be uncertain theory is transformed into its counterpart in another theory. That is, the princi es that no information is unwittingly added or eliminated solely by changing the mathematical framework by which a articular phenomenon is formalised. By this principle, a TU in dSt should allow us to extend it on more general theories than DST. This one could be considered as a requirement of behaviour for a tU in DSt, that we can call extensibility RB4) The extension of a TU in DST on more general theories must be possible We will now review the above require RBI: As we can see in Jousselme et al.(2006), the AM function has a simpler lculation than the other functions(MI includes S in its definition), and it is only necessary to obtain the pignistic probability distribution of a b p a. The calculation of S="in DST has a high computational complexity. Meyerot al's algorithm(1994)was the first to obtain this value. More recently, the computation of this algorithm was reduced byshortcomings have been presented in publications by Klir et al. and can be expressed in the following way: 1. Computing complexity. 2. Concealment of the two types of uncertainty coexisting in the evidence theory: conflict and non-specificity. 3. Insensitivity to changes in evidence. We consider Klir et al.’s considerations about the behaviour of a total uncertainty measure (TU) in DST to be very important, because a TU in DST makes no sense if it verifies all the basic properties (P1–P6) but its calculation is unfeasible. A TU in DST should also give us information about the quantification of the two types of uncertainty coexisting in DST. Finally, a TU should be sensitive to changes in evidence directly or via its parts of conflict or non-specificity since it is possible for an increase in conflict to cause a decrease in non-specificity, and vice versa, and we could have two situations with similar total uncertainty values but with different conflict and non-specificity parts of the uncertainty. This set of shortcomings found in certain total uncertainty measures could therefore be used to present a set of requirements of behaviour of a TU in DST. P1–P6 can be considered as requirements of properties that a total uncertainty measure in DST must satisfy. The set of requirements of behaviour (RB) of a TU in DST could be expressed in the following way: (RB1) The calculation of a TU should not be too complex. (RB2) A TU must not conceal the two types of uncertainty (conflict and non-specificity) co-existing in the evidence theory. (RB3) A TU must be sensitive to changes in evidence either directly or via its parts of conflict and non-specificity. There are certain situations where the information available is more suitable for being mathematically quantified with more general models than the DST. In such cases, we are talking about a ‘Generalised Information Theory’ [see Klir (2006)] and take into account Klir’s following principle of Klir: Principle of uncertainty invariance: the amount of uncertainty (and information) must be preserved when a representation of uncertainty in one mathematical theory is transformed into its counterpart in another theory. That is, the principle guarantees that no information is unwittingly added or eliminated solely by changing the mathematical framework by which a particular phenomenon is formalised. By this principle, a TU in DST should allow us to extend it on more general theories than DST. This one could be considered as a requirement of behaviour for a TU in DST, that we can call extensibility: (RB4) The extension of a TU in DST on more general theories must be possible. We will now review the above requirements of behaviour for functions MI, S* and AM: RB1: As we can see in Jousselme et al. (2006), the AM function has a simpler calculation than the other functions (MI includes S * in its definition), and it is only necessary to obtain the pignistic probability distribution of a b.p.a. The calculation of S* in DST has a high computational complexity. Meyerowitz et al.’s algorithm (1994) was the first to obtain this value. More recently, the computation of this algorithm was reduced by International Journal of General Systems 739 Downloaded by [New York University] at 12:09 08 November 2011