1. A computer implemented multiple criteria decision analysis method in which a plurality of L basic criteria of one or more entities are assessed so as to generate an assessment of the one or more entities in which the assessments of each entity on the plurality of L basic criteria expressed as {(H n β n,i ), n=1,. .. , N} for i={1,. .. , L} are aggregated in order to generate an overall assessment for the entity on a general criterion, the method comprising the steps of: i) assigning weights W i (i=1,. .. , L) to each of the L basic criteria according to a relative importance determined by a decision maker of the basic criteria to the general criterion; ii) calculating by a computer a set of normalized weights ω i using the following equation: ω i = W i ∑ j = 1 L ⁢ ⁢ W j ⁢ ( i = 1 , … ⁢ ⁢ L ) ; iii) calculating by the computer weighted degrees of belief defining basic probability mass m n,i , using the following equation: 
 m n,i =ω i β n,i , ( n =1 ,. .. , N; i =1 ,. .. , L ), where β n,i is the degree to which the i th basic criterion is assessed to H n , H n is the n th grade for assessment of the general criterion, the general criterion being assessed to N grades, and m n,i represents the degree to which the i th basic criterion supports a hypothesis that the general criterion is assessed to the n th grade of H n ; iv) calculating by the computer a remaining probability mass m H,i using the following equation: m H , i = 1 - ∑ n = 1 N ⁢ ⁢ m n , i , ( i = 1 , … ⁢ , L ) ; v) displaying on a computer-generated graphical user interface comparative results of the overall assessment to thereby enhance entity evaluation; vi) decomposing m H,i into m H,i and {tilde over (m)} H,i , wherein: m H , i = m _ H , i + m ~ H , i , ⁢ m _ H , i = 1 - ω i , and m ~ H , i = ω i ⁡ ( 1 - ∑ n = 1 N ⁢ ⁢ β n , i ) ⁢ ⁢ for ⁢ ⁢ i = 1 , … ⁢ , L ; vii) aggregating m n,i , m H,i and {tilde over (m)} H,i (i=1,. .. , L) into combined probability masses I n,L ,Ī H,L and Ĩ H,L , respectively, using the following equations 1) to 9) in a recursive manner, where: I n , 1 = m n , 1 - ( n = 1 , 2 , … ⁢ , N ) , 1 ) I H , 1 = m H , 1 , 2 ) I ~ H , 1 = m ~ H , 1 , 3 ) I _ H , 1 = m _ H , 1 , 4 ) K i + 1 = [ 1 - ∑ t = 1 N ⁢ ⁢ ∑ j = 1 N j ≠ t ⁢ ⁢ I t , i ⁢ m j , i + 1 ] - 1 , 5 ) I n , i + 1 = K i + 1 [ I n , i ⁢ m n , i + 1 + I H , i ⁢ m n , i + 1 + I n , i ⁢ m H , i + 1 ] ⁢ ( n = 1 , 2 , … ⁢ , N ) , 6 ) I ~ H , i + 1 = K i + 1 ⁡ [ I ~ H , i ⁢ m ~ H , i + 1 + I _ H , i ⁢ m ~ H , i + 1 + I ~ H , i ⁢ m _ H , i + 1 ] , 7 ) I _ H , i + 1 = K i + 1 ⁡ [ I _ H , i ⁢ m _ H , i + 1 ] , and 8 ) I H , i + 1 = I _ H , i + 1 + I ~ H , i + 1 , ⁢ i = { 1 , 2 , … ⁢ , L - 1 } ; 9 ) viii) generating combined degrees of belief β n and β H using the equations: β n = I n , L 1 - I _ H , L ⁢ n = 1 , 2 , … ⁢ , N , and β H = I ~ H , L 1 - I _ H , L , where β n is a degree of belief to which the general criterion is assessed to the n th grade H n , and β H is a remaining degree of belief which is not assigned to any specific grade; and ix) generating an overall assessment for each entity a on the general criterion represented as: s(a)={(H 1 ,β 1 (a)),. .. , (H n ,β n (a)),. .. , (H N ,β N (a)), (H H ,β H (a))}.