A non-transitory computer readable medium for storing one or more sequences of one or more instructions for execution by one or more processors in a processing system to perform a method for determining a dichotomy for a parametric decision process, the instructions when executed by the one or more processors are presented. Embodiments can be configured to define a network having a plurality of members as inter-member coherency coupling represented by elements of a coherency matrix, wherein for each ith member the network includes an intensity value, Ii. Further embodiments may include determining non-diagonal elements Tij of the coherency matrix in which i≠j and in which i and j represent ith and jth members of the network, and constructing diagonal kernels Ki or non-diagonal kernels Hi based on the non-diagonal elements Tij of the coherency matrix and intensity values (Ii) of the members.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer readable medium storing one or more sequences of one or more instructions for execution by one or more processors in a processing system to perform a method for determining a dichotomy for a parametric decision process, the instructions when executed by the one or more processors, cause the one or more processors to perform the operations of: defining a network having a plurality of members an inter-member coherency coupling represented by elements of a coherency matrix, wherein for each ith member the network includes an intensity value, Ii; determining non-diagonal elements Tij of the coherency matrix in which i≠j and in which i and j represent ith and jth members of the network, respectively; constructing diagonal kernels Ki or non-diagonal kernels Hi based on the non-diagonal elements Tij of the coherency matrix and intensity values (Ii) of the members. 2. The non-transitory computer readable medium of claim 1, wherein for a given ith member, defining a diagonal kernel vector as a function of its diagonal kernel vector Ki and a unit vector as: {right arrow over (K)}i=·Ki. 3. The non-transitory computer readable medium of claim 2, wherein {circumflex over (k)}i is given by: {circumflex over (k)}i=bxêx+byêy+bzêz in which |êx|=|êy|=|êz|=1; êx·êy=0; êx·êz=0; êy·êz=0 and bx2+by2+bz2=1. 4. The non-transitory computer readable medium of claim 2, wherein for the given ith member, defining a parametric decision vector, {right arrow over (Si)}, as a function of parametric decision scalar Si, and unit vector, , as: {right arrow over (S)}i=·Si. 5. The non-transitory computer readable medium of claim 2, wherein is given by: ŝi=axêx+ayêy+azêz in which |êx|=|êy|=|êz|=1; êx·êy=0; êx·êz=0; êy·êz=0 and ax2+ay2+az2=1. 6. The non-transitory computer readable medium of claim 4, wherein the operation further comprises determining a moral skew factor for the ith network member as cos θi in which {right arrow over (Si)}·{right arrow over (Ki)}=SiKi cos θi. 7. The non-transitory computer readable medium of claim 1, wherein wherein the operations further comprise calculating a strength of diagonal kernels Ki as: K i = ∑ j = 1 N  T ij  I i  I j . In which Ij is the intensity value of the jth member of the network. 8. The non-transitory computer readable medium of claim 1, wherein the operations further comprise calculating a decision weighted mean, <S>, based on diagonal kernels Ki as: 〈 S 〉 = ∑ i = 1 N  S i  K i ∑ i = 1 N  K i . 9. The non-transitory computer readable medium of claim 1, wherein the operations further comprise calculating an ith-weight as w i = K i ∑ i = 1 N  K i ; 0 ≤ w i ≤ 1 , and   ∑ i = 1 N  w i = 1. 10. The non-transitory computer readable medium of claim 1, wherein the members comprise an individual, a high-value-individual candidate, or a group of interest. 11. The non-transitory computer readable medium of claim 1, wherein matrix elements are non-symmetrical, such that Tij≠Tji. 12. The non-transitory computer readable medium of claim 1, wherein Tij is defined as: Tii=1, and Tij≦1. 13. A non-transitory computer readable medium storing one or more sequences of one or more instructions for execution by one or more processors in a processing system to perform a method for determining a moral skew factor for a parametric decision process, the instructions when executed by the one or more processors, cause the one or more processors to perform the operations of: defining a network having a plurality of members in which each member comprises an intra-ego influence including a first unit vector, {circumflex over (k)}, and an inter-ego influence including a second unit vector, ŝ, representing a member strength, constructing first and second kernel vectors parallel to said first and second unit vectors, respectively; computing an angle, θ, between the first and second kernel vectors; and determining the moral skew factor as cos(θ). 14. The non-transitory computer readable medium of claim 13, further comprising determining an inter-member coherency coupling represented by elements of a coherency matrix, wherein each ith member the network includes an intensity value, Ii. 15. A non-transitory computer readable medium storing one or more sequences of one or more instructions for execution by one or more processors in a processing system to perform a method for determining a moral skew factor for a parametric decision process, the instructions when executed by the one or more processors, cause the one or more processors to perform the operations of: defining a network having a plurality of members an inter-member coherency coupling represented by elements of a coherency matrix, wherein for each ith member the network includes an intensity value, Ii; determining non-diagonal elements Rij of the coherency matrix in which i≠j and in which i and j represent ith and jth members of the network, respectively; determining a non-diagonal pseudo-vector, {right arrow over (G)}i, for the ith member; determining a parametric decision vector, {right arrow over (S)}i, for the ith member; computing an angle, θ, between the non-diagonal pseudo-vector and parametric decision vector; and determining the moral skew factor as cos(θ). 16. The method of claim 15, wherein the operations further comprise determining a projection of non-diagonal pseudo-vector, {right arrow over (G)}i, onto parametric decision vector, {right arrow over (S)}i, and computing a non-normalized weight of the parametric decision vector as a sum of the projection and the intensity Ii for that member. 17. The method of claim 16, wherein the operations further comprise determining whether a member is a high-value member based on the moral skew factor and the non-normalized weight.