Method and system for analytic network process (ANP) total influence analysis

• US 8,832,013 B1
• Filed: 11/11/2011
• Issued: 09/09/2014
• Est. Priority Date: 07/24/2009
• Status: Expired due to Fees

• Current Assignee: Decision Lens, Inc.
• Original Assignee: Decision Lens, Inc.
• Inventors: Adams, William James Louis, Saaty, Daniel Lowell
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Traktovenko, Ilya
• Application Number:
  US13/294,369
• Time in Patent Office: 1,033 Days
• Field of Search: 706/46, 706/45, 706/62
• US Class Current: 706/46
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

An apparatus includes an analytic network process (ANP) storage memory that stores an ANP model populated with data, the ANP model having feedback connections in place among nodes within the ANP model, and a processor in communication with the ANP storage memory. The processor is configured to facilitate selecting one or more metrics to use to determine influence of criteria within the ANP model. Also, the processor determines a combined influence score, the combined influence score being a single score for each of the criteria in the ANP model. Also, the processor determines which of the criteria in the ANP model is most influential among the criteria in the ANP model, for the one or more metric which is selected to use to determine the influence of the nodes in the ANP model.

Claims

1. An apparatus comprising:

an analytic network process (ANP) storage memory that stores an ANP weighted supermatrix representing an ANP model populated with data, the ANP model having feedback connections in place among nodes within the ANP model; and

a processor in communication with the ANP storage memory, the processor being configured to facilitate

selecting one or more metrics to use to determine influence of criteria within the ANP model;

determining a combined influence score using the selected one or more metrics, the combined influence score being a single score for each of the criteria throughout the entire ANP weighted supermatrix representing the ANP model that has feedback connections; and

determining, based on the combined influence score for each of the criteria, which of the criteria in the ANP model is most influential among the criteria in the ANP model, for the one or more metric which is selected to use to determine the influence of the nodes in the ANP model.

2. The apparatus of claim 1, wherein the processor is configured to isolate at least one of the metrics and to work on the isolated metrics alone.

3. The apparatus of claim 1, wherein the metrics are one or more of rank change, percent change, and raw change.

4. The apparatus of claim 1, wherein the processor structures the ANP model into a metric first approach, to arrive at the combined influence score based on individual influence scores for the nodes of the ANP model.

5. The apparatus of claim 1, wherein the processor structures the ANP model into an influence-type first approach, to arrive at the combined influence score based on individual influence scores for the nodes in the ANP model.

6. The apparatus of claim 1, wherein the processor is configured to use overarching criteria in a calculation for providing a combined influence score.

7. The apparatus of claim 1, further comprising

an input unit configured to input, from an input device, pairwise comparisons, ANP ratings, or ANP client data, which are stored into the ANP model, the pairwise comparisons representing a judgment of priority between ANP alternatives in the pair, the ANP ratings representing a rating of a choice, and the ANP client data representing real world values.

8. A method, comprising:

storing, in an analytic network process (ANP) storage memory, an ANP weighted supermatrix representing an ANP model populated with data, the ANP model having feedback connections in place among nodes within the ANP model; and

in a processor in communication with the ANP storage memory;

selecting one or more metrics to use to determine influence of criteria within the ANP model;

determining a combined influence score using the selected one or more metrics, the combined influence score being a single score for each of the criteria throughout the entire ANP weighted supermatrix representing the ANP model that has feedback connections; and

determining, based on the combined influence score for each of the criteria, which of the criteria in the ANP model is most influential among the criteria in the ANP model, for the one or more metric which is selected to use to determine the influence of the nodes in the ANP model.

9. The method of claim 8, further comprising isolating at least one of the metrics and working on the isolated metrics alone.

10. The method of claim 8, wherein the metrics are one or more of rank change, percent change, and raw change.

11. The method of claim 8, further comprising structuring the ANP model into a metric first approach, to arrive at the combined influence score based on individual influence scores for the nodes of the ANP model.

12. The method of claim 8, further comprising structuring the ANP model into an influence-type first approach, to arrive at the combined influence score based on individual influence scores for the nodes in the ANP model.

13. The method of claim 8, further comprising using overarching criteria in a calculation for providing a combined influence score.

14. The method of claim 8, further comprising

inputting, from an input device, pairwise comparisons, ANP ratings, or ANP client data, which are stored into the ANP model, the pairwise comparisons representing a judgment of priority between ANP alternatives in the pair, the ANP ratings representing a rating of a choice, and the ANP client data representing real world values.

15. A non-transitory computer-readable storage medium encoded with a computer executable instructions, wherein execution of said computer executable instructions by one or more processors causes a computer to perform the steps of:

storing, in an analytic network process (ANP) storage memory, an ANP weighted supermatrix representing an ANP model populated with data, the ANP model having feedback connections in place among nodes within the ANP model; and

selecting one or more metrics to use to determine influence of criteria within the ANP model;

determining a combined influence score using the selected one or more metrics, the combined influence score being a single score for each of the criteria throughout the entire ANP weighted supermatrix representing the ANP model that has feedback connections; and

determining, based on the combined influence score for each of the criteria, which of the criteria in the ANP model is most influential among the criteria in the ANP model, for the one or more metric which is selected to use to determine the influence of the nodes in the ANP model.

16. The non-transitory computer-readable storage medium of claim 15, further comprising isolating at least one of the metrics and working on the isolated metrics alone.

17. The non-transitory computer-readable storage medium of claim 15, wherein the metrics are one or more of rank change, percent change, and raw change.

18. The non-transitory computer-readable storage medium of claim 15, further comprising structuring the ANP model into a metric first approach, to arrive at the combined influence score based on individual influence scores for the nodes of the ANP model.

19. The non-transitory computer-readable storage medium of claim 15, further comprising structuring the ANP model into an influence-type first approach, to arrive at the combined influence score based on individual influence scores for the nodes in the ANP model.

20. The non-transitory computer-readable storage medium of claim 15, further comprising using overarching criteria in a calculation for providing a combined influence score.

21. The non-transitory computer-readable storage medium of claim 15, further comprising

inputting, from an input device, pairwise comparisons, ANP ratings, or ANP client data, which are stored into the ANP model, the pairwise comparisons representing a judgment of priority between ANP alternatives in the pair, the ANP ratings representing a rating of a choice, and the ANP client data representing real world values.