PROVIDING EXPERT ELICITATION

• US 20140019394A1
• Filed: 07/12/2012
• Published: 01/16/2014
• Est. Priority Date: 07/12/2012
• Status: Abandoned Application

• Current Assignee: Center For Disease Dynamics, Economics & Policy, Inc.
• Original Assignee: Center For Disease Dynamics, Economics & Policy, Inc.
• Inventors: LAXMINARAYAN, Ramanan, Cooke, Roger, Colson, Abigail, Lenoir, Griffin, Megiddo, Itamar
• Application Number:
  US13/547,634
• Publication Number:
  US 20140019394A1
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services

Abstract

Systems and methods of providing expert elicitation are provided. Expert information may be stored in an expert database. A request for expert opinion may be received. A plurality of experts may be selected. A plurality of seed questions and target questions may be generated and sent to the experts selected. Answers to the questions may be received. A performance-based weight may be assigned to each expert based on the answers of the seed questions. Expert opinion may be generated based on the performance-based weight and answers to the target questions. The expert opinion may be provided.

Claims

1. A computer-implemented method of providing expert elicitation via a computer network, comprising:

storing a plurality of expert information to an expert database, wherein the expert information comprises at least one of areas of expertise, contact information, active and inactive fields, or projects working and worked on;

receiving, by at least one processing circuit, a request for an expert opinion from a user via the computer network;

selecting a plurality of experts based on information in the request of the user;

generating a plurality of seed questions and target questions based on information in the request of the user;

sending the plurality of seed questions and target questions to each of the experts selected;

receiving answers of the seed questions and the target questions from each of the experts;

assigning, by the at least one processing circuit, a performance-based weight to each of the experts based on the answers of the seed questions;

generating, by the at least one processing circuit, the expert opinion based on the performance-based weight of each of the experts and the answers to the target questions; and

providing the expert opinion to the user.

2. The method of claim 1, wherein each of the answers comprises:

a plurality of estimates; and

a plurality of uncertain metrics, wherein each uncertain metric corresponding to an estimate in the plurality of estimates.

3. The method of claim 2, wherein the performance-based weight is determined by a calibration score and an information score, wherein the calibration score indicates the likelihood that the expert'"'"'s estimate matches a sample experimental result and the information score is determined by the expert'"'"'s uncertainty metrics.

4. The method of claim 3, wherein the assigning further comprises:

if the estimate to the seed question matches the sample experimental result, assigning a higher performance-based weight; and

if the estimate to the seed question does not match the sample experimental result, assigning a lower performance-based weight.

5. The method of claim 1, wherein the seed questions are between 10 and 20 in number.

6. The method of claim 1, further comprising searching the expert database for seed questions related to the expert opinion requested by the user.

7. The method of claim 1, wherein the experts are selected based on past performance of the experts and projects conducted by the experts.

8. The method of claim 1, wherein the expert in the database will be assigned a reward if the performance of the expert meets a certain benchmark.

12. The system of claim 1, wherein the one or more processing circuits are further configured to search the expert database for seed questions related to the expert opinion requested by the user.

13. The system of claim 1, wherein the experts are selected based on past performance of the experts and projects conducted by the experts.

14. The system of claim 1, wherein the expert in the database will be assigned a reward if the performance of the expert meets a certain benchmark.

9. A system of providing expert elicitation via a computer network, comprising:

one or more processing circuits configured to;

store a plurality of expert information to an expert database, wherein the expert information comprises at least one of areas of expertise, contact information, active and inactive fields, or projects working and worked on;

receive a request for an expert opinion from a user via the computer network;

select a plurality of experts based on information in the request of the user;

generate a plurality of seed questions and target questions based on information in the request of the user;

send the plurality of seed questions and target questions to each of the experts selected;

receive answers of the seed questions and the target questions from each of the experts;

assign a performance-based weight to each of the experts based on the answers of the seed questions;

generate the expert opinion based on the performance-based weight of each of the experts and the answers to the target questions; and

provide the expert opinion to the user.

10. The system of claim 9, wherein each of the answers comprises:

a plurality of estimates; and

a plurality of uncertain metrics, wherein each uncertain metric corresponding to an estimate in the plurality of estimates.

11. The system of claim 10, wherein the performance-based weight is determined by a calibration score and an information score, wherein the calibration score indicates the likelihood that the expert'"'"'s estimate matches a sample experimental result and the information score is determined by the expert'"'"'s uncertainty metrics.

15. A non-transitory computer-readable medium having machine instructions stored therein, the instructions being executable by one or more processors to cause the one or more processors to perform operations comprising:

storing a plurality of expert information to an expert database, wherein the expert information comprises at least one of areas of expertise, contact information, active and inactive fields, or projects working and worked on;

receiving a request for an expert opinion from a user via a computer network;

selecting a plurality of experts based on information in the request of the user;

generating a plurality of seed questions and target questions based on information in the request of the user;

sending the plurality of seed questions and target questions to each of the experts selected;

receiving answers of the seed questions and the target questions from each of the experts;

assigning a performance-based weight to each of the experts based on the answers of the seed questions;

generating the expert opinion based on the performance-based weight of each of the experts and the answers to the target questions; and

providing the expert opinion to the user.

16. The non-transitory computer-readable medium of claim 15, wherein each of the answers comprises:

a plurality of estimates; and

a plurality of uncertain metrics, wherein each uncertain metric corresponding to an estimate in the plurality of estimates.

17. The non-transitory computer-readable medium of claim 16, wherein the performance-based weight is determined by a calibration score and an information score, wherein the calibration score indicates the likelihood that the expert'"'"'s estimate matches a sample experimental result and the information score is determined by the expert'"'"'s uncertainty metrics.

18. The non-transitory computer-readable medium of claim 15, the instructions further comprising searching the expert database for seed questions related to the expert opinion requested by the user.

19. The non-transitory computer-readable medium of claim 15, wherein the experts are selected based on past performance of the experts and projects conducted by the experts.

20. The non-transitory computer-readable medium of claim 15, wherein the expert in the database will be assigned a reward if the performance of the expert meets a certain benchmark.