The present invention concerns methods and apparatus for detecting biasing effects induced by one or more biasing entities when using a quantum representation of like-minded subjects and their shared contextualizations of propositions. Like-minded subjects are understood to be any sentient beings, e.g., human beings, that use shared contextualizations modulo certain propositions and exhibit analogous measurable indications in response to these propositions. Measureable indications can take on the form of responses, actions, behaviors or any measurable aspects that can be collected from the subjects in response to the propositions. Contextualizations that are mutually incompatible are represented by non-commuting quantum mechanical operators while compatible contextualizations are represented by commuting quantum mechanical operators.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for detecting biasing effects on the contextualization of a test proposition by like-minded subjects, said method comprising: a) selecting by a mapping module said like-minded subjects exhibiting a shared contextualization and a shared measurable indication modulo a preparatory proposition analogous to said test proposition; b) assigning by an assignment module a subject value matrix PRV likely to be used in the contextualization of said test proposition by said like-minded subjects; c) collecting by a network monitoring unit measurable indications corresponding to eigenvalues of said subject value matrix PRV from a first subgroup of said like-minded subjects; d) introducing a biasing entity to a second subgroup of said like-minded subjects; and e) collecting by said network monitoring unit measurable indications corresponding to eigenvalues of said subject value matrix PRV from said second subgroup of said like-minded subjects. 2. The computer implemented method of claim 1, further comprising the step of detecting by a statistics module said biasing effects on the contextualization of said test proposition by comparing said measurable indications from said first subgroup and from said second subgroup. 3. The computer implemented method of claim 2, further comprising estimating by said statistics module a change in a quantum representation of said like-minded subjects due to said biasing effects. 4. The computer implemented method of claim 3, wherein said quantum representation comprises a density matrix. 5. The computer implemented method of claim 3, wherein said step of estimating further comprises presenting at least one additional proposition to said like-minded subjects. 6. The computer implemented method of claim 5, wherein said at least one additional proposition is chosen to be incompatible with said test proposition such that it induces in said like-minded subjects a secondary subject value matrix PRSV that does not commute with said subject value matrix PRV. 7. The computer implemented method of claim 1, wherein said biasing entity is selected from any one or more members of the group consisting of subjects, objects and messages. 8. The computer implemented method of claim 7, wherein said step of introducing comprises exposing said second subgroup of said like-minded subjects to said biasing entity within a network. 9. The computer implemented method of claim 7, wherein said step of introducing comprises exposing said second subgroup of said like-minded subjects to said biasing entity in real life. 10. The computer implemented method of claim 1, wherein said test proposition is associated with at least one item from the group consisting of a subject, an object and an experience. 11. A computer system for detecting biasing effects on the contextualization of a test proposition by like-minded subjects, said computer system comprising: a) a mapping module for selecting said like-minded subjects having a shared measurable indication and a shared contextualization of a preparatory proposition analogous to said test proposition; b) an assignment module for assigning a subject value matrix PRV likely to be used in the contextualization of said test proposition by said like-minded subjects; c) a network monitoring unit for collecting measurable indications corresponding to eigenvalues of said subject value matrix PRV from a first subgroup of said like-minded subjects and from a second subgroup of said like-minded subjects; and d) a biasing entity for introduction to said second subgroup prior to collecting measurable indications. 12. The computer system of claim 11, further comprising a statistics module for detecting said biasing effects of the contextualization of said test proposition by comparing said measurable indications from said first subgroup and from said second subgroup. 13. The computer system of claim 11, wherein said biasing entity is selected from any one or more members of the group consisting of subjects, objects and messages. 14. The computer system of claim 13 further comprising a network and wherein said biasing entity is introduced by exposure to said second subgroup of like-minded subjects within said network. 15. The computer system of claim 14, wherein said network is selected from the group consisting of the Internet, the World Wide Web, a Wide Area Network (WAN) and a Local Area Network (LAN). 16. The computer system of claim 14, wherein said like-minded subjects are members of a social group. 17. The computer system of claim 16, wherein said social group is selected from any one or more of the group of social networks consisting of Facebook, LinkedIn, Google+, MySpace, Instagram, Tumblr, YouTube. 18. The computer system of claim 16, wherein said social group manifests an affiliation with one or more product sites selected from the group consisting of Amazon.com, Walmart.com, bestbuy.com, Groupon.com, Netflix.com, iTunes, Pandora and Spotify. 19. The computer system of claim 11, wherein said test proposition is associated with at least one item from the group consisting of a subject, an object and an experience.