A system, method and program product for optimizing a team to solve a problem. The system includes: a team building system for building a fundamental analytic team from a database of analysts to solve an inputted problem, wherein the fundamental analytic team includes at least one cluster of analysts characterized with specificity and at least one cluster of analysts characterized with sensitivity; and a problem analysis system that collects sensor data from the fundamental analytic team operating within an immersive environment, wherein the problem analysis system includes a system for evaluating the sensor data to identify a bias condition from the fundamental analytic team, and includes a system for altering variables in the immersive environment in response to a detected bias condition.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computerized method for training a bias classifier for an immersive environment, comprising: encoding a cognitive solicitation scenario into a game having a set of action nodes, wherein each action node is identified with a set of possible bias classifications; providing a training mode in which each of a set of users traverses the set of action nodes and generates a bias probability at each action node; separating bias probability data from different user scenarios into groups and determining a specificity measure and a sensitivity measure for each group; and analyzing true positive rates and false positive rates for each group to determine a quality of the group as a bias classifier. 2. The computerized method of claim 1, wherein the specificity measure indicates a likelihood of giving a false positive response. 3. The computerized method of claim 1, wherein the sensitivity measure indicates a likelihood of giving a false negative response. 4. The computerized method of claim 1, wherein the set of possible bias classifications include Confirmation, Anchor, Representation, Projection, Blind Spot, and Attribution biases. 5. A computer program product stored on a computer readable storage medium, which when executed by a computing system, trains a bias classifier for an immersive environment, comprising: program code for encoding a cognitive solicitation scenario into a game having a set of action nodes, wherein each action node is identified with a set of possible bias classifications; program code for providing a training mode in which each of a set of users traverses the set of action nodes and generates a bias probability at each action node; program code for separating bias probability data from different user scenarios into groups and determining a specificity measure and a sensitivity measure for each group; and program code for analyzing true positive rates and false positive rates for each group to determine a quality of the group as a bias classifier. 6. The program product of claim 5, wherein the specificity measure indicates a likelihood of giving a false positive response. 7. The program product of claim 5, wherein the sensitivity measure indicates a likelihood of giving a false negative response. 8. The program product of claim 5, wherein the set of possible bias classifications include: Confirmation, Anchor, Representation, Projection, Blind Spot, and Attribution biases. 9. A system for training a bias classifier for an immersive environment, comprising: a system for encoding a cognitive solicitation scenario into a game having a set of action nodes, wherein each action node is identified with a set of possible bias classifications; a training mode in which each of a set of users traverses the set of action nodes and generates a bias probability at each action node; a system for separating bias probability data from different user scenarios into groups and determining a specificity measure and a sensitivity measure for each group; and a system for analyzing true positive rates and false positive rates for each group to determine a quality of the group as a bias classifier. 10. The system of claim 9, wherein the specificity measure indicates a likelihood of giving a false positive response. 11. The system of claim 9, wherein the sensitivity measure indicates a likelihood of giving a false negative response. 12. The system of claim 9, wherein the set of possible bias classifications include: Confirmation, Anchor, Representation, Projection, Blind Spot, and Attribution biases.