Methods for analyzing decisions made by real-time collective intelligence systems
Systems and methods for real-time collaborative computing and collective intelligence are disclosed. A collaborative application runs on a collaborative server connected to a plurality of computing devices. Collaborative sessions are run wherein a group of independent users, networked over the internet, collaboratively answer questions in real-time, thereby harnessing their collective intelligence. Methods are disclosed for assigning users to factions during a collaborative decision process, wherein the collaborative server repeatedly checks the input of each user with respect to a plurality of proposed answers and assigns the user to the faction associated with the answer the user is trying to select. Furthermore, user assessments are made based on a stored time-history of faction associations for that user during a decision period. Such assessments include, but are not limited to, determining which users were entrenched, which were flexible, and which were fickle, during the collective intelligence decision making process.
1. A method for faction analysis for a collaboration system including a plurality of individual computing devices and a collaboration server exchanging data with each of the plurality of computing devices, whereby a group of individual users is enabled to participate in a decision process including the real-time collaborative control of a graphical object and a plurality of selection targets, each user of the group of individual users associated with and interacting with one of the individual computing devices, the collaboration system performing the steps of:
receiving, from each of the computing devices at multiple time steps of the decision process, at least one real-time user intent value reflecting the associated individual user's current user input regarding a desired motion of the graphical object, whereby a current location and orientation of a user icon associated with the user is updated on a display of the computing device of the associated user;
determining, for multiple time steps, a current location, on the display, of the graphical object based at least in part upon the plurality of real-time user intent values received from the plurality of computing devices;
determining, for multiple time steps, one spatial region associated with each selection target, wherein each spatial region is determined based at least in part on the current location of the graphical object and each spatial region is a region bounded by an angle having an angle vertex located at a center of the current location of the graphical object, wherein the angle is centered on the selection target associated with the spatial region, wherein the size of each spatial region angle is determined by the proximity of the current location of the graphical object to the selection target associated with the selection target, and wherein the closer the proximity of the graphical object to the selection target, the larger the size of the spatial region angle associated with that selection target;
determining, for multiple time steps and for each user, if the user icon is associated with one spatial region; and
assigning, for multiple time steps, for each user icon at least partially present within a boundary of one spatial region, of the user to a faction associated with the spatial region associated with the selection target.
2. A method for faction analysis for a collaboration system including a plurality of individual computing devices and a collaboration server exchanging data with each of the plurality of computing devices, whereby a group of individual users is enabled to participate in a decision process including the real-time collaborative control of a graphical object and a plurality of selection targets, each user of the group of individual users associated with and interacting with one of the individual computing devices, the collaboration system performing the steps of:
receiving, from each of the computing devices at multiple time steps of the decision process, at least one real-time user intent value reflecting the associated individual user's current user input regarding a desired motion of the graphical object, whereby a current location and orientation of a user icon associated with the user is updated on a display of the computing device of the associated user; determining, for multiple time steps, a current location, on the display, of the graphical object based at least in part upon the plurality of real-time user intent values received from the plurality of computing devices,
determining, for multiple time steps, one spatial region associated with each selection target, wherein each spatial region is determined based at least in part on the current location of the graphical object and each spatial region is a region bounded by an angle having an angle vertex located at a center of the current location of the graphical object, wherein the angle is centered on the selection target associated with the spatial region, wherein the size of each spatial region angle is determined by the proximity of the current location of the graphical object to the selection target associated with the selection target, the step of determining the spatial regions further comprising
determining, for multiple time steps, a current distance for each spatial region, wherein the current distance for each selection is a distance from the center of the graphical object at a current time step to a center of the selection target,
determining, for multiple time steps and for each user, if the user icon is associated with one spatial region; and
assigning, for multiple time steps, for each user icon at least partially present within a boundary of one spatial region, of the user to a faction associated with the spatial region associated with the selection target.
3. The method for faction analysis of claim 2 , the step of determining the spatial regions further comprising the step of:
calculating according to a formula, for multiple time steps, the size of each spatial region angle based on the current distance.
4. The method for faction analysis of claim 3 , wherein the formula results in increasing the spatial region angle size as the current distance decreases.
5. The method for faction analysis of claim 4 , wherein the angle size is limited to less than a maximum angle size.
6. A method for faction analysis for a collaboration system including a plurality of individual computing devices and a collaboration server exchanging data with each of the plurality of computing devices, whereby a group of individual users is enabled to participate in a decision process including the real-time collaborative control of a graphical object and a plurality of selection targets, each user of the group of individual users associated with and interacting with one of the individual computing devices, the collaboration system performing the steps of:
receiving, from each of the computing devices at multiple time steps of the decision process, at least one real-time user intent value reflecting the associated individual user's current user input regarding a desired motion of the graphical object, whereby a current location and orientation of a user icon associated with the user is updated on a display of the computing device of the associated user;
determining, for multiple time steps, a current location, on the display, of the graphical object based at least in part upon the plurality of real-time user intent values received from the plurality of computing devices;
determining, for multiple time steps, one spatial region associated with each selection target, wherein each spatial region is determined based at least in part on the current location of the graphical object;
determining, for multiple time steps and for each user, if the user icon is associated with one spatial region;
assigning, for multiple time steps, for each user icon at least partially present within a boundary of one spatial region, of the user to a faction associated with the spatial region associated with the selection target;
determining and storing, for at least one user, a number of times the faction assignment for each user changes during a decision period
determining and storing, for each at least one user, a number of factions each user “pulled” towards during the decision period; and
determining a user assessment value for the at least one user based on at least one of the number of factions each user “pulled” towards during the decision period, and the number of times the faction assignment changed during the decision period.
7. The method for faction analysis of claim 6 , further comprising the step of determining, for at least one user, whether the user is entrenched, wherein the user is entrenched when the number of different factions the user “pulled” towards during the decision period was determined to be 1.
8. The method for faction analysis of claim 6 , further comprising the step of determining, for at least one user, whether the user is flexible, wherein the user is flexible when the number of times the faction assignment for the user changed during the decision period was determined to be greater than a defined lower limit and less than a defined upper limit.
9. The method for faction analysis of claim 6 , further comprising the step of determining, for at least one user, whether the user is fickle, wherein the user is fickle when the number of times the faction assignment for the user changed during the decision period was determined to be greater than a defined upper limit.