Abstract: The present disclosure describes systems and methods for computer-moderated collaborative estimation among a population of human participants using a plurality of networked computing devices. A query (e.g., estimation prompt) may be presented to participants for collecting collaborative responses. The population of users may each set an initial estimation response to the prompt during an initial estimation time period, which is then followed by a collaborative estimation time period (during which all users are asked to produce an updated estimation response to the same prompt). A forbidden deadband region defines an area around each participant's initial estimation that the participant must move their variable slider out of in order for their slider to register as part of the collaborative real-time process. In some embodiments, a participant is not shown initial estimation responses of other participants until the participant has moved their slider out of the forbidden deadband region.

Abstract: The present disclosure describes systems and methods for enabling real-time conversational dialog among a large population of networked human users while facilitating convergence on groupwise decisions, insights, and solutions, and amplifying collective intelligence. A collaboration server running a collaboration application is provided, wherein the collaboration server is in communication with the plurality of the networked computing devices and each computing device is associated with one user of the population of human participants. In some cases, the collaboration server defines a plurality of sub-groups of the population of human participants. A local chat application configured for displaying a conversational prompt received from the collaboration server is provided on each networked computing device. The local chat application enables real-time chat communication with other users of a sub-group assigned by the collaboration server.

Abstract: Systems and methods for amplifying the collective intelligence of networked human groups engaged in collaborative forecasting of future events having two possible outcomes. During a real-time session a computing device for each user displays a forecasting prompt and a dynamic user interface which includes a user-manipulatable marker moved by the user between a first limit and a second limit, where the position of the marker defines a forecasted probability of each possible outcome. The display also includes a first reward value and a second reward value, both of which are interactively responsive to the marker position. During a first time period, users manipulate the markers. After the first time period, a perturbation stimulus is displayed. During a second time period, users again manipulate the markers. After the second time period, a final group forecast is calculated based on the data collected during the first and second time periods.

Abstract: Systems and methods for amplifying the accuracy of group forecasts and insights through interactive behavioral polling with perturbation analysis. During a first time period the system collects behavioral data of the user interaction with the dynamic user interface and a first displayed prompt, and then performs a perturbation analysis using the data collected during the first time period. A perturbation stimulus is displayed to the user, and during a second time period the user may additionally interact with the dynamic interface during a second time period, during which the system collects behavioral data of the user interaction during the second time period. The system determines a prediction based on data collected during at least one of the time periods from each of a plurality of users.

Abstract: System and method for amplifying the accuracy of forecasts generated by software systems that harness the collective intelligence of human populations by curating optimized sub-populations through an intelligent selection process. Participants predict event outcomes and/or provide evaluations of their confidence in their predictions. The system determines a score wherein the alignment score indicates how well that participant's prediction aligns with the predictions given by the baseline population. Participants can then be selected from the population based on the participant alignment scores.

Abstract: Systems and methods for amplifying the intelligence of networked human populations, maximizing the accuracy of collaborative forecasts and group insights. This includes systems and methods for sending and presenting a forecasting query for a future event to a plurality of participants, each using a networked computing device, the forecasting query describing a future event to be collaboratively predicted by the population of human participants. An initial forecast response is collected from each participant and analyzed by a central server. A plurality of unique overlapping subsets of responses are determined by the server. Unique overlapping subsets of unique initial forecast responses are then displayed, at substantially the same time, on each computing device. A second forecast response is collected from each participant and a final forecast is determined.

Abstract: Systems and methods for amplifying the collective intelligence of networked human groups engaged in collaborative forecasting of future events having two possible outcomes. During a real-time session a computing device for each user displays a forecasting prompt and a dynamic user interface which includes a user-manipulatable marker moved by the user between a first limit and a second limit, where the position of the marker defines a forecasted probability of each possible outcome. The display also includes a first reward value and a second reward value, both of which are interactively responsive to the marker position. During a first time period, users manipulate the markers. After the first time period, a perturbation stimulus is displayed. During a second time period, users again manipulate the markers. After the second time period, a final group forecast is calculated based on the data collected during the first and second time periods.

Abstract: Systems and methods for obtaining dynamic behavioral data from a plurality of users of a plurality of computing devices in response to a stimuli, and using the dynamic behavioral data to make an optimized forecast regarding the outcome of a future event. During a first time period the system collects behavioral data of the user interaction with the dynamic user interface and a first displayed prompt, and then performs a perturbation analysis using the data collected during the first time period. A perturbation stimulus is displayed to the user, and during a second time period the user may additionally interact with the dynamic interface during a second time period, during which the system collects behavioral data of the user interaction during the second time period. The system determines a prediction based on data collected during at least one of the time periods from each of a plurality of users.

Abstract: System and method amplifying the accuracy of forecasts generated by software systems that harness the collective intelligence of human populations by curating optimized sub-populations through an intelligent selection process. Participants predict event outcomes and/or provide evaluations of their confidence in their predictions. The system determines an outlier score for each participant based on the participant's responses and the relation of the participant's responses to the predictions of the population as a whole. Participants can then be selected from the population based on the participant outlier scores.

Abstract: Systems and methods for amplifying the intelligence of networked human populations, maximizing the accuracy of collaborative forecasts and group insights. This includes systems and methods for sending and presenting a forecasting query for a future event to a plurality of participants, each using a networked computing device, the forecasting query describing a future event to be collaboratively predicted by the population of human participants. An initial forecast response is collected from each participant and analyzed by a central server. A plurality of unique overlapping subsets of responses are determined by the server. Unique overlapping subsets of unique initial forecast responses are then displayed, at substantially the same time, on each computing device. A second forecast response is collected from each participant and a final forecast is determined.

Abstract: Systems and methods for eliciting from a user a probabilistic indication of the likelihood of two possible outcomes of a future event. During a session a user manipulates a wager marker on a graphical user interface displayed on a display, where the wager marker is moveable between two limits. Probabilities for each of the two limits are determined based on the position of the marker. Values representing the probabilities are updated on the interface in real time.

Abstract: Systems and methods are for enabling a group of individuals, each using an individual computing device, to collaboratively answer questions or otherwise express a collaborative will/intent in real-time as a unified intelligence. The collaboration system comprises a plurality of computing devices, each of the devices being used by an individual user, each of the computing devices enabling its user to contribute to the emerging real-time group-wise intent. A collaboration server is disclosed that communicates remotely to the plurality of individual computing devices. Herein, a variety of inventive methods are disclosed for interfacing users and calibrating for their variable confidence in a real-time synchronized group-wise experience, and for deriving a convergent group intent from the collective user input.

Abstract: 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 for collaborative selection of a target point on an image displayed on the computing devices. Methods for collaborative zooming and panning of an image displayed on the computing devices.

Abstract: Systems and methods for user interfaces for use on a computing device of a real-time collaborative computing system. A collaborative application runs on the computing device and displays information and data regarding the collaboration system and also receives user input via the user interface. The display interface arrangement may vary based on a type of collaborative session. Graphical user interfaces include a user interface based on a magnetic force model.

Abstract: Systems and methods are for enabling a population of individuals to collaboratively answer questions or otherwise express a collaborative intent by forming a real-time closed-loop system influenced at least in part, by human gaze input from a plurality of participants. The system comprises a plurality of computing devices connected to a central server, each of the computing devices being used by an individual user and configured to enable its user to contribute to the emerging group intelligence through their real-time gaze location. In some embodiments, gaze is used in combination with other input modalities.

Abstract: 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. Systems and methods for determining a group intent vector from a plurality of user intent vectors in response to user input, the group intent vector including a bias restoring vector to correct positional bias resulting from a target layout.

Abstract: Systems and methods are for enabling a group of individuals, each using an individual computing device, to collaboratively answer questions or otherwise express a collaborative will/intent in real-time. The collaboration system comprises a plurality of computing devices, each of the devices being used by an individual user, each of the computing devices enabling its user to contribute to the emerging real-time group-wise intent. A collaboration server is disclosed that communicates remotely to the plurality of individual computing devices. Herein, a variety of inventive methods are disclosed for interfacing users in a real-time synchronized group-wise experience, and for deriving a convergent group intent from the collective user input.

Abstract: 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. Systems and methods for selection among a set of user-input suggestions. The group is divided into subgroups, each subgroup selecting one suggestion from a subgroup of suggestions. The process is repeated with the remaining selected suggestions until one final target is selected, by the entire group, from the remaining suggestions.

Abstract: Systems and methods for real-time collaborative computing and collective intelligence are disclosed. A hybrid swarm intelligence system includes a central collaboration server, a plurality of computing devices in communication with the central server, and an agent application in communication with the central server. In response to information sent from the central server during a group collaboration session, user input is sent to the central server via the computing devices, and machine input is given to the server via the agent application, which determines input based on rules, additional data, and/or machine learning techniques. The central server uses the user input and the machine input to repeatedly provide feedback to the agent application and users during the group collaboration session.

Abstract: Systems and methods are for enabling a group of individuals, each using an individual computing device, to collaboratively control a vehicle in real-time as a unified intelligence. The collaboration system comprises a plurality of computing devices, each of the devices being used by an individual user, each of the computing devices enabling its user to contribute to the emerging real-time group-wise intent. A collaboration server is disclosed that moderates the closed-loop system, enabling convergence upon a unified group intent. Control commands are repeatedly sent to the vehicle based on the determined group intent.