A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for social collaboration in probabilistic prediction, the method comprising: sending, from an application executing in a data processing system using a processor and a memory, a set of predictions to a user device, wherein a prediction in the set of predictions is a probability of an outcome of an event, wherein the probability is computed using a prediction model trained with training data corresponding to the event; receiving an input from the user device, wherein the input comprises a new prediction made at the user device using a new prediction model executing on the user device; determining a difference between the prediction and the new prediction; and revising the prediction model to produce a revised prediction, wherein a revised difference between the revised prediction and the new prediction is smaller than the difference. 2. The method of claim 1, further comprising: collecting a set of inputs from a set of user devices, wherein the set of inputs provide a corresponding set of new predictions, the set of user devices including the user device, and the set of new predictions including the new prediction; determining whether a subset of the set of inputs identify the difference, wherein a number of inputs in the subset exceeds a threshold number; and performing the revising responsive to the subset of the set of inputs identifying the difference. 3. The method of claim 1, further comprising: determining a category assigned to a user providing the new prediction from the user device; and performing the revising responsive to the category exceeding a threshold category. 4. The method of claim 3, wherein the category is indicative of a level of expertise of the user about the event. 5. The method of claim 1, further comprising: storing the input as labeled data, wherein the labeled data comprises: a value derived from the input, a category assigned to a user providing the input from the user device; and a fact that affects the outcome of the event and where the fact has been used in the new prediction. 6. The method of claim 5, wherein the labeled data further comprises: a second value computed from a vote provided by the user, wherein the vote comprises an ordinal value indicative of the difference. 7. The method of claim 5, further comprising: retraining the prediction model using the labeled data, the retraining forming a retrained prediction model, wherein the retrained prediction model produces a post-retraining prediction by changing a weight associated with the fact in the prediction model, and wherein a post-retraining difference between the post-retraining prediction and the new prediction is smaller than the difference. 8. The method of claim 1, further comprising: comparing a set of previous inputs with a corresponding set of actual outcomes of a corresponding set of previous events, wherein the set of previous inputs is stored in a historical repository of inputs, wherein the input is provided by a user of the user device towards the event and a previous input in the set of previous inputs is provided by the user towards a previous event, wherein the event and the previous event are of a common type; determining a degree of match between outcomes predicted in the set of previous inputs and the set of actual outcomes; selecting a category corresponding to a threshold degree of match, wherein the degree of match exceeds the threshold degree of match; and assigning the user to the category. 9. The method of claim 8, further comprising: adding the input to the set of previous inputs to form a revised set of previous inputs; adding an outcome of the event to the set of actual outcomes; determining a revised degree of match between outcomes predicted in the revised set of previous inputs and the set of actual outcomes; selecting a new category corresponding to a new threshold degree of match, wherein the revised degree of match exceeds the new threshold degree of match; and assigning the user to the new category. 10. The method of claim 1, wherein the difference exceeds a threshold amount of the difference. 11. The method of claim 1, wherein the input further comprises a vote on the prediction, wherein vote comprises one of (i) a binary value indicative of an agreement of a user with the prediction, and (ii) an ordinal value indicative of the difference. 12. The method of claim 1, wherein the new prediction model is unknown in the application, and wherein the new prediction model is trained using training data available at the user device. 13. The method of claim 1, further comprising: selecting a set of prediction models, wherein a first prediction model in the set of prediction models is trained to predict the outcome of the event using a first algorithm and a second prediction model in the set of prediction models is trained to predict the outcome of the event using a second algorithm; and generating the set of predictions in an ensemble of prediction models. 14. A computer program product comprising one or more computer-readable tangible storage devices and computer-readable program instructions which are stored on the one or more storage devices and when executed by one or more processors, perform the method of claim 1. 15. A computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on the one or more storage devices for execution by the one or more processors via the one or more memories and when executed by the one or more processors perform the method of claim 1. 16. A computer program product for social collaboration in probabilistic prediction, the computer program product comprising: one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices, to send, from an application executing in a data processing system using a processor and a memory, a set of predictions to a user device, wherein a prediction in the set of predictions is a probability of an outcome of an event, wherein the probability is computed using a prediction model trained with training data corresponding to the event; program instructions, stored on at least one of the one or more storage devices, to receive an input from the user device, wherein the input comprises a new prediction made at the user device using a new prediction model executing on the user device; program instructions, stored on at least one of the one or more storage devices, to determine a difference between the prediction and the new prediction; and program instructions, stored on at least one of the one or more storage devices, to revise the prediction model to produce a revised prediction, wherein a revised difference between the revised prediction and the new prediction is smaller than the difference. 17. The computer program product of claim 16, further comprising: program instructions, stored on at least one of the one or more storage devices, to collect a set of inputs from a set of user devices, wherein the set of inputs provide a corresponding set of new predictions, the set of user devices including the user device, and the set of new predictions including the new prediction; program instructions, stored on at least one of the one or more storage devices, to determine whether a subset of the set of inputs identify the difference, wherein a number of inputs in the subset exceeds a threshold number; and program instructions, stored on at least one of the one or more storage devices, to perform the revising responsive to the subset of the set of inputs identifying the difference. 18. The computer program product of claim 16, further comprising: program instructions, stored on at least one of the one or more storage devices, to determine a category assigned to a user providing the new prediction from the user device; and program instructions, stored on at least one of the one or more storage devices, to perform the revising responsive to the category exceeding a threshold category. 19. The computer program product of claim 18, wherein the category is indicative of a level of expertise of the user about the event. 20. A computer system for social collaboration in probabilistic prediction, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to send, from an application executing in a data processing system using a processor and a memory, a set of predictions to a user device, wherein a prediction in the set of predictions is a probability of an outcome of an event, wherein the probability is computed using a prediction model trained with training data corresponding to the event; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to receive an input from the user device, wherein the input comprises a new prediction made at the user device using a new prediction model executing on the user device; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine a difference between the prediction and the new prediction; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to revise the prediction model to produce a revised prediction, wherein a revised difference between the revised prediction and the new prediction is smaller than the difference.