14. A computer-implemented method for recognizing surprising events and predicting future surprising events, comprising: providing a predictive model; associating definitions of surprising events with the predictive model; providing the predictive model with contextual and historical data; predicting an event as a function of the contextual and historical data; and determining that the predicted event corresponds to one or more of the definitions of surprising events by calculating a measure of difference between a prediction made by a robust predictive model and a prediction made by a user expectancy model; wherein the robust predictive model predicts events that are surprising to a person based on interdependencies between variables associated with the contextual and historical data, and wherein the interdependencies are not contemplated by the person; and wherein the user expectancy model predicts events that are surprising to the person based on, at least in part, machine learning and a case library that includes a plurality of surprising events and observations associated with the plurality of surprising events; and outputting the predicted event to the person if the predicted event corresponds to one or more of the definitions of surprising events.
14. A computer-implemented method for recognizing surprising events and predicting future surprising events, comprising: providing a predictive model; associating definitions of surprising events with the predictive model; providing the predictive model with contextual and historical data; predicting an event as a function of the contextual and historical data; and determining that the predicted event corresponds to one or more of the definitions of surprising events by calculating a measure of difference between a prediction made by a robust predictive model and a prediction made by a user expectancy model; wherein the robust predictive model predicts events that are surprising to a person based on interdependencies between variables associated with the contextual and historical data, and wherein the interdependencies are not contemplated by the person; and wherein the user expectancy model predicts events that are surprising to the person based on, at least in part, machine learning and a case library that includes a plurality of surprising events and observations associated with the plurality of surprising events; and outputting the predicted event to the person if the predicted event corresponds to one or more of the definitions of surprising events. 18. The method of claim 14 , further comprising: receiving a first prediction of a first event from a user-expectancy model, wherein the user-expectancy model generates the first prediction based on at least one marginal statistical model defined by a subset of observations that are contemplated by the person considering contextual and historical data; receiving a second prediction of a second event from a robust predictive model, wherein the robust predictive model utilizes the contextual and historical data to generate the second prediction based on interdependencies between variables that are not contemplated by the person; and outputting the second prediction of the second event as a surprising event based on, at least in part, a comparison between the first prediction and the second prediction.