The invention notably relates to a computer-implemented method for selecting an appropriate decision by constraining options assessed with a model. The method comprises selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing index for indexing the generated options and target values associated with the trained model; querying the said index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for selecting an appropriate decision by constraining options assessed with a model, comprising: selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing an index for indexing the generated options and target values associated with the trained model; querying the index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; and returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option. 2. The computer-implemented method of claim 1, wherein training the model further comprise: collecting a set of data representing similar events; and training the model based on the collected set of data representing similar events. 3. The computer-implemented method of claim 1, wherein the set of data representing similar events comprises a set of observations wherein each observation is defined with variables: identifying target variables on the set of observations, a target variable being a variable; identifying measurable variables on the set of observations, a measurable variable being a variable that can be measured in a current observation of a current event that is similar to the past events; and identifying actionable variables on the set of observations, an actionable variable being a variable that can be acted upon based on the decision of a user. 4. The computer-implemented method of claim 2, wherein training the model further comprises: computing a set of data that are generated from a set of simulations; and training the model based on the generated set of data. 5. The computer-implemented method of claim 3, wherein generating the options that represent hypothetical events comprises: defining each variable of each observation on continuous segments or on discrete sets of possible values of the said each variable; and generating the options by generating values on the segments of definition or sets of possible values for the respective variables. 6. The computer-implemented method of claim 5, wherein generating the options option further comprises, for each option to be generated: selecting an event among the similar events represented by the set of data; and setting a variable at the option to be generated based on the value of the variable of the selected event. 7. The computer-implemented method of claim 5, wherein the step of generating values is performed with the use of a random variate generator. 8. The computer-implemented method of claim 3, comprising: determining dependencies between the variables of the observations of the set; and removing events among the generated events whose variables do not satisfy the determined dependencies. 9. The computer-implemented method of claim 8, wherein one or more variables of one or more observations are transmitted in real time. 10. The computer-implemented method of claim 9, further comprising: generating new options from the transmitted one or more variables; computing new target values by applying the trained model on the new options generated; and updating the index by indexing the generated new options and the computed new target values. 11. The computer-implemented method of claim 9, wherein the constraint according to which a selection is performed by querying the index applies to the variables transmitted in real-time and is set using the values transmitted in real-time. 12. A computer program comprising instructions for performing the method of claim 1. 13. A non-transitory computer readable storage medium having recorded thereon a computer program that when executed by a computer causes the computer to implement a method for selecting an appropriate decision by constraining options assessed with a model, the method comprising: selecting a model capable of receiving inputs and providing output in response to an input; training a model with a set of data representing similar events; generating options that represent hypothetical events; computing target values by applying the trained model on the generated options; computing an index for indexing the generated options and target values associated with the trained model; querying the index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; and returning, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option. 14. A server comprising: processing circuitry coupled to a memory, the memory having recorded thereon the computer program for selecting an appropriate decision by constraining options assessed with a model, the processing circuitry implementing the computer program by being configured to select a model capable of receiving inputs and providing output in response to an input; train a model with a set of data representing similar events; generate options that represent hypothetical events; compute target values by applying the trained model on the generated options; compute an index for indexing the generated options and target values associated with the trained model; query the index for obtaining a selection of a set of options, the selection being performed according to a specific constraint; and return, as a result of the query, a subset of the set of the generated options, the subset being ranked according to the target values associated with each option. 15. The server of claim 14, wherein the server is connected to a client computer from which the query on the said index is generated.