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We propose a model for several alternative approaches to the prediction of the probabilities involved by processing the obtained signals sent by the players, which may be companies competing in the market. They set a minimum level of expected utility and are interested in the probability that this expectation will be satisfied. These approaches are analyzed using Monte Carlo experiments, developed in the framework of Game Theory and different statistical models to make predictions about the probability of success or to obtain a certain utility. The behavior of the different proposed strategies is established, considering that the errors follow a Normal, double Exponential and Cauchy distribution. A comparison is made of the results given by the algorithms programmed R and SAS, finding that R are better in terms of, however GLIMIX PRC MIXED of SAS has lower values of V2 in all cases. RESUMEN Se propone un modelo para varios enfoques alternativos de la predicción de las probabilidades involucradas a partir de procesar las señales obtenidas enviadas por los jugadores, que pueden ser empresas compitiendo en el mercado. Ellos fijan un nivel mínimo de la utilidad esperada y se interesan en la probabilidad de que se satisfaga esta expectativa. Estos enfoques son analizados usando experimentos de Monte Carlo, desarrollado en el marco de la Teoría de Juegos y diferentes modelos estadísticos para hacer predicciones sobre la probabilidad de éxito o de obtener una cierta utilidad. Se establece el comportamiento de las distintas estrategias propuestas considerando que los errores siguen una distribución normal, doble exponencial y Cauchy. Se hace una comparación de los resultados dados por los algoritmos programados el R y SAS, encontrando que son mejores los de R en términos de, sin embargo GLIMIX PRC MIXED de SAS tiene valores menores de V2 en todos los casos.

Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. The scrambling of the sensitive variable is performed through the selection of a report Ri, i = 1,2,3. In order to evaluate the accuracy and efficiency of the proposed estimators, a simulation was carried out with two databases, where the sensitive variables are the destruction of poppy crops in Guerrero, Mexico, and the age at first sexual intercourse. The results show that more accurate estimates are obtained with the proposed model.