A probabilistic estimation and prediction technique for dynamic continuous social science models: The evolution of the attitude of the Basque Country population towards ETA as a case study

作者：Cortes Juan Carlos; Santonja Francisco J; Tarazona Ana C; Villanueva Rafael J^*; Villanueva Oller Javier

来源：Applied Mathematics and Computation, 2015, 264: 13-20.

DOI：10.1016/j.amc.2015.03.128

• Social dynamic models
• Probabilistic estimation
• Probabilistic prediction
• Attitude dynamics

In this paper, a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences is presented. Considering data from surveys, the method consists of determining the probability distribution of the survey output and this allows to sample data and fit the model to the sampled data using a goodness-of-fit criterion based on the chi(2)-test. Taking the fitted parameters that were not rejected by the chi(2)-test, substituting them into the model and computing their outputs, 95% confidence intervals in each time instant capturing the uncertainty of the survey data (probabilistic estimation) is built. Using the same set of obtained model parameters, a prediction over the next few years with 95% confidence intervals (probabilistic prediction) is also provided. This technique is applied to a dynamic social model describing the evolution of the attitude of the Basque Country population towards the revolutionary organisation ETA.

• 出版日期2015-8-1