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In this paper, we present a computational technique to deal with uncertaintyin dynamic continuous models in Social Sciences. Considering data from surveys,the method consists of determining the probability distribution of the surveyoutput and this allows to sample data and fit the model to the sampled datausing a goodness-of-fit criterion based on the chi-square-test. Taking thefitted parameters non-rejected by the chi-square-test, substituting them intothe model and computing their outputs, we build 95% confidence intervals ineach time instant capturing uncertainty of the survey data (probabilisticestimation). Using the same set of obtained model parameters, we also provide aprediction over the next few years with 95% confidence intervals (probabilisticprediction). This technique is applied to a dynamic social model describing theevolution of the attitude of the Basque Country population towards therevolutionary organization ETA.

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