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

TL;DR

This paper introduces a probabilistic method for estimating and predicting the evolution of social attitudes over time, accounting for data uncertainty, demonstrated through the case of Basque attitudes towards ETA.

Contribution

It presents a novel computational approach combining uncertainty quantification and model fitting for dynamic social science models using survey data.

Findings

Built 95% confidence intervals for attitude evolution over time.

Provided probabilistic predictions for future attitude trends.

Validated the method on Basque population attitude data.

Abstract

In this paper, we present a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences. 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-square-test. Taking the fitted parameters non-rejected by the chi-square-test, substituting them into the model and computing their outputs, we build 95% confidence intervals in each time instant capturing uncertainty of the survey data (probabilistic estimation). Using the same set of obtained model parameters, we also provide a prediction over the next few years with 95% confidence intervals (probabilistic prediction). This technique is applied to a dynamic social model describing the evolution of the attitude of the Basque Country population towards the revolutionary organization ETA.