2014/03/30 by J.‐C. Cortés, Francisco-J. Santonja, Cortés, Juan-Carlos +7
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Data Analysis with R #FOS: Computer and information sciences #Machine Learning (cs.LG) #Opinion Dynamics and Social Influence #Probabilistic and Robust Engineering Design #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1404.0649
openalex publication_date 2014/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present a computational technique to deal with uncertainty\nin dynamic continuous models in Social Sciences. Considering data from surveys,\nthe method consists of determining the probability distribution of the survey\noutput and this allows to sample data and fit the model to the sampled data\nusing a goodness-of-fit criterion based on the chi-square-test. Taking the\nfitted parameters non-rejected by the chi-square-test, substituting them into\nthe model and computing their outputs, we build 95% confidence intervals in\neach time instant capturing uncertainty of the survey data (probabilistic\nestimation). Using the same set of obtained model parameters, we also provide a\nprediction over the next few years with 95% confidence intervals (probabilistic\nprediction). This technique is applied to a dynamic social model describing the\nevolution of the attitude of the Basque Country population towards the\nrevolutionary organization ETA.\n