"Artificial neural networks and a database of real cases have revealed the most predictive factors of corruption. / Pixabay
Two researchers from the University of Valladolid have developed a model with artificial neural networks to predict in which Spanish provinces corruption cases could appear with more probability, after one, two and up to three years.
The study, published in Social Indicators Research, does not mention the provinces most prone to corruption so as not to generate controversy, explains one of the authors, Ivan Pastor, to Sinc, who recalls that, in any case, "a greater propensity or high probability does not imply corruption will actually happen."
The data indicate that the real estate tax (Impuesto de Bienes Inmuebles), the exaggerated increase in the price of housing, the opening of bank branches and the creation of new companies are some of the variables that seem to induce public corruption, and when they are added together in a region, it should be taken into account to carry out a more rigorous control of the public accounts.
"In addition, as might be expected, our model confirms that the increase in the number of years in the government of the same political party increases the chances of corruption, regardless of whether or not the party governs with majority,” says Pastor..."
at http://www.orrazz.com/2018/01/artificial-intelligence-predicts.html
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