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Scientists develop neural network that can predict public corruption

They say that absolute power corrupts absolutely, and there are always concerns as to how the general public can be sure that elected officials are on the up and up. Now a pair of scientists from the Higher School of Economics and University of Valladolid, Spain may have found an answer for that.

Researchers Felix J. Lopez-Iturriaga and Ivan Pastor Sanz have developed a neural network that they claim can predict the probability of corruption cases emerging, based on certain political and economic factors. The pair analyzed the data from a number of actual corruption cases in Spain, and they determined that economic factors like growth, increasing housing prices, and real estate taxation can be used as predictors of future public corruption. The researchers also found that the risks for corruption increase when the same party remains in control for a substantial length of time.

The results of the study have been published in Social Indicators Research, and the scientists hope that their work will lead to anti-corruption "early warning" measures being implemented.

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