首页|Investigators at Federal University Goias Describe Findings in Machine Learning (Intervencion Con El Empleo De Nudges Y Normas Sociales)
Investigators at Federal University Goias Describe Findings in Machine Learning (Intervencion Con El Empleo De Nudges Y Normas Sociales)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news originating from Goiania, Brazil, by N ewsRx correspondents, research stated, “In this research, a group of charging me ssages for the payment of taxes was investigated. Altogether 12 variations of bi lling messages (social norms, simplification, disclosure, previous engagement, r eminders and previous choices) were evaluated, and their effectiveness was teste d.” Our news journalists obtained a quote from the research from Federal University Goias, “The messages were transmitted to defaulting microentrepreneurs in four B razilian states. From a database containing information about defaulting micro e ntrepreneurs, 250 thousand text messages were sent making charges. The data were obtained from the Secretaria Especial da Micro e Pequena Empresa (Sempe). Tests were used to analyse the difference between means and Logistic Regression was u sed in sequence. The Random Forest, Logistic Regression and Na & i uml;ve Bayes widgets were used to indicate the robustness of the Machine Learnin g predictive model. The research findings indicated that the formats ‘simplifica tion’, ‘previous choices’ and ‘alert’, employees, did not have an effect in comb ating default. However, when aligned with social norms, messages in the form of ‘past options’ and ‘reminders’ increase the payment of debts. The widgets used i ndicated an excellent fit to the machine learning model. The Random Forest tool attested with superiority that the model is robust and suitable for the predicti ve function. The results of the research provide a contribution to public polici es when they present an effective action to reduce defaults in the payment of ta xes.”
GoianiaBrazilSouth AmericaCyborgsEmerging TechnologiesMachine LearningFederal University Goias
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