From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation |
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| Affiliation: | 1. Unidad Académica Profesional Tianguistenco, Universidad Autónoma del Estado de México, Paraje el Tejocote, San Pedro Tlaltizapan, Santiago Tianguistenco, Mexico;2. Facultad de Contaduría y Administración, Universidad Autónoma del Estado de México, Cerro de Coatepec S/N, Ciudad Universitaria, Toluca de Lerdo, Mexico;3. Center for Technology in Government and Rockefeller College of Public Affairs and Policy University at Albany, State University, New York, United States of America & Universidad de las Americas Puebla, Cholula, Puebla, Mexico;1. Department of Human and Social Sciences, University of Naples L''Orientale, Italy;2. Department of Economics, University of Rome Roma Tre, Italy;3. Interuniversity Department of Regional and Urban Studies and Planning, University of Turin, Italy;4. Department of law, economics, politics and modern languages, Libera Università degli Studi Maria Ss. Assunta di Roma, Italy;5. Department of Politics, Birkbeck College, University of London, UK;1. Universidade Federal do Parana, Department of Science and Information Management, 632 Prefeito Lothário Meissner Av, Jardim Botanico, Curitiba, PR, Brazil;2. Escola de Administração de Empresas de São Paulo da Fundação Getulio Vargas (FGV EAESP), Department of Technology and Data Science, 2029 9 de Julho Avenue, Bela Vista, São Paulo, SP, Brazil;3. HEC Montréal, Department of International Business, 3000 chemin de la Cote de Sainte Catherine, Montréal, Quebec H3T-2A7, Canada |
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| Abstract: | Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AI-based approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques. |
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