A machine learning model of national competitiveness with regional statistics of public expenditure

Artemisa Zaragoza-Ibarra, Gerardo G. Alfaro-Calderón, Víctor G. Alfaro-García (), Fernando Ornelas-Tellez and Rodrigo Gómez-Monge
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Artemisa Zaragoza-Ibarra: Universidad Michoacana de San Nicolás de Hidalgo
Gerardo G. Alfaro-Calderón: Universidad Michoacana de San Nicolás de Hidalgo
Víctor G. Alfaro-García: Universidad Michoacana de San Nicolás de Hidalgo
Fernando Ornelas-Tellez: Universidad Michoacana de San Nicolás de Hidalgo
Rodrigo Gómez-Monge: Universidad Michoacana de San Nicolás de Hidalgo

Computational and Mathematical Organization Theory, 2021, vol. 27, issue 4, No 4, 468 pages

Abstract: Abstract Competitiveness, defined as the rate of success in attracting and maintaining industries to foster the sustained improvement in citizens’ wellbeing, has been a long-pursued goal for regions and nations. Today’s rapid advancements in technology, especially in telecommunications, open challenges for decision and policy makers to generate effective and efficient solutions in a global scenario. In this context, the latest developments in artificial intelligence, machine learning and deep learning open new paths for describing, analyzing, and representing complex phenomena in systemic environments. This paper presents a model using a neural network to predict the behavior of competitive benchmarks using public expenditure variables. The theory of control, in which the neural network approach is based, offers some advantages such as solving the problem while considering the dynamic nature of the phenomenon and allowing control blocks to be implemented in a straightforward method. The present paper establishes a neural network model that links control, administration, and systems theories in a statistically sound approach that connects both sets of variables, opening the path for extensions that allow optimal allocation of resources.

Keywords: Regional development; Public expenditure; Neural networks; Decision-making (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10588-021-09338-9

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