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.

竞争力被定义为吸引和维持产业以促进公民福祉持续改善的成功率，一直是地区和国家长期追求的目标。当今技术（尤其是电信技术）的快速进步，为决策者和政策制定者在全球范围内制定有效且高效的解决方案提出了挑战。在此背景下，人工智能、机器学习和深度学习的最新发展为描述、分析和表示系统环境中的复杂现象开辟了新的途径。本文提出了一个模型，使用神经网络来预测使用公共支出变量的竞争基准的行为。基于神经网络方法的控制理论具有一些优点，例如在解决问题时考虑现象的动态本质，并允许以简单的方法实现控制块。本文建立了一个神经网络模型，以统计上合理的方法将控制、管理和系统理论联系起来，连接两组变量，为实现资源优化配置的扩展开辟了道路。