Using Automated Decision-Making and Macroeconomic Data Flows for Governance Resilience

Pub Date : 2023-10-01 DOI:10.14207/ejsd.2023.v12n3p38

Florina Bran, Dumitru Alexandru Bodislav, Cătălin Romeo Crețu, Irina Elena Petrescu

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Abstract

This study is based on an algorithm created for the American stock market to improve closed investment funds' efficiency, which had as a secondary output a suitable and sustainable model that could be scaled to fit solutions for problems with automated decision making at the government level, similar to a fundamental business intelligence solution (that adheres to similar procedures as the IBM Cognos workflow), which provides a solution in creating the best sustainable model. The model is based on businesses that are listed on the NASDAQ and LSE since these markets offer the finest examples of transparency and accurate audits. It also replicates the economic sectors that make up a fictitious national economy. In order to provide a better perspective and to report the main findings of this study, we also created an overview to analyze the development of B.A.D.E.M., an indicator that simulates a national economy, which in 2023 reached its tenth version, and HSS, a micro-indicator that simulates the healthcare sector. Keywords: artificial intelligence, automated decision making, business intelligence, economic growth, resilience

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使用自动化决策和宏观经济数据流实现治理弹性

本研究基于为美国股票市场创建的一种算法，该算法旨在提高封闭式投资基金的效率，该算法的次要输出是一个合适且可持续的模型，该模型可以扩展以适应政府层面自动化决策问题的解决方案，类似于基本的商业智能解决方案(遵循与IBM Cognos工作流类似的过程)，它提供了创建最佳可持续模型的解决方案。该模型基于在纳斯达克(NASDAQ)和伦敦证交所(LSE)上市的企业，因为这些市场提供了透明度和准确审计的最佳范例。它还复制了构成虚拟国民经济的经济部门。为了提供更好的视角并报告本研究的主要发现，我们还创建了一个概述来分析B.A.D.E.M的发展，B.A.D.E.M是一个模拟国民经济的指标，在2023年达到了第10个版本，HSS是一个模拟医疗保健行业的微观指标。关键词:人工智能，自动化决策，商业智能，经济增长，弹性

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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