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

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