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Acta Scientific Computer Sciences

Research Article Volume 3 Issue 1

Brain-inspired Decision-making: A Reduction of the Stress and Anxiety Factor in Business Intelligence

Cheryl Ann Alexander1* and Lidong Wang2

1Institute for IT innovation and Smart Health, Mississippi, USA 2Institute for Systems Engineering Research, Mississippi state university, Vicksburg, USA

*Corresponding Author: Cheryl Ann Alexander, Institute for IT Innovation and Smart Health, Mississippi, USA.

Received: November 03, 2020; Published: December 30, 2020

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Abstract

  Decision-making is critical for every manager at all levels. Decision-making is necessary for managers to decide when to increase sales, improve customer service, develop new technologies, or conduct a project. However, decision-making can be subjected to many variables such as stress, emotions, morals, situations, etc. But brain-inspired decision-making (BIDM), using technologies such as artificial neural networks (ANNs) and artificial intelligence (AI), can eliminate indecision and stress, emotion, situations, etc., in the decision-making process. Stress has been discovered to be the most corrosive element in the decision-making process. Stress can cause managers to make the wrong decision in critical situations. Stress can affect decision-making by skewing critical decisions, causing emotional distress in individuals under stress, and leading to bad decision-making. On the other hand, using AI and ANNs to develop BIDM can lead to effective decision-making under the most corrosive stress. In this paper, we have proposed a method to test and evaluate BIDM using 25 engineering managers under stress. A literature review and history of BIDM is also given.

Keywords: Brain-inspired decision-making; Decision-making; Business Intelligence; Artificial Intelligence; Artificial Neural Networks; Cognitive Dynamic System

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Citation

Citation: Cheryl Ann Alexander and Lidong Wang. “Brain-inspired Decision-making: A Reduction of the Stress and Anxiety Factor in Business Intelligence”.Acta Scientific Computer Sciences 3.1 (2021): 35-44.




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Acceptance rate35%
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