Version 1
: Received: 24 July 2019 / Approved: 30 July 2019 / Online: 30 July 2019 (03:48:37 CEST)
How to cite:
Hafezi, R. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments. Preprints2019, 2019070338. https://doi.org/10.20944/preprints201907.0338.v1
Hafezi, R. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments. Preprints 2019, 2019070338. https://doi.org/10.20944/preprints201907.0338.v1
Hafezi, R. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments. Preprints2019, 2019070338. https://doi.org/10.20944/preprints201907.0338.v1
APA Style
Hafezi, R. (2019). How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments. Preprints. https://doi.org/10.20944/preprints201907.0338.v1
Chicago/Turabian Style
Hafezi, R. 2019 "How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments" Preprints. https://doi.org/10.20944/preprints201907.0338.v1
Abstract
Decision-makers are concerned with the inherent complexity of the modern world's markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21st century's more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This paper proposed that artificial intelligence can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective purified input features subset refer to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improves prediction accuracy and reliability.
Keywords
prediction; futures studies; complex environment; machine learning data mining
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
