Beyond Traditional Probabilistic Methods in Economics - Kreinovich, Vladik; Thach, Nguyen Ngoc.; Trung, Nguyen Duc.; Van Thanh, Dang.; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2019)
This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important - and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
Keywords: Computational intelligence; Artificial intelligence; Econometrics; Computational Intelligence; Artificial Intelligence; Quantitative Economics