Special issue: Decision, risk analytics and data intelligence
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EDITORIAL
Xiaozhe ZHAO, Desheng WU
Special issue: Decision, risk analytics and data intelligence
© Higher Education Press 2020
The intelligent decision-making domain is a fast-growing area of research that integrates various aspects of computer science and information systems, which includes risk analytics, intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using intelligent decision-making often have a significant impact on decisionmaking processes in government, industry, business, and academia in general. This is particularly applied in finance, accounting, healthcare, computer networks, real-time safety monitoring, and crisis response systems. Moreover, intelligent decision-making is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics (Tweedale et al., 2016). Intelligent decision-making has incorporated new capabilities that mimic and extend human cognitive abilities in some manner. In today’s environment, information needed for decision-making tends to be distributed (Phillips-Wren and Forgionne, 2002). Networks exist within and outside of enterprises, home, the military, government, and national boundaries. Information is segmented for logistical and security reasons onto different machines, databases, and systems. Informed decisions may need integration of information from various internal or external sources. As enterprises become multinational, information tends to be distributed geographically across national boundaries. The speed of communication in the 21st century requires fast response to be competitive, so the integration of information for decision making needs to be fast and accurate. Technology can help decisions that are complex and semi-structured by integrating information flow with analysis using both conventional and artificial intelligence techniques (Tweedale et al., 2016). Intelligent decision-making systems have possibilities to convert human decision-making by integrating information technology, artificial intelligence and system engineering research areas. The field of intelligent decision-making and risk analytics is developing rapidly. The purpose of this special issue is to provide exquisite research methodologies in intelligent decision-making and risk analytics. This special issue contains 10 research papers. These papers focus on recent advances topics of intelligent decision-making and risk analytics including non-probabilistic scenarios and decision analysis coupling, credit and financial risk assessment, quality function deployment (QFD) with Pythagorean fuzzy sets (PFSs), real-time decision making, system dynamic simulation, intelligent knowledge management, network data envelopment analysis, and application of big data in sustainable
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