Data-Driven Group Decision-Making

Topic Information

Dear Colleagues,

With the existence of both global integration and reverse integration, as well as the rapid development of Internet technology and communication technology, the environment of governments, enterprises and other organizations is becoming more complex. Currently, there are major problems with group decision-making that require solving with the help of group wisdom. As the basic form of decision-making in social activities, group decision-making takes into account a variety of interests and overcomes the shortcomings of individual knowledge, information and ability. It has been widely used in many fields, such as emergency decision-making in major emergencies, major strategic decision-making in the government, logistics and supply chain management decision-making, etc. However, with the rapid development and deep integration of information technology, a new chapter of digital life has begun and we have been placed into the era of big data. Because of the characteristics of large volume, diversity, dynamic and low-value density of big data, dynamic and social group decision-making in a big data environment creates new challenges in the decision-making field, which are worthy of further exploration. Consistent or compromised schemes are more effective than the traditional decision-making method. Therefore, applying data-driven technology to carry out more research and innovation in group decision-making has extensive theoretical and practical significance. This Special Issue's aim is to solicit the latest research and review articles on group decision-making driven by data. We hope to combine the two studies, including new theoretical methods based on existing theories. We welcome new ideas to explore the future development direction of intelligent group decision-making. You are invited to provide original contributions of novel theories, methods and applications to the problems of data-driven group decision-making research. Potential topics：

• Application of robust optimization method in group decision-making; 
• Application of data driven method in group decision-making; 
• Application of machine learning method in group decision-making; 
• Analysis and application of data-driven group decision-making behavior; 
• Clustering method of preference data in group decision-making; 
• Multistage dynamic group decision-making method; 
• Data-driven preference learning method; 
• Data-driven preference clustering method; 
• Large-group emergency decision based on decision-maker behavior data mining; 
• Data collection and extraction in online reviews; 
• Data mining for feature learning, classification, regression and clustering.

Prof. Dr. Shaojian Qu
Prof. Dr. Ying Ji
Dr. M. Faisal Nadeem
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800	Submit
Digital digital	-	3.1	2021	23.6 Days	CHF 1000	Submit
Information information	2.4	6.9	2010	14.9 Days	CHF 1600	Submit
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600	Submit
Systems systems	2.3	2.8	2013	17.3 Days	CHF 2400	Submit

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Published Papers (15 papers)