Special Issue on Hybrid Data and Knowledge Driven Decision Making under Uncertainty
• 大类 : 工程技术 - 2区
• 小类 : 计算机：信息系统 - 1区
Data-driven and domain knowledge-driven approaches and techniques have been widely used in decision making. They form a hot research topic owing to their importance and effectiveness in addressing aspects of uncertainty and incompleteness. information explosion resulting via the spread of digital technologies impacts the ways we can study and understand the dynamics of socio-economic-political systems by increasing the variety, availability, and complexity of the data available to both qualitative and quantitative research scientists. These new information sources can importantly support integrated approaches that can be more effective than either approach. Accordingly, there are many challenges and open research problems to be explored as well as many issues to be addressed.
This special issue will focus on integration of both techniques for different decision-making problems under uncertainty and incompleteness and aim at providing an opportunity for collecting some excellent work in the above common research areas including compiling the latest research, development and practical experiences as well as up-to-date issues; reviewing accomplishments, assessing future directions and challenges in this field.
Accordingly, we would like to invite you to submit your contribution to the development of algorithms and systems for modelling, designing and fusing information, particularly if they represent a practical application of the integration of both quantitative and qualitative formalisms and modelling approaches to handle multiple and heterogonous information (numerical, symbolic, logical, probabilistic, fuzzy or possibilistic, etc.) for decision making purposes in science, engineering, business, and education. Contributions to investigating, reviewing and assessing the principles, explanation, and strategies on how humans represent and use incomplete and uncertain data and knowledge from a cognitive science perspective are also allowed.
Manuscripts must be original andnotpreviously published or currently submitted for journal publication elsewhere, mustfit this special issue themeand must clearly delineate the role ofintegrationof quantitative and qualitative methods and algorithms for decision making purpose.