Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
This is an incremental review paper that synthesizes existing methods for researchers in decision making and AI, without introducing new techniques.
The paper provides a taxonomy and review of distributed linguistic representations for modeling uncertainty in linguistic decision making, discussing key elements like distance measurement and aggregation methods, and identifies future challenges in data science and explainable AI.
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.