Distributed Linguistic Representations in Decision Making: Taxonomy, Key
  Elements and Applications, and Challenges in Data Science and Explainable
  Artificial Intelligence
  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.
