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

TL;DR

This paper reviews the development, key elements, and challenges of distributed linguistic representations in decision making, emphasizing their role in data science and explainable AI.

Contribution

It provides a comprehensive taxonomy and analysis of distributed linguistic representations, highlighting their applications and future research challenges.

Findings

Taxonomy of distributed linguistic representations

Analysis of key elements like distance measurement and aggregation methods

Discussion on challenges and future directions in data science and XAI

Abstract

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.