Linguistic scale consistency issues in multi-granularity decision making contexts

The symbolic model based on the linguistic scale has been widely used to represent linguistic knowledge to deal with various linguistic decision problems. However, linguistic scales with different granularity may yield inconsistent decision outcomes in the linguistic decision making. Thus, this paper systematically studies the linguistic scale consistency issues in multi-granularity decision making contexts. We first define the concepts of the consistent multi-granularity representation, consistent multi-granularity aggregation and consistent multi-granularity ranking. After that, we analytically present a necessary and sufficient condition to guarantee the consistent multi-granularity representation and a sufficient condition to characterize the intrinsic mechanism of the consistent multi-granularity aggregation. Then, an attitude-based linguistic representation method (ALRM) is proposed to improve the consistent multi-granularity ranking. Finally, a detailed numerical analysis and simulation experiments are presented to show the advantages of the ALRM over the traditional linguistic approach. These results will provide new insights into the use of linguistic scales in the linguistic decision making. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper systematically studies the linguistic scale consistency issues in multi-granularity decision making contexts. Necessary and sufficient conditions for consistent multi-granularity representation and a sufficient condition for the consistent multi-granularity aggregation are analytically presented. An attitude-based linguistic representation method (ALRM) is proposed to improve the consistent multi-granularity ranking, showing advantages over traditional linguistic approach in detailed numerical analysis and simulation experiments.