Using argumentation in expert’s debate to analyze multi-criteria group decision making method results

Autor: Gang Kou, K. Samuylov, Juan Antonio Morente-Molinera, Francisco Javier Cabrerizo, Enrique Herrera-Viedma
Rok vydání: 2021
Předmět:
Zdroj: Digibug. Repositorio Institucional de la Universidad de Granada
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ISSN: 0020-0255
DOI: 10.1016/j.ins.2021.05.086
Popis: The authors would like to thank the Spanish State Research Agency through the project PID2019-103880RB-I00/AEI/10.13039/501100011033, grants from the National Natural Science Foundation of China (#71725001 and #71910107002) . The publication has also been prepared with the support of the "RUDN University Program 5-100".
Recent multi-criteria group decision making methods focus their analysis on the experts preferences. They do not take into account the reasons why each expert has provided a specific set of preferences. In this paper, a method that introduces novel measures capable of explaining the reasons behind experts decisions is presented. A novel concept, the arguments are presented. They represent the experts have for maintaining a certain position in the debate. Several measures related to the arguments are proposed. These new argumentation measures, along with consensus measures, help us to get a clear idea about how and why a specific resolution has been reached. They help us to determine which is the most influential expert, that is, the expert whose contributions to the debate have inspired the rest. Also, the proposed method allows us to determine which are the arguments that most of the experts have followed. A clear overview about how the debate is evolving in terms of arguments is also provided. The novel presented analysis indicate how the experts change their opinions in every round and what was the reason for it, which changes have occurred between rounds and they also provide global analysis results.
Spanish State Research Agency PID2019-103880RB-I00/AEI/10.13039/501100011033
National Natural Science Foundation of China (NSFC) 71725001 71910107002
RUDN University Program 5-100
Databáze: OpenAIRE