Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Decisions, having the possibility to have important consequences on people's lives, are made every day. For this reason, there exists a great need for making good decisions in today's world. Because consistency has been assumed to be a rationality measure, inconsistent judgments are considered to lead to bad decisions. This study aims to introduce a new granular-based approach to deal with consistency, concretely multiplicative consistency, of reciprocal preference relations in decision-making. Firstly, we present a process of an optimal distribution of information granularity maximizing the consistency of the reciprocal preference relation. Secondly, based on it, we develop an interactive procedure for multiplicative consistency improvement with the implication of the decision maker. Several numerical examples are conducted to validate the effectiveness of this granular-based approach.
Recommended Citation
Cabrerizo, Francisco; Kaklauskas, Artūras; Pérez, Ignacio Javier; and Herrera-Viedma, Enrique, "A Granular-Based Approach to Address Multiplicative Consistency of Reciprocal Preference Relations in Decision-Making" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/da/soft_computing/2
A Granular-Based Approach to Address Multiplicative Consistency of Reciprocal Preference Relations in Decision-Making
Online
Decisions, having the possibility to have important consequences on people's lives, are made every day. For this reason, there exists a great need for making good decisions in today's world. Because consistency has been assumed to be a rationality measure, inconsistent judgments are considered to lead to bad decisions. This study aims to introduce a new granular-based approach to deal with consistency, concretely multiplicative consistency, of reciprocal preference relations in decision-making. Firstly, we present a process of an optimal distribution of information granularity maximizing the consistency of the reciprocal preference relation. Secondly, based on it, we develop an interactive procedure for multiplicative consistency improvement with the implication of the decision maker. Several numerical examples are conducted to validate the effectiveness of this granular-based approach.
https://aisel.aisnet.org/hicss-56/da/soft_computing/2