Induced unbalanced linguistic ordered weighted average and its application in multiperson decision making.

Area of Science:

• Decision Sciences
• Fuzzy Logic
• Artificial Intelligence

Background:

• Linguistic variables enhance decision making by using natural language terms.
• Existing aggregation operators often require symmetric and uniformly distributed linguistic terms.
• There is a need to relax these constraints for more flexible linguistic variable aggregation.

Purpose of the Study:

• To introduce the induced unbalanced linguistic ordered weighted average (IULOWA) operator.
• To develop a method for handling unbalanced linguistic terms represented by fuzzy sets.
• To propose a new order-inducing criterion for linguistic terms based on specificity and fuzziness.

Main Methods:

• Development of the induced unbalanced linguistic ordered weighted average (IULOWA) operator.
• Representation of unbalanced linguistic terms using fuzzy sets.
• Proposal of a new order-inducing criterion considering term specificity and fuzziness (uncertainty).
• Application in a multiperson multicriteria decision making model for environmental assessment.

Main Results:

• The IULOWA operator effectively manages unbalanced linguistic terms.
• The new order-inducing criterion assigns relevance based on the uncertainty degree of fuzzy values.
• Demonstration of the operator's behavior in a practical environmental assessment case study.

Conclusions:

• The IULOWA operator provides a flexible approach to linguistic decision making.
• The proposed method accommodates linguistic terms with varying degrees of uncertainty.
• The operator is suitable for complex decision making scenarios, such as multiperson multicriteria environmental assessments.