Confidence Level based on Logarithmic Einstein Aggregation Approach for Fuzzy Matrix Games with Multi-Expert Evaluation

Abstract: In real world situations decision-making usually depends on the opinions of multiple experts with different level of knowledge and confidence. Namarta et al. [18] introduced a method for solving intuitionistic fuzzy matrix games using Intuitionistic Fuzzy Einstein Interactive Weighted Averaging (IFEIWA) Operator to aggregate the weightage of multiple experts for payoffs. However, a significant limitation of this method is that they assume all the experts have 100% confidence in their payoffs. But in real world competition scenarios experts have varying degrees of familiarity with specific strategies. For example; they might be very experienced yet still feel unsure about particular strategy. To address this limitation, this paper proposes a significant method for solving intuitionistic fuzzy game problems by introducing CLIFEWA (Confidence Logarithmic Intuitionistic Fuzzy Einstein Weighted Averaging) Operator. To show the superiority, validity and practical applicability of proposed method illustrative example has been given.

Keywords: Intuitionistic Fuzzy Sets, Fuzzy Matrix Game, Aggregation Operator