A q-rung orthopair fuzzy non-cooperative game method for competitive strategy group decision-making problems based on a hybrid dynamic experts’ weight determining model

• Published in: Complex & intelligent systems 2021-12, Vol.7 (6), p.3077-3092
• Main Authors: Yang, Yu-Dou, Ding, Xue-Feng
• Format: Article
• Language: English
• Subjects: Business competition; Competition; Complexity; Computational Intelligence; Data Structures and Information Theory; Decision making; Engineering; Evaluation; Feasibility; Fuzzy logic; Fuzzy sets; Game theory; Games; Metric space; Original Article; Strategy
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-021-00475-x

How to select the optimal strategy to compete with rivals is one of the hottest issues in the multi-attribute decision-making (MADM) field. However, most of MADM methods not only neglect the characteristics of competitors’ behaviors but also just obtain a simple strategy ranking result cannot reflect the feasibility of each strategy. To overcome these drawbacks, a two-person non-cooperative matrix game method based on a hybrid dynamic expert weight determination model is proposed for coping with intricate competitive strategy group decision-making problems within q -rung orthopair fuzzy environment. At the beginning, a novel dynamic expert weight calculation model, considering objective individual and subjective evaluation information simultaneously, is devised by integrating the superiorities of a credibility analysis scale and a Hausdorff distance measure for q -rung orthopair fuzzy sets ( q -ROFSs). The expert weights obtained by the above model can vary with subjective evaluation information provided by experts, which are closer to the actual practices. Subsequently, a two-person non-cooperative fuzzy matrix game is formulated to determine the optimal mixed strategies for competitors, which can present the specific feasibility and divergence degree of each competitive strategy and be less impacted by the number of strategies. Finally, an illustrative example, several comparative analyses and sensitivity analyses are conducted to validate the reasonability and effectiveness of the proposed approach. The experimental results demonstrate that the proposed approach as a CSGDM method with high efficiency, low computation complexity and little calculation burden.