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.

如何选择最优策略与对手竞争是多属性决策（MADM）领域的热点问题之一。然而，大多数MADM方法不仅忽略了竞争对手的行为特征，而且仅仅得到一个简单的策略排名结果，并不能反映每个策略的可行性。为了克服这些缺点，提出了一种基于混合动态专家权重确定模型的两人非合作矩阵博弈方法来应对q内复杂的竞争策略群决策问题。-梯级正畸模糊环境。在开始时，一个新的动态专家权重计算模型，同时考虑目标个体和主观评价信息，通过集成可信度分析规模和豪斯多夫距离量度的优势为设计q -rung orthopair模糊集合（q-ROFS）。上述模型得到的专家权重会随着专家提供的主观评价信息而变化，更接近实际情况。随后，制定了一个两人非合作模糊矩阵博弈来确定竞争者的最优混合策略，该博弈可以呈现每种竞争策略的具体可行性和发散度，并且受策略数量的影响较小。最后，通过一个说明性的例子，进行了一些比较分析和敏感性分析，以验证所提出方法的合理性和有效性。实验结果表明，所提出的方法作为一种高效、低计算复杂度和小的计算负担的CSGDM方法。