In real life, there are many complex multiple attribute group decision making (MAGDM) problems with high decision risk and uncertainty. The decision-making process of the complex MAGDM can encounter the following three problems: (1) Since different experts have different knowledge structures and interests, they master different individual attribute information of alternatives. (2) Experts may have different consensus degrees for alternatives under different attributes. (3) For some alternatives, the experts can not make an immediate decision in the actual decision-making process. The experts need much more information to decide on these alternatives in the subsequent decision step. To solve the problems as mentioned above, we propose sequential three-way multiple attribute group decision making (STWMAGDM) with individual attributes by introducing sequential three-way decisions. Meantime, we construct a multilevel granular structure based on the consensus degree of attributes. Further, at each granularity level, the experts need to reach consensus before deducing decision results. For improving the consensus reaching process, we take into account the social influence among experts with the aid of opinion dynamics. In this case, we construct social networks based on the similarity of experts and the amount of attribute information mastered by experts to describe the social influence. Meanwhile, we modify the model of opinion dynamics by introducing the interaction willingness of experts and establish the corresponding adjustment rules of interaction willingness. Finally, we use two diagnosis examples of breast cancer and heart disease to explain our model in detail. In order to verify the effectiveness of our method, we also perform the corresponding comparative experiments and sensitivity analyses.

在现实生活中，存在许多具有高决策风险和不确定性的复杂多属性组决策（MAGDM）问题。复杂MAGDM的决策过程会遇到以下三个问题：（1）由于不同的专家具有不同的知识结构和兴趣，因此他们掌握了替代方案的不同个体属性信息。（2）对于不同属性下的替代方案，专家可能具有不同的共识度。（3）对于某些替代方案，专家们无法在实际决策过程中立即做出决策。专家需要更多信息来在后续决策步骤中决定这些替代方案。为了解决上述问题，通过引入顺序三向决策，我们提出了具有单个属性的顺序三向多属性组决策（STWMAGDM）。同时，我们基于属性的共识程度构造了一个多层粒度结构。此外，在每个粒度级别上，专家都需要在得出决策结果之前达成共识。为了改善共识达成过程，我们借助观点动态考虑了专家之间的社会影响。在这种情况下，我们基于专家的相似性和专家掌握的描述社会影响力的属性信息量来构建社交网络。同时，通过引入专家的互动意愿来修改意见动态模型，并建立相应的互动意愿调整规则。最后，我们使用两个乳腺癌和心脏病的诊断实例来详细解释我们的模型。为了验证我们方法的有效性，我们还进行了相应的对比实验和灵敏度分析。