This work extends the recent opinion dynamics model from Cheng et al., emphasizing the role of group pressure in consensus formation. We generalize the findings to incorporate social influence algorithms with general time-varying, opinion-dependent weights and multidimensional opinions, beyond bounded confidence dynamics. We demonstrate that, with uniformly positive conformity levels, group pressure consistently drives consensus and provide a tighter estimate for the convergence rate. Unlike previous models, the common public opinion in our framework can assume arbitrary forms within the convex hull of current opinions, offering flexibility applicable to real-world scenarios such as opinion polls with random participant selection. This analysis provides deeper insights into how group pressure mechanisms foster consensus under diverse conditions.

翻译：本研究拓展了Cheng等人近期提出的意见动力学模型，重点探讨群体压力在共识形成中的作用。我们将研究结果推广至包含具有一般时变、意见依赖性权重及多维意见的社会影响力算法，超越了传统有界置信动力学的范畴。研究表明，在一致性水平均匀为正的条件下，群体压力能够持续驱动共识形成，并提供了更精确的收敛速率估计。与既有模型不同，本框架中的公共意见可呈现为当前意见凸包内的任意形式，这种灵活性适用于现实场景（如随机选择参与者的民意调查）。该分析为理解群体压力机制如何在多样化条件下促进共识形成提供了更深入的见解。