The formation of public opinions is a complex phenomenon that revolves around the aggregation of individuals’ beliefs. To accurately capture this phenomenon, one needs to build links from individualistic experiences to personal beliefs, which evolve in a social space through information exchange and belief revision. Despite many efforts to model opinion dynamics, the role of personal experiences and beliefs has often been overlooked. In this paper, we address this issue and propose an agent-based model in a social network. We explicitly model belief acquisition as a learning process from experiences that take the form of local data sets. Agents interact through a social network and update their beliefs based on how accurately the belief reflects experiences. Through iterations of interactions, the agents are able to arrive at a unified belief. We then focus on the accuracy of the personal beliefs during their evolution and the impact of the social network structure. On a micro-level, we investigate positional attributes such as the centrality of nodes that affect belief accuracy. On a macro-level, we investigate structural features that affect the overall performance. We then investigate a method to intervene in opinion formation through expert agents. Experiments are performed on real-world and synthetic data sets, which validate a number of important structural insights.

舆论的形成是一个复杂的现象，围绕着个人信念的聚集而发生。为了准确地捕捉这种现象，需要建立从个人主义经验到个人信念的链接，这些信念通过信息交换和信念修正在社会空间中发展。尽管为建模意见动态做出了许多努力，但个人经验和信念的作用经常被忽略。在本文中，我们解决了这个问题并在社交网络中提出了一种基于代理的模型。我们明确地将信念获取建模为来自采用本地数据集形式的经验的学习过程。代理人通过社交网络进行交互，并根据信念反映体验的准确性来更新其信念。通过交互的迭代，代理可以达成统一的信念。然后，我们关注个人信念在其演变过程中的准确性以及社交网络结构的影响。在微观层面上，我们研究了位置属性，例如影响信念准确性的节点的中心性。在宏观层面上，我们研究了影响整体性能的结构特征。然后，我们研究通过专家代理干预意见形成的方法。实验是在现实世界和综合数据集上进行的，这些数据集验证了许多重要的结构见解。我们研究了影响整体性能的结构特征。然后，我们研究通过专家代理干预意见形成的方法。实验是在现实世界和综合数据集上进行的，这些数据集验证了许多重要的结构见解。我们研究了影响整体性能的结构特征。然后，我们研究通过专家代理干预意见形成的方法。实验是在现实世界和综合数据集上进行的，这些数据集验证了许多重要的结构见解。