Zimmaro Filippo, Contucci Pierluigi, Kertész János
Department of Computer Science, University of Pisa, 56126 Pisa, Italy.
Department of Mathematics, University of Bologna, 40126 Bologna, Italy.
Entropy (Basel). 2023 May 24;25(6):838. doi: 10.3390/e25060838.
Two of the main factors shaping an individual's opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases' strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.
塑造个人观点的两个主要因素是社会协调和个人偏好，或个人偏见。为了理解这些因素以及互动网络拓扑结构的作用，我们研究了增田和雷德纳（2011年）提出的选民模型的一个扩展，其中主体被分为具有相反偏好的两类群体。我们考虑一个具有两个社区的模块化图，这两个社区反映了偏见分配，对认知泡沫现象进行建模。我们通过近似解析方法和模拟来分析这些模型。根据网络和偏见强度的不同，系统可能会达成共识或进入极化状态，即两类群体稳定在不同的平均观点上。模块化结构通常会增加极化程度及其在参数空间中的范围。当两类群体之间的偏见强度差异很大时，非常坚定的群体将其偏好观点强加给另一群体的成功程度在很大程度上取决于后一群体的隔离程度，而对前一群体拓扑结构的依赖性则可以忽略不计。我们将简单的平均场方法与对近似方法进行比较，并在一个真实网络上检验平均场预测的准确性。
