模块网络上具有冲突偏好的类选民动力学
Voter-like dynamics with conflicting preferences on modular networks
AI总结：
本文研究含两个对立偏好群体的模块网络类选民模型，结合解析近似、模拟与真实网络检验，揭示模块结构对共识与极化的影响。
摘要：
塑造个人观点的两个主要因素是社会协调和个人偏好，或称个人偏见。为理解这些因素以及交互网络拓扑的作用，我们研究了 Masuda 和 Redner（2011）提出的选民模型扩展，其中智能体被划分为两个具有相反偏好的群体。我们考虑一个包含两个社区的模块图，这些社区反映了偏见分配，从而对认知气泡现象进行建模。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，系统既可能达成共识，也可能进入极化状态，在后一种状态下两个群体稳定在不同的平均观点。总体而言，模块结构会同时提高极化程度及其在参数空间中的范围。当两个群体之间的偏见强度差异较大时，高度坚定的群体能否成功将其偏好观点强加给另一群体，主要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可以忽略不计。我们将简单平均场方法与对近似进行比较，并在真实网络上检验平均场预测的优良性。
英文摘要：
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 has generally 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
