This paper investigates how interacting agents arrive to a consensus or a polarized state. We study the opinion formation process under the effect of a global steering mechanism (GSM), which aggregates the opinion-driven stochastic agent states at the network level and feeds back to them a form of global information. We also propose a new two-layer agent-based opinion formation model, called GSM-DeGroot, that captures the coupled dynamics between agent-to-agent local interactions and the GSM's steering effect. This way, agents are subject to the effects of a DeGroot-like local opinion propagation, as well as to a wide variety of possible aggregated information that can affect their opinions, such as trending news feeds, press coverage, polls, elections, etc. Contrary to the standard DeGroot model, our model allows polarization to emerge by letting agents react to the global information in a stubborn differential way. Moreover, the introduced stochastic agent states produce event stream dynamics that can fit to real event data. We explore numerically the model dynamics to find regimes of qualitatively different behavior. We also challenge our model by fitting it to the dynamics of real topics that attracted the public attention and were recorded on Twitter. Our experiments show that the proposed model holds explanatory power, as it evidently captures real opinion formation dynamics via a relatively small set of interpretable parameters.

翻译：本文研究了相互作用的智能体如何达成共识或进入极化状态。我们探讨了在全局导向机制（GSM）影响下的观点形成过程，该机制在网络层面聚合观点驱动的随机智能体状态，并向其反馈一种全局信息。我们还提出了一种新的双层智能体观点形成模型——GSM-DeGroot，该模型捕捉了智能体间局部交互与GSM导向效应之间的耦合动力学。通过这种方式，智能体既受到类DeGroot局部观点传播的影响，也受到各种可能的聚合信息（如趋势新闻推送、媒体报道、民意调查、选举等）的影响，这些信息可以改变其观点。与标准DeGroot模型不同，我们的模型通过允许智能体以固执的差异化方式对全局信息做出反应，从而允许极化现象的产生。此外，引入的随机智能体状态能够产生可拟合真实事件数据的事件流动力学。我们通过数值探索模型动力学，发现了不同定性行为的状态区域。我们还将模型拟合到吸引公众关注并在Twitter上记录的真实话题动力学中，以此检验模型性能。实验表明，所提出的模型具有解释力，因为它通过一组相对较少的可解释参数明显捕捉到了真实观点形成的动力学过程。