一句话：提出带确认偏差的多智能体信念更新形式模型，刻画社会网络极化最终消失或持续的条件。
本文研究多智能体社会网络中的极化现象。作者在 Esteban-Ray 极化度量与 DeGroot 社会学习模型基础上，引入确认偏差：智能体按影响图更新信念时，会折扣观点不同者的意见。论文证明若影响图强连通，极化最终收敛到零；对正则对称 circulation，给出所有智能体收敛的唯一信念值。更有意义的结论是，在自然假设下，若极化不消失，则要么存在不连通的智能体子群，要么有智能体对他人的影响大于其受影响程度。作者还证明弱连通图下确认偏差未必使极化消失，并讨论模型与经典 DeGroot 模型的关系，最后用疫苗极化等模拟案例验证理论。
We describe a model for polarization in multi-agent systems based on Esteban and Ray's standard family of polarization measures from economics. Agents evolve by updating their beliefs (opinions) based on an underlying influence graph, as in the standard DeGroot model for social learning, but under a confirmation bias; i.e., a discounting of opinions of agents with dissimilar views. We show that even under this bias polarization eventually vanishes (converges to zero) if the influence graph is strongly-connected. If the influence graph is a regular symmetric circulation, we determine the unique belief value to which all agents converge. Our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced. We also prove that polarization does not necessarily vanish in weakly-connected graphs under confirmation bias. Furthermore, we show how our model relates to the classic DeGroot model for social learning. We illustrate our model with several simulations of a running example about polarization over vaccines and of other case studies. The theoretical results and simulations will provide insight into the phenomenon of polarization.
@article{paperbot2231,
title = {A Formal Model for Polarization under Confirmation Bias in Social Networks},
author = {Mário S. Alvim and Bernardo Amorim and Sophia Knight and Santiago Quintero and Frank Valencia},
journal = {Logical Methods in Computer Science},
volume = {Volume 19, Issue 1},
year = {2023},
doi = {10.46298/lmcs-19(1:18)2023}
}