偏见的意见动态：当魔鬼在细节中
Biased Opinion Dynamics: When the Devil Is in the Details
摘要：
我们调查的意见动态多智能体网络时，偏向两种可能的意见之一存在，例如，反映现状与优越的替代品。从所有代理共享代表现状的初始意见开始，系统逐步发展。在每一步中，一个随机选择的代理人以一定的概率$α$接受上级意见，并以概率$1 - α$遵循一个基本的更新规则，根据邻居的意见修改自己的意见。我们分析了两个著名的更新规则，即多数和选民的收敛过程。我们提出的框架具有丰富的结构，拓扑结构和底层更新规则之间的相互作用不明显。例如，对于选民规则，我们表明，收敛的速度没有显着的依赖于底层的拓扑结构，而图片完全改变下的多数规则，网络密度产生负面影响的收敛。我们相信，我们提出的模型是在同一时间简单，丰富，模块化，提供偏见之间的相互作用的数学表征，潜在的意见动态，在一个统一的设置和社会结构。
英文摘要：
We investigate opinion dynamics in multi-agent networks when a bias toward one of two possible opinions exists; for example, reflecting a status quo vs a superior alternative. Starting with all agents sharing an initial opinion representing the status quo, the system evolves in steps. In each step, one agent selected uniformly at random adopts the superior opinion with some probability $α$, and with probability $1 - α$ it follows an underlying update rule to revise its opinion on the basis of those held by its neighbors. We analyze convergence of the resulting process under two well-known update rules, namely majority and voter. The framework we propose exhibits a rich structure, with a non-obvious interplay between topology and underlying update rule. For example, for the voter rule we show that the speed of convergence bears no significant dependence on the underlying topology, whereas the picture changes completely under the majority rule, where network density negatively affects convergence. We believe that the model we propose is at the same time simple, rich, and modular, affording mathematical characterization of the interplay between bias, underlying opinion dynamics, and social structure in a unified setting.
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。
