论文标题
意见扩散软件具有战略意见启示和不朋友
Opinion Diffusion Software with Strategic Opinion Revelation and Unfriending
论文作者
论文摘要
我们提出了一个用于社交网络建模和意见扩散过程的新型软件套件。关于社交网络科学的许多研究已经建立了具有静态拓扑的网络。最近，注意力转向发展的网络。尽管用于建模网络拓扑演变和扩散过程的软件正在不断改善，但对代理建模的关注很少。我们的软件旨在强大，模块化和可扩展，可提供模拟动态社交网络拓扑和多维扩散过程的能力，包括非全体动态范式在内的不同风格的代理，以及用于多种代理类型的多样性强化学习（MARL）实验的测试环境。我们还说明了不同的代理建模的价值以及允许战略性不交流的环境。我们的工作表明，两极分化和共识动态以及拓扑聚类效应，可能比以前在个人目标中所知的更多依赖于邻里的观点。
We present a novel software suite for social network modeling and opinion diffusion processes. Much research on social network science has assumed networks with static topologies. More recently, attention has been turned to networks that evolve. Although software for modeling both the topological evolution of networks and diffusion processes are constantly improving, very little attention has been paid to agent modeling. Our software is designed to be robust, modular, and extensible, providing the ability to model dynamic social network topologies and multidimensional diffusion processes, different styles of agent including non-homophilic paradigms, as well as a testing environment for multi-agent reinforcement learning (MARL) experiments with diverse sets of agent types. We also illustrate the value of diverse agent modeling, and environments that allow for strategic unfriending. Our work shows that polarization and consensus dynamics, as well as topological clustering effects, may rely more than previously known on individuals' goals for the composition of their neighborhood's opinions.
