基于主体的惯例转变模型：移动性、记忆与网络结构的影响
原标题：Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence
AI 摘要
该研究提出一个基于主体的模拟框架，探讨移动性、有限记忆和网络拓扑如何共同影响少数派推动社会惯例转变的临界阈值。研究发现，在许多配置下，惯例转变几乎不可避免，因此重点转向收敛速度，并建立统一预测模型，表明移动性是主要加速因素，而记忆和连通性以系统方式调节收敛。该模型不仅适用于惯例转变，也适用于规范变化等传染类社会过程。
正文节选
Computer Science > Multiagent Systems Title:Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence View PDF HTML (experimental) Abstract:Tipping-point dynamics describe the critical conditions under which a committed minority drives a population to abandon an established convention in favor of a new one. We present a transparent agent-based model of this process, in which agents hold one of two behavioral states and a mobile committed mino