基于智能体的惯例颠覆与收敛模型中的流动性、记忆与网络结构
Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence
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中文总结 AI 辅助
该研究构建智能体基模型，探究流动性、记忆、网络拓扑对惯例颠覆阈值的影响，发现多数配置下颠覆必然发生，还建立预测模型估计收敛时间，拓展了经典颠覆点研究。
中文摘要 AI 辅助
颠覆点动力学描述了在何种关键条件下，坚定的少数群体能够推动整个群体放弃既有惯例，转而采用新惯例。我们提出了一个透明的智能体基模型来模拟这一过程，模型中智能体持有两种行为状态之一，且存在一个具有坚定立场的移动少数群体试图颠覆现有惯例。我们的目标是探究局部流动性、有限智能体记忆以及网络拓扑如何共同影响颠覆阈值。通过自定义智能体基模拟框架，我们发现，在诸多配置下，颠覆实际上是必然的：只要有足够时间，群体总会收敛到少数群体的状态。这一观察结果促使我们开展补充分析，聚焦于收敛的速度而非其可行性。我们引入了一个统一预测模型，该模型能准确估计结构与行为参数如何决定完全采纳新惯例所需的时间，结果显示流动性是主要的加速因素，而记忆和连通性则以系统方式调节收敛过程。这些结果将经典颠覆点研究拓展至，不仅将结构与行为因素与惯例变更的可能性关联，还将其与变更发生的时间尺度关联。尽管我们以类似惯例的二元行为采纳来构建模型，但相同机制也适用于规范变更及其他类似传染的社会过程。
英文摘要
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 minority attempts to overturn the incumbent convention. Our goal was to examine how localized mobility, bounded agent memory, and network topology jointly influence the tipping threshold. Using a custom agent-based simulation framework, we found that in many configurations, tipping becomes effectively inevitable: given sufficient time, the population always converges to the minority state. This observation motivated a complementary analysis focused on the pace of convergence rather than its feasibility. We introduce a unified predictive model that accurately estimates how structural and behavioral parameters determine the time required for complete adoption, showing that mobility is the dominant accelerator while memory and connectivity modulate convergence in systematic ways. Together, these results extend classical tipping-point research by linking structural and behavioral factors not only to the likelihood of convention change but also to the timescale on which it unfolds. While we frame the model in terms of convention-like binary behavioral adoption, the same mechanisms bear on norm change and other contagion-like social processes.
