发表机构
Indian Institute of Technology (BHU)（印度理工大学（贝拿勒斯印度教大学））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在Abrams–Strogatz模型基础上，针对Erdős–Rényi随机网络提出带动态边权重的语言竞争模型，确定相边界、识别异常持续区域并开展标度研究，还探讨了模型扩展方向。
AI 中文摘要
本文对Erdős–Rényi随机网络上的语言竞争动力学开展计算研究，在基础Abrams–Strogatz模型的基础上做出两项新贡献：（i）提出动态边权重机制，通过加性增量Δ强化同属少数群体使用者间的社会联系；（ii）构建基于概率的智能体框架，通过加权多数规则调控语言转换。在二维参数空间（p, Δ）中确定区分优势 regime 与共存 regime 的相边界，其中p为网络连接概率。我们进一步在预测的优势区域内识别出异常持续区域，将其归因于孤立少数群体使用者集群的形成。对网络规模N∈{50,100,250,500,1000}的标度研究显示，平均集群规模随N增大而减小，且相边界随随机噪声增加而扩散。最后，我们探讨向三方双语模型、异质威望/波动性的扩展，以更准确地刻画真实社会语言学接触场景。
英文摘要
This paper presents a computational study of language competition dynamics on Erdős--Rényi random networks, extending the foundational Abrams--Strogatz model through two novel contributions: (i) a dynamic edge-weighting mechanism that reinforces social ties between co-minority speakers by an additive increment $Δ$, and (ii) a probabilistic agent-based framework governing language switching via a weighted majority rule. Phase boundaries separating the dominance and coexistence regimes are identified across a two-dimensional parameter space $(p, Δ)$, where $p$ denotes the network connectivity probability. We further characterise anomalous persistence zones within predicted dominance regions, attributing them to the formation of isolated minority speaker clusters. Scaling study across network sizes $N \in \{50, 100, 250, 500, 1000\}$ reveal that average cluster size decreases with $N$ and that phase boundaries diffuse with increasing stochastic noise. Finally, we discuss extensions to a tripartite bilingual model and heterogeneous prestige/volatility to more faithfully capture real sociolinguistic contact scenarios.
