从时间演化地图推断的语言变化的统计物理学
Statistical physics of language change inferred from time evolving maps
- School of Mathematics and Physics, University of Portsmouth（朴茨茅斯大学数学与物理学院）
- Department of Theoretical & Applied Linguistics, University of Cambridge（剑桥大学理论与应用语言学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过统计场模型推断语言变体频率的时空演化，发现迁移破坏模式而调适与扩散创造并保护模式，为方言相序提供证据，并量化了25年预测能力。
AI中文摘要：
语言变化可以通过追踪语言变体频率在空间和时间上的分布来研究。观测到的频率可以被视为统计场的实现。我们首先提出一种高效的交叉验证方法，用于推断经验场——即直接从数据估计的变体频率分布——在我们的案例中，数据来自20世纪美国的高分辨率调查。然后，我们推导了一个统计场模型，假设个体在局部扩散、周期性迁移，并基于一种包含偏差和语言调适的复制子动力学形式相互模仿。我们通过比较模型场和经验场的变化来推断模型参数。我们的推断表明，空间语言模式可能因迁移而迅速破坏，但调适可以创造并保护这些模式，且与扩散相结合，能产生部分可预测的模式动态。这些结果为人类方言中表面张力驱动的相序提供了迄今最有力的证据。我们使用预测与观测之间的Kullback-Leibler散度，量化了模型在长达二十五年时间范围内的预测能力。
英文摘要：
Language change may be studied by tracking linguistic variant frequencies through space and time. Observed frequencies may be viewed as realisations of a statistical field. We first set out an efficient cross validated method for inferring the empirical field --- the variant frequency distribution estimated directly from data --- in our case, high resolution surveys from 20th century USA. We then derive a model of the statistical field, assuming that individuals diffuse locally, periodically migrate, and copy one another based on a form of replicator dynamics involving both bias and linguistic accommodation. We infer model parameters by comparing changes in model and empirical fields. Our inferences demonstrate that spatial linguistic patterns can be rapidly destroyed by migration but that accommodation can create them, protect them, and in combination with diffusion, produce partially predictable pattern dynamics. The results provide the strongest evidence yet for surface tension driven phase ordering in human dialects. We quantify the model's forecasting ability up to a time horizon of twenty five years using the Kullback-Leibler divergence between forecasts and observations.
