Ghanbarnejad Fakhteh, Gerlach Martin, Miotto José M, Altmann Eduardo G
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.
J R Soc Interface. 2014 Dec 6;11(101):20141044. doi: 10.1098/rsif.2014.1044.
It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period and slow end). In this paper, we analyse how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g. the Bass dynamics on complex networks), we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism.
人们普遍认为，创新的采用情况可用S曲线来描述（缓慢启动、加速期和缓慢结束）。在本文中，我们分析了从S曲线的定量描述中可以获得多少关于创新传播动态的信息。我们关注语言创新的采用情况，过去200年的详细书面文本数据库使得对其进行统计的精度达到了前所未有的程度。通过将数据分析与简单模型（如复杂网络上的巴斯动态模型）的模拟相结合，我们在采用的S曲线中识别出内生和外生因素的特征。我们提出了一种量化这些因素强度的方法以及三种从S曲线估计该强度的不同方法。我们得到了外生因素占主导的情况（如德国正字法改革和一个不规则动词的采用）以及内生因素占主导的情况（如俄罗斯名字罗马化惯例的采用以及大多数被研究动词的正则化）。这些结果表明，S曲线的形状并非普遍适用，且包含了关于采用机制的信息。
