Complex Systems Institute of Paris Ile-de-France, Paris, France.
PLoS One. 2013;8(2):e54847. doi: 10.1371/journal.pone.0054847. Epub 2013 Feb 11.
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.
我们介绍了一种基于数字图书馆中大批量数据的、自下而上的科学认知演化重建自动化方法，其模型为科学领域之间的谱系关系。我们通过类比生物进化，将这些动态结构称为“系统发生网络”或“系统发生记忆”；并表明它们具有很强的规律性，存在可明确识别的系统发生模式。科学领域的某些结构特性（特别是其密度）与系统发生记忆的重建无关，它们与科学领域的地位和命运（如年龄或短期生存能力）明显相关。在定量认识论的框架内，这种方法引发了科学演化可预测性的问题，并勾勒出了科学领域的原型生命周期：在出现后其凝聚力增加，通过分支或合并事件更新其概念背景，然后当其密度变得过低时衰减。
