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Quantifying technological change as a combinatorial process

机译：将技术变化量化为一个组合过程

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摘要

Technological change plays a critical role in industrial- and societal development. As a consequence, modelling, measuring and monitoring the rate and direction of technological change has been extensively studied. However, there is to-date no scalable, cross-domain, and data source agnostic quantitative indicator for technological change that considers a combinatorial process of invention and technology development. This paper develops and empirically tests a network-based method that takes a combinatorial view of technological development as underlying rationale and provides a quantitative indicator for technological change. Unlike prior research, the proposed method allows for the simultaneous inclusion of multiple and diverse types of data sources, i.e. publications, patents, and projects and it uses text-mining analyses based on co-occurrences of terms over time. The novel method proposed here goes beyond reliance on domain experts building custom sets of taxonomies, is applicable across different technological domains, and permits a temporal analysis of changes across years, industries, and countries. This is illustrated using a large database of worldwide bioenergy research and development (R&D) records. Findings include the detection of biofuel generations in the data before they were first mentioned as such in literature. Implications for research, industry and policy are also discussed.
机译：技术变革在工业和社会发展中起着至关重要的作用。结果，对技术变化的速率和方向进行建模，测量和监视已得到广泛研究。但是，到目前为止，还没有针对技术变革的可扩展，跨域和数据源不可知定量指标，该指标考虑了发明与技术开发的组合过程。本文开发并实证测试了一种基于网络的方法，该方法将技术发展的组合视图作为基本原理，并为技术变化提供了定量指标。与先前的研究不同，所提出的方法允许同时包含多种类型的数据源，即出版物，专利和项目，并且它使用基于术语共现的文本挖掘分析。这里提出的新颖方法不仅仅依赖于领域专家构建定制的分类法集，它还适用于不同的技术领域，并且可以对跨年，跨行业和跨国家的变化进行时间分析。大型的全球生物能源研究与开发（R＆D）记录数据库对此进行了说明。研究结果包括在文献中首次提及生物燃料生成之前对其进行检测。还讨论了对研究，行业和政策的影响。

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