创新系统的知识生成效率：结构效应与时间效应的关系
Knowledge-generating Efficiency in Innovation Systems: The relation between structural and temporal effects
摘要：
利用美国每百万居民专利数量的时间序列，可以区分知识产生的周期。这些周期与康德拉季耶夫长波部分重合。它们之间斜率的变化表明知识生成范式的不连续性。知识生成范式可以根据相互作用的维度（例如，在大学-产业-政府关系中）来建模，这些维度限制了创新系统的最大效率。模型中参数的最大值与经验波的回归系数的阶数相同。维数增加的机制被指定为自组织，这导致现有关系被打破为更多样化的分形网络结构。这种断裂可以类似于2D和3D（Koch）雪花来建模。知识产生的推动导致了新出现的技术，预计这些技术将更加多样化，生存周期比以前更短。知识生成周期的时间跨度也可以用斐波那契数来分析。这一观点允许预测未来可能的范式变化的预期日期。就政策含义而言，这表明重点从制造技术转移到开发新的组织技术和人类互动模式
英文摘要：
Using time series of US patents per million inhabitants, knowledge-generating cycles can be distinguished. These cycles partly coincide with Kondratieff long waves. The changes in the slopes between them indicate discontinuities in the knowledge-generating paradigms. The knowledge-generating paradigms can be modeled in terms of interacting dimensions (for example, in university-industry-government relations) that set limits to the maximal efficiency of innovation systems. The maximum values of the parameters in the model are of the same order as the regression coefficients of the empirical waves. The mechanism of the increase in the dimensionality is specified as self-organization which leads to the breaking of existing relations into the more diversified structure of a fractal-like network. This breaking can be modeled in analogy to 2D and 3D (Koch) snowflakes. The boost of knowledge generation leads to newly emerging technologies that can be expected to be more diversified and show shorter life cycles than before. Time spans of the knowledge-generating cycles can also be analyzed in terms of Fibonacci numbers. This perspective allows for forecasting expected dates of future possible paradigm changes. In terms of policy implications, this suggests a shift in focus from the manufacturing technologies to developing new organizational technologies and formats of human interactions
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
