Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach
Author(s)Park, Hyunseok; Magee, Christopher L
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The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.
DepartmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society; SUTD-MIT International Design Centre (IDC)
Public Library of Science
Park, Hyunseok and Magee, Christopher L. “Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.” Edited by Zhong-Ke Gao. PLOS ONE 12, no. 1 (January 2017): e0170895 © 2017 Park, Magee
Final published version