2019/05/21 by Tom Broekel · 1 voice · 1 citation
Economics, Econometrics and Finance · Business, Management and Accounting · Decision Sciences · #Economic and Technological Innovation #Innovation and Knowledge Management #Innovation Diffusion and Forecasting
paper · pdf · doi:10.1371/journal.pone.0216856
openalex publication_date 2019/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
The paper introduces structural diversity as a new approach to quantify the complexity of technologies. By modeling technologies as combinatorial networks, a measure of technological complexity is derived that represents the diversity of (sub-)network topologies in these networks. It is further argued that this measure can be empirically approximated with the Network Diversity Score (NDS). The paper also presents an application of this approach to European patent data from 1980 to 2015. On this basis, the measure of structural diversity is shown to replicate a number of stylized facts commonly associated with technological complexity: Complexity increases over time and younger technologies are more complex than older technologies. Complex technologies are also associated to larger R&D efforts and require more collaborative R&D activities. Lastly, when controlling for technologies' size, technologies scoring high on structural diversity are also shown to concentrate in space.