Estimating technology performance improvement rates by mining patent data

Giorgio Triulzi (), Jeff Alstott and Christopher L. Magee

Technological Forecasting and Social Change, 2020, vol. 158, issue C

Abstract: The future direction of technology development depends on the relative yearly rate of functional performance improvement of different technologies. We use patent data to identify accurate and reliable predictors of this rate for 30 technologies. We illustrate how patent-based predictors should be normalized to correct for possible confounding factors introduced by changing patenting dynamics. We test the accuracy and reliability of various predictors by means of a Monte Carlo cross-validation exercise. We find that a measure of the centrality of domains’ patented inventions in the overall US patent citation network is an accurate and highly reliable predictor of improvement rates.

Keywords: Technology performance; Exponential rates; Patent citations; Centrality; Technological trajectories (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162520309264
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:158:y:2020:i:c:s0040162520309264

DOI: 10.1016/j.techfore.2020.120100

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().