Next Article in Journal
Evaluation of Radiation Response in CoCrFeCuNi High-Entropy Alloys
Next Article in Special Issue
Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States
Previous Article in Journal
Cities, from Information to Interaction
Previous Article in Special Issue
Complexity of Products: The Effect of Data Regularisation
Open AccessArticle

A Context Similarity-Based Analysis of Countries’ Technological Performance

1
Institute for Complex Systems—CNR, Via dei Taurini 19, 00185 Rome, Italy
2
International Finance Corporation—World Bank Group, Washington, DC 20433, USA
3
Dipartimento di Fisica, Sapienza University of Rome, Piazzale Aldo Moro, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(11), 833; https://doi.org/10.3390/e20110833
Received: 26 July 2018 / Revised: 10 October 2018 / Accepted: 17 October 2018 / Published: 31 October 2018
(This article belongs to the Special Issue Economic Fitness and Complexity)
This work contributes to the literature in the field of innovation by proposing a quantitative approach for the prediction of the timing and location of patenting activity. In a recent work, it was shown that focusing on couples of technological codes allows for the formation of testable predictions of innovation events, defined as the first time two codes appear together in a patent. In particular, the construction of the vector space of codes and the introduction of the context similarity metric allows for a quantitative analysis of technological progress. Here, we move from that result and we show that, through context similarity, it is possible to assign to countries a score which measures the probability of being the first to patent a potential innovation. In other words, we show that we can not only estimate the likelihood that a potential innovation will be patented in the imminent future, but also forecast where it will be patented. View Full-Text
Keywords: innovation; economic studies; machine learning innovation; economic studies; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Napoletano, A.; Tacchella, A.; Pietronero, L. A Context Similarity-Based Analysis of Countries’ Technological Performance. Entropy 2018, 20, 833.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop