Born from statistical mechanics, the physics of Complex Systems lays the foundations for answering questions such as how our minds or society work or how third-world development can be achieved. The Complex Systems approach consists of studying how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. An example of these relationships is between metropolitan areas (MA) or firms that interact with each other through the production of new technologies. My PhD work focuses on studying the technology innovation patenting of metropolitan areas, exploiting data from around the world from 1980 to 2014. The data adopted as a starting point are bipartite networks MA-technology in which a link between an MA, a, and a technology, t, shows if a is competitive in the technology sector t. These networks are obtained by matching two databases. Through PATSTAT, we can associate technologies to patents, and with the work of De Rassenfosse et al. we can geolocalize the patents. From these bipartite networks, I am pursuing two main research lines. The first focused on studying complex metrics used to guide the economic development of MAs. Here, in particular, we show evidence of the relationship between these metrics and the GDP per capita, highlighting how technological innovation represents a decisive factor in the economic growth of MAs. The second research line focuses on the study of the similarity between MAs and technologies to evidence the dynamic of innovation. The complex metrics can indicate which future MAs can grow economically, though they cannot tell us how this will happen. Instead, similarity measures can be used to select the new future technologies in specific MAs that can be relevant to increase the complex metrics and their economic growth.
Useful references:
Straccamore, M. Bruno, B. Monechi and V. Loreto, “Urban Economic Fitness and Complexity from Patent Data”, arXiv preprint arXiv:2210.01001, 2022
PATSTAT database: www.epo.org/searching-for-patents/business/patstat
G. De Rassenfosse, J. Kozak, and F. Seliger, “Geocoding of worldwide patent data”, Scientific data, vol. 6, no. 1, pp. 1–15, 2019.
A. Tacchella, M. Cristelli, G. Caldarelli, A. Gabrielli, and L. Pietronero, “A new metrics for countries’ fitness and products’ complexity”, Scientific reports, vol. 2, no. 1, pp. 1–7, 2012.+
Saracco et al., Inferring monopartite projections of bipartite networks: an entropy-based approach, 2017 - Python package: https://github.com/mat701/BiCM
Pugliese, E., Napolitano, L., Zaccaria, A. & Pietronero, L. Coherent diversification in corporate technological portfolios. PloS one 14, e0223403 (2019)
M. Straccamore, L. Pietronero, and A. Zaccaria, “Which will be your firm?s next technology? Comparison between machine learning and network-based algorithms”, Journal of Physics: Complexity, vol. 3, no. 3, p. 035002, 2022.