Luciano Pietronero's research while affiliated with Centro FERMI and other places

... For instance, having in mind an urban system, its elements would be the cities composing it and as size one can use different measures ranging from population to light emission [7]. Also most financial and economical systems are generally described by Zipf 's scaling, which is found for instance in the distribution of returns, of stock prices, of the cryptocurrency market and of people' wealth [5,[8][9][10][11][12], this last being the first system where power law distributions were observed [13]. This explains a number of very counter-intuitive properties typical of financial and economics data. ...

... In such a way, two products can be defined as close in the sense that they share many of the capabilities needed in order to export them in a competitive way. Co-occurrences based approaches have however a low predictive performance, and this fact favors machine learning approaches as better tools to measure relatedness both at country (18)(19)(20) and firm level (21,22). In (16,23,24), the authors proposed approaches to explicitly model the relationship among products, capabilities, and development. ...

... In order to address some of the perceived weaknesses of the Economic Complexity framework's 'linear' approach, an enhanced method for the measurement of economic complexity was developed by Pietronero, Tacchella, Calderalli and their collaborators [9][10][11][13][14][15]. This research [13] makes use of crude oil as an anecdotal example of the inconsistency of Economic Complexity as a measure to rank the competitiveness of countries. ...

... Many studies recently relied on network-based techniques to unfold the complex interplay among patents, technological codes and geographical reference areas. Network science techniques allowed to analyse economic activities of countries 34 , regions [35][36][37][38][39] , cities 2,40-42 or firms 43,44 . ...

... Recent studies have shown that it is helpful to calculate the Fitness of sub-national actors using the complexity that comes from the national systems 54,79,80 . This measure is called exogenous Fitness and overcomes the issue of the limited capabilities of sub-national entities, such as cities or MAs in our case. ...

... Zipf 's law is a characteristic feature of complex systems and is often considered as a footprint of complexity [1][2][3]. Such scaling law is observed in an astonishing number of natural and social systems characterized by emerging complex behaviors, such as solar flares, cosmic structures, earthquakes, language, urban systems and many more [2,[4][5][6]. ...

... Zipf 's law is a characteristic feature of complex systems and is often considered as a footprint of complexity [1][2][3]. Such scaling law is observed in an astonishing number of natural and social systems characterized by emerging complex behaviors, such as solar flares, cosmic structures, earthquakes, language, urban systems and many more [2,[4][5][6]. Denoting by S(k) the size of the kth largest element in the system, Zipf 's law reads ...

... In (16,23,24), the authors proposed approaches to explicitly model the relationship among products, capabilities, and development. These frameworks naturally lead to the concepts of product progression (16,19,25) and arrow of development (26): the relationship between products is often not undirected, or symmetric, as in the product space (10), but directed: countries starts their development from simple products and gradually enter in more sophisticated markets, following well defined paths of development (16). Obviously, the identification of the specific products enabling countries to competitively export a given target product is a key element to design industrial policies and strategic patterns of development. ...

... Obviously, these modelling exercises face two very difficult challenges: they have to work in different country contexts, and they have to work at all times, hence predicting growth potentials and not only recoveries from shocks (Cristelli et al. 2017). In the wake of COVID-19, institutional forecasters turned to their models and tried to adapt them to the rapidly changing circumstances. ...

... Technological innovation is often highlighted as one of the main drivers for evolution and change in cities, and it has been shown that complex economic activities flourish in large urban areas 11 , although remote working and dispersed research teams can mitigate the concentration of innovation in urban areas 12-15 . In parallel, many studies recently focused on how innovation diffuses temporally and spatially [16][17][18] , also with a particular focus on cities 19 . In this paper, we focus on technological innovation, and we investigate how the technological fingerprints of cities can affect their development and potential. ...