Showing papers by "Luciano Pietronero published in 2021"

Zipf's law for cosmic structures: How large are the greatest structures in the universe?

Abstract: The statistical characterization of the distribution of visible matter in the universe is a central problem in modern cosmology. In this respect, a crucial question still lacking a definitive answer concerns how large the greatest structures in the universe are. This point is closely related to whether or not such a distribution can be approximated as being homogeneous on large enough scales. Here we assess this problem by considering the size distribution of superclusters of galaxies and by leveraging the properties of Zipf–Mandelbrot law, providing a novel approach which complements the standard analysis based on the correlation functions. We find that galaxy superclusters are well described by a pure Zipf’s law with no deviations and this implies that all the catalogs currently available are not sufficiently large to spot a truncation in the power-law behavior. This finding provides evidence that structures larger than the greatest superclusters already observed are expected to be found when deeper redshift surveys will be completed. As a consequence, the scale beyond which galaxy distribution crossovers toward homogeneity, if any, should increase accordingly.

Zipf's law for cosmic structures: how large are the greatest structures in the universe?.

Abstract: The statistical characterization of the distribution of visible matter in the universe is a central problem in modern cosmology. In this respect, a crucial question still lacking a definitive answer concerns how large are the greatest structures in the universe. This point is closely related to whether or not such a distribution can be approximated as being homogeneous on large enough scales. Here we assess this problem by considering the size distribution of superclusters of galaxies and by leveraging on the properties of Zipf-Mandelbrot law, providing a novel approach which complements standard analysis based on the correlation functions. We find that galaxy superclusters are well described by a pure Zipf's law with no deviations and this implies that all the catalogs currently available are not sufficiently large to spot a truncation in the power-law behavior. This finding provides evidence that structures larger than the greatest superclusters already observed are expected to be found when deeper redshift surveys will be completed. As a consequence the scale beyond which galaxy distribution crossovers toward homogeneity, if any, should increase accordingly

Product Progression: a machine learning approach to forecasting industrial upgrading.

Abstract: Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should play this role, and we compare various machine learning models to set the prediction benchmark. We find that the key object to forecast is the activation of new products, and that tree-based algorithms clearly overperform both the quite strong auto-correlation benchmark and the other supervised algorithms. Interestingly, we find that the best results are obtained in a cross-validation setting, when data about the predicted country was excluded from the training set. Our approach has direct policy implications, providing a quantitative and scientifically tested measure of the feasibility of introducing a new product in a given country.

Behind the Italian Regional Divide: An Economic Fitness and Complexity Perspective

Abstract: This paper applies the Economic Fitness and Complexity approach to analyse the underlying factors behind the wide and persistent economic disparities across the Italian regional units. Measures of regional fitness are obtained from their revealed comparative advantage and from their patent performance. Southern regions tend to be characterised by a lower level of complexity than the regions in the Centre-North of the country. We interpret these results as indicating a lower level of capability endowment in the South. The system-wide approach of the paper is able to identify some critical sectors which display a rich pattern of connections with other sectors and which could play a pivotal role to create additional capabilities and foster a more balanced regional development.

Which will be your firm's next technology? Comparison between machine learning and network-based algorithms.

Abstract: We reconstruct the innovation dynamics of about two hundred thousand companies by following their patenting activity for about ten years. We define the technological portfolios of these companies as the set of the technological sectors present in the patents they submit. By assuming that companies move more frequently towards related sectors, we leverage on their past activity to build network-based and machine learning algorithms to forecast the future submission of patents in new sectors. We compare different evaluation metrics and prediction methodologies, showing that tree-based machine learning algorithms overperform the standard methods based on networks of co-occurrences. This methodology can be applied by firms and policymakers to disentangle, given the present innovation activity, the feasible technological sectors from those that are out of reach, given their present innovation activity.