GIAMBATTISTA ALBORA

Dottore di ricerca

ciclo: XXXV



Titolo della tesi: Machine learning to assess relatedness in Economic Complexity

Relatedness is a measure of the affinity between an activity and a location. In the Economic Complexity framework it is used to quantify how much a country is close to the export of a product. Since knowing which products are within the reach of a country is a powerful tool to increase its diversification and with it its fitness, relatedness is a key tool for institutions and policy makers, and a driver for investments. In my PhD thesis I address the problem of measuring the relatedness focusing on the importance of empirical evidence to validate or falsify a model, that is something at the basis of every study in physics, but it is often neglected in economy. Our proposal is to interpret the relatedness between a country and a product as the probability that the country will export the product. Quantifying how good a relatedness measure is in forecasting the future exports we can determine which is the best model. I will show that a machine learning approach, in particular a decision tree based algorithm, provides the best assessment of the relatedness and I will discuss about its critical points like the explainability of the results and the computational effort.

Produzione scientifica

11573/1669482 - 2023 - Product progression: a machine learning approach to forecasting industrial upgrading
Albora, G.; Pietronero, L.; Tacchella, A.; Zaccaria, A. - 01a Articolo in rivista
rivista: SCIENTIFIC REPORTS (London: Springer Nature London: Nature Publishing Group) pp. 1481- - issn: 2045-2322 - wos: WOS:000954574000035 (3) - scopus: 2-s2.0-85146964102 (4)

11573/1651642 - 2022 - Machine Learning to Assess Relatedness: The Advantage of Using Firm-Level Data
Albora, Giambattista; Zaccaria, Andrea - 01a Articolo in rivista
rivista: COMPLEXITY ([New York, N.Y.] : John Wiley & Sons, Inc.) pp. 1-12 - issn: 1099-0526 - wos: WOS:000837873100004 (3) - scopus: 2-s2.0-85135708270 (2)

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