DaY 1

Mon 8 July 2024

8:15 - 8:30


8:30 - 9:15

Welcome address and coffee

Opening words from UNU Rector Prof. Tshilidzi Marwala, the EFC organising team and the Young Scholars Initiative

9:15 - 10:00

Economic Fitness: Concepts, Methods and Applications

Lecturer: Luciano Pietronero

Economic Fitness and Complexity (EFC) is the recent economic discipline and methodology which makes use and develops the modern techniques of data analysis to build Economic Models based the science of Complex Systems to provide a sound scientific framework. It consists of a data based and bottom up approach that considers specific and concrete problems without economic ideologies and it acquires information from the previous growth data of all countries with methods of Complex Networks, Algorithms, Machine Learning and AI. Its main characteristics are the scientific rigor, the precision in the analysis and in the forecasting, transparency and adaptability. The new Fitness algorithm overcomes the conceptual and practical problems of the early attempts in this field and sets the basis for a testable and successful implementation of the field of Economic Complexity. According to Bloomberg Views: “New research has demonstrated that the "fitness" technique systematically outperforms standard methods, despite requiring much less data” In addition EFC has provided a detailed understanding and forecasting of the fantastic growth of China in the past thirty years which has been a major mystery for most of the standard economic analysts. The Economic Fitness represents a synthetic measure of the degree of competitivity in terms of the capabilities to produce products and services. Mathematically the Fitness corresponds to the diversification weigthed by the complexity of the products. The diversification provides stability and resilience while the complexity of the products represents the exclusivity and the relative wealth. The European Commission (Joint Research Center) has recently adopted these methods to evaluate the recovery fund projects (PNRR).

Since a few years it has been used by IFC-World Bank Group and recently also by EBRD (London) to define specific economic actions tuned for specific countries, in particular for developing ones. One of the main targets is to identify the products or technologies which will enable to open new markets, considering the specific situation of each country. In this respect there is a complementarity with the New Structural Economics developed by Justin Lin and collaborators. An example for African countries can be found here. 

Another concept which is important in this field is that of Relatedness which describes the dynamics in the Product Space. The usual approach is often based in the co-occurrency of products in the basket of countries. The problem with this approach is that the products are 5200 while the countries are only about 150. This leads to a serious problem of signal to noise that makes this approach not far from a random choice. In order to obtain concrete and testable results it is necessary to resort to much more sophisticated Machine Learning methods. The validity of these methods has been tested in detail with out of the box approaches. In this way it is possible to identify the possible trajectories for development which are characterized by the Feasibility, which describes how easy is to go in that direction and the Complexity Gain which is essentially the microscopic increase of Fitness of a given trajectory. This information leads to a scientifically based knowledge which defines the framework for the decisions of policy makers.

EFC for Companies. Also for companies the EFC analysis leads to a variety of original results. Companies show a block-nested pattern with respect to the matrix of the products which requires a different analysis with respect to the country matrix which is fully nested. The Fitness algorithm can be applied within each block to define the Company Fitness. Then from the patents one can obtain the technological network and introduce the concept of coherency for a groups of technologies related to a specific product. The Product Progression identifies the next product or technology that a company may be able to produce and its competitiveness in the various markets. Along these lines one can derive a number of results related to the opportunities to enter a certain market or to develop a new product. Also the analysis and optimization of the Merging and Acquisition process can be done with these methods.

Recently we have developed these methodologies to study the impact of AI on the Job Market. Introducing the concept of Job Fitness on average we observe an inverse proportionality between Job Fitness and AI impact. However, there are also important outliers which require additional considerations.

10:00 - 10:30

Introducing EFC methods

Lecturer: Andrea Tacchella

Break 10:30 - 11:00

11:00 - 12:30

Introducing EFC methods

Lecturer: Andrea Tacchella

Lunch 12:30 - 13:30

13:30 - 15:30

Coding Lab

Break 15:30 - 16:00

16:00 - 18:00

Group work

Free time 18:00 - 18:30

Drinks reception 18:30 onwards @D'n hiemel