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The Enrico Fermi Research Center - CREF promotes original and high-impact lines of research, based on physical methods, but with a strong interdisciplinary character and in relation to the main problems of the modern knowledge society.

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The CREF was born with a dual soul: a research centre and a historical museum. Its aim is to preserve and disseminate the memory of Enrico Fermi and to promote the dissemination and communication of scientific culture.

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European Researchers' Night in Rome
25 September 2025
The Enrico Fermi Research Center and Historical Museum (CREF) will actively participate in the 2025 edition...

the research

Complexity in economic and technological development

Complexity theory provides a powerful conceptual and methodological framework for analyzing emergent phenomena in social and economic systems. These systems, characterized by multiple interconnected actors and nonlinear dynamics, require approaches that can capture complex relationships and emergent patterns. Since traditional approaches aren’t sufficient to grasp this complexity, this project is divided into three areas of investigation:

Economic Fitness and Complexity (EFC): A method developed in Rome by Professor Luciano Pietronero’s group to build data-driven economic models based on Complex Networks and Machine Learning. It has been shown to outperform the IMF’s country growth forecasts and is used by international institutions such as the World Bank and the Joint Research Centre (European Commission).

Computational Social Science: Uses mobility data, social networks, and digital tracking to study phenomena like the spread of epidemics, political (dis)information, and group dynamics, by integrating physics, mathematics, computer science, and social sciences.

Network Theory Analysis Methods: Analyzes the structure of complex networks to understand fundamental processes (financial shocks, climate change, and the propagation of diseases and information). Because real-world networks contain statistical noise, null models inspired by statistical physics and information theory are being developed to distinguish genuine signals from random fluctuations.

Download the document  “Economic Fitness Concepts, Methods and Applications”

Economic Fitness

Over the next three years (2025-27), the main objective is to strengthen the EFC approach to analyze crucial topics such as technological innovation, economic development, sustainability, and the labor market, with a focus on policy implications. This work will be based on individual and collaborative projects, requiring a deeper theoretical understanding of complex networks in economics and a greater integration of physics, economics, social sciences, and computer science to develop more effective models.

Computational Social Science

Recent research has used null models to identify structures in online dynamics, such as discourse communities and misinformation, distinguishing between authoritative sources, opinion leaders, and the general public. These studies, part of the PRIN PNRR 2022 CODE project, also explore the link between real-world events (e.g., epidemics) and online debates, offering new sociological perspectives on the interaction between the digital and physical worlds.

Network Theory Analysis Methods

This work focuses on applying null models, inspired by Statistical Mechanics and Information Theory, to analyze economic and social networks (such as international trade, patents, and employment data). These methods make it possible to filter out noise in data and extract relevant information, both at the topological and weighted levels, which is fundamental for identifying significant signals in contexts with high statistical variability.

The Economic Fitness and Complexity (EFC) approach considers the economy as a complex system, analyzing the interactions between “agents” (countries, regions, cities, or companies) and “items” (products, sectors, technologies, scientific articles) through bipartite networks. A key example is the country-product network, which links countries to the products they competitively export, allowing for the forecasting of GDP and the identification of new, promising products.

EFC aims to pinpoint products, sectors, and technologies that offer competitive advantages for different types of agents. By using methods of data reconstruction and harmonization at the product, sector, and technology levels, combined with applications from complex systems and machine learning, detailed forecasts and “relatedness” indices are generated to quantify links and predict development between “items.” Recently, in collaboration with the European Commission’s Joint Research Centre and the European Bank of Reconstruction and Development, the group has been focusing on reconstructing international trade data at unit prices and integrating it with global value chains, with a specific focus on the automotive and wind power sectors.

In the future, the group plans to expand the analysis to include non-exportable “items,” such as services, and to study employment shifts between industrial sectors to analyze wage dynamics and the polarization of labor markets at a granular level.

Theoretical Developments of EFC

The theoretical developments of the EFC framework will follow three main directions:

  • EFC Metrics at Different Geographic Scales: The spatial dynamics of “capabilities” (the skills, infrastructure, and resources needed to produce a given item) will be studied, analyzing how the interactions between agents and items vary as the scale changes and what emergent phenomena result.

  • Integration with the “Capability-Based Theory of the Firm”: Microeconomic data on the organizational-strategic and financial aspects of Italian companies will be analyzed to integrate the capability-based theory of the firm, developed in innovation economics, with the EFC framework.

  • Theoretical and Applied Framework for the Fitness & Complexity Algorithm: The focus will be on the Fitness & Complexity algorithm and its developments through the optimal transport problem, linking it to economic theories such as structuralist economics and innovation economics, as well as theories on the evolution of mutualistic ecosystems.

Technological Innovation and Multilayer Economic Complexity

The group will examine different techniques to evaluate how industrial production, labor markets, patent activity, and scientific research interact to predict competitive advantages in new “items.” The goal is to create multilayer graphs that describe the interactions between the economy, science, and innovation, analyzing the dynamics of competitiveness and inequality in scientific production, as well as the connection between different work fields, industrial sectors, and patent domains in Europe and the USA.

Technological Innovation and Sustainability

The group will deepen the analysis of the sustainable transition, studying “green” innovation through the analysis of patents related to climate change mitigation and adaptation, using machine learning and complex networks methods. The growth potential and competitiveness of regional and national innovation systems will be examined, considering the interactions between productive capacity and green versus non-green innovation systems. The link between industrial competitiveness and pollution will also be analyzed in “abandoned areas,” which suffer from socioeconomic and environmental inequalities.

Computational Social Science

Research focuses on identifying and analyzing online discourse communities and the spread of (dis)information. Through null models, it has been possible to quantify statistically significant signals of “echo chambers”—groups of online users with similar ideas who get their information from the same sources. These structures are crucial for understanding disinformation phenomena, as the opinions of users in echo chambers are particularly resistant to change.

Network Theory Analysis Methods

The use of null models to distinguish signal from noise in networks is well-established in the reconstruction of partial networks, and has recently been applied to the analysis of online social networks and the validation of economic data. This has made it possible, for example, to identify coordinated behavior in the spread of disinformation. On a theoretical level, maximally entropic models are being developed and adapted to different types of networks (monopartite, bipartite, hypergraphs, dynamic, or signed networks). On an applied level, these tools are used to characterize real networks, such as those in Economic Complexity or social networks, verifying the robustness of the identified structures and isolating significant behaviors from background noise.

In addition to the research activities, the project plans to share its results with the scientific community and the public. Annually, there will be the Economic Fitness and Complexity Spring School and study days at CREF, which are open to academics and civil society. The project also plans for parallel sessions at international conferences and public outreach initiatives, such as “The Researchers’ Night” and the “Maker Faire in Rome.” Finally, the scientific research results from the group are shared on the organization’s social media channels and on the websites of the research projects.

This articulated and multidisciplinary approach positions the Economic Fitness group as a key player in understanding and forecasting complex economic and social dynamics.

Luciano Pietronero 

Dario Mazzilli 

Aurelio Patelli

Fabio Saracco 

Angelica Sbardella 

Andrea Tacchella 

This methodology is officially used by:

Other scientific collaborations include:

  • SonyLab – Paris and Rome: A collaboration with the entire CSL group, directed by V. Loreto.

  • CNEL: Prof. Tiziano Treu.

  • ISC – CNR.

University Collaborations

This project is developed in close synergy with universities, particularly with the Sapienza University of Rome (and its Department of Physics staff) and the University of Tor Vergata.

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Editoriali (riviste internazionali)

 Editorials (riviste nazionali)

Conferenze

Libri

Siti Web 

  • lucianopietronero.it
  • https://www.economic-fitness.com/it