César A. Hidalgo

César A. Hidalgo

ANITI Chair @ University of Toulouse

César Hidalgo holds an ANITI Chair at the University of Toulouse, an Honorary Professorship at the University of Manchester, and is a visiting Professor at Harvard’s School of Engineering and Applied SciencCes. Hidalgo is also a founder of Datawheel, a data visualization and distribution company. Prior to joining the University of Toulouse, Hidalgo was a professor at MIT where he directed the Collective Learning group.

Dr. Hidalgo’s research focuses on collective learning–the learning taking place in teams, organizations, and economies. With his group, he develops analytical tools and models to understanding how collective learning takes place, and also, they design tools to help improve the collective learning of organizations.

These tools include The Observatory of Economic Complexity (the world’s leading visualization engine for international trade data), Pantheon (a platform mapping human collective memory), DataViva (a platform visualizing economic data for all of Brazil), Immersion (a tool to visualize personal email networks), DataUSA (the most comprehensive effort to visualize US public data), and DIVE (a generalized tool designed to transform any dataset into a story).

A native of Santiago de Chile and a permanent resident of the US, Hidalgo is the only hispanic faculty of the MIT Media Lab. He holds a PhD in physics from the University of Notre Dame and a bachelor’s degree in physics from the Pontificia Universidad Católica de Chile. Hidalgo is the author of Why Information Grows: The Evolution of Order, from Atoms to Economies (Basic Books, 2015), and a co-author of The Atlas of Economic Complexity (MIT, Press 2011).

Keynote speech at GEOINNO2020: Wednesday 29th January at 12:40 - 14:10, Energy Hall

In recent years, measures of economic complexity have become popular tools for economic forecasting and industrial policy. Unlike factor-based descriptions of economies, economic complexity methods attempt to measure the presence of multiple factors simultaneously, using disaggregate data on economic outputs, such as exports, employment, or patents. In recent years, multiple metrics of economic complexity have been introduced, causing confusion as to what metrics to use and when. Here I show that many of these metrics belong to a single family of mathematical maps from which these measures can be derived. These maps contain multiple metrics of economic complexity that are strongly correlated with each other and with those described in the literature, suggesting that the idea of measuring the complexity of economies using iterative mappings, or the spectra of matrices connecting related economies or activities, is a much more robust phenomena than previously thought.


  • Bocconi University


  • Intesa Sanpaolo
UPDATE 10/06/2022: Please find the preliminary program for the parallel sessions here.