Marco Pangallo joined the Institute of Economics of the Sant’Anna School and EMbeDS as a research fellow in economics. His position is funded by the James S. Mc Donnell Foundation based in the US. Postdoctoral fellowships are awarded to candidates working at the intersection of disciplines, advancing the science of complex, adaptive, nonlinear systems, and applying complex systems approaches to fields where such approaches are not yet mainstream but show great potential.
Marco has always been interested in a quantitative understanding of the economy obtained through complex systems methods, such as agent-based modeling, network theory and non-linear dynamics. He obtained a Master’s degree in Physics of Complex Systems from the University of Turin, where he also attended courses in Economics at Collegio Carlo Alberto. He then received a PhD in Mathematics from the University of Oxford, where he worked in the Complexity Economics group at the Institute for New Economic Thinking of the Oxford Martin School.
In his PhD thesis, Marco investigated the emergence of endogenous fluctuations at micro- and macro-scales. For the former, he analyzed equilibrium convergence in game theory. For the latter, he studied synchronization of endogenous business cycles. In collaboration with researchers at the Bank of Italy, he also worked on a large dataset of housing adverts, extracting novel information about the Italian housing market.
What are Marco’s plans for this new chapter of his academic career, in connection with the objectives of EMbeDS? “I will mainly focus on understanding the potential of Agent-Based Models for time series forecasting. ABMs are much more detailed than more traditional equation-based models, yet their additional flexibility has rarely been used to provide more accurate quantitative predictions. Showing that ABMs can produce more accurate forecasts would be particularly useful for economists, who tend to be skeptical about their 'value added'. It would explicitly demonstrate that ABMs can give more reliable answers to key policy questions than traditional models, because they more realistically represent the workings of the economy.”