Research Scientist at CENTAI (Center for Artificial Intelligence)
Marco Pangallo is a research scientist at CENTAI (Center for Artificial Intelligence), a newly established research institute that aims at combining independent research on artificial intelligence and complex systems with industrial applications. Previously, he was a JSMF Postdoctoral Fellow at the Sant’Anna School for Advanced Studies, Italy. Marco obtained his PhD in Mathematics at the University of Oxford and was part of the Complexity Economics group at the Oxford Martin School's Institute for New Economic Thinking.
Marco is generally interested in understanding the economy quantitatively through a combination of data-driven and theoretical approaches. He believes that traditional economic models – based on optimization and equilibrium – are not best suited to quantitatively account for the complexity of the economy. Instead, agent-based models are the best tool to assimilate increasingly available granular data and produce more reliable economic forecasts.
Currently, Marco is collaborating with INET Oxford on projects that attempt to understand business cycles as a result of endogenous phenomena, combining several economic forces that have been proposed to generate business cycles, and validating theoretical results on extensive microeconomic and macroeconomic datasets.
The unequal effects of the health–economy trade-off during the COVID-19 pandemic
16 Nov 23
In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcom...
Best-response dynamics, playing sequences, and convergence to equilibrium in random games
28 Jun 23
We analyze the performance of the best-response dynamic across all normal-form games using a random ...
The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
13 Dec 22
The potential tradeoff between health outcomes and economic impact has been a major challenge to pol...
Forecasting the propagation of pandemic shocks with a dynamic input-output model
28 Sep 22
We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of ...
Towards a taxonomy of learning dynamics in 2 × 2 games
21 Dec 21
Studying 2X2 games for tractability, we recover some well-known results in the limiting cases in whi...
No. 2021-18 - In and out of lockdown: Propagation of supply and demand shocks in a dynamic input-output model
22 Feb 21
Economic shocks due to Covid-19 were exceptional in their severity, suddenness and heterogeneity acr...
Production networks and epidemic spreading: Re-opening the UK economy
07 Jun 20
This column reports several re-opening scenarios for the UK economy, documenting their projected imp...
No. 2020-12 - Production networks and epidemic spreading: How to restart the UK economy?
21 May 20
We analyse the economics and epidemiology of different scenarios for a phased restart of the UK econ...
Residential income segregation: A behavioral model of the housing market
01 Mar 19
We represent the functioning of the housing market and study the relation between in- come segregati...
Best reply structure and equilibrium convergence in generic games
20 Feb 19
We show that best reply cycles, basic topological structures in games, predict nonconvergence of six...
Home is where the ad is: online interest proxies housing demand
09 Nov 18
In this paper we investigate if online interest can be used as a proxy of housing demand, a key yet ...
No. 2017-07 - Best reply structure and equilibrium convergence in generic games
17 Mar 18