Session

DescriptionThis minisymposium, "Advances of Deep Learning in Economics," focuses on the intersection of economic research and computational methods. Esteemed speakers, including Douglas Araujo from the Bank of International Settlements, Jonathan Payne from Princeton University, Aleksandra Friedl from the Ifo Institute, and Adam Zhang from the University of Minnesota, will share their ground-breaking work. Araujo's presentation, "Benchmarking economic reasoning in artificial intelligence models," leverages insights from the large language model benchmarking literature and the social economics literature to inform the design of benchmarking tests. Payne's talk, "Deep Learning Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models," applies deep learning to understand the complexities of continuous time models featuring rich heterogeneity. Friedl will discuss "Green energy transition: decarbonisation of developing countries and the role of technological spillovers," highlighting deep learning's efficacy in solving high-dimensional climate economics models. Lastly, Zhang's "Before and After Target Date Investing: The General Equilibrium Implications of Retirement Saving Dynamics" explores financial innovation's equilibrium effects using a unique machine learning approach. This symposium exemplifies the profound impact of computational methods, particularly deep learning, on advancing economic modeling and analysis, promising new insights in econometrics, macroeconomics, and finance.