Title: Deep Reinforcement Learning in a Monetary Model (深度强化学习在货币模型中的应用)

Speaker: Mingli Chen（陈明俐），University of Warwick

Mingli Chen is an Associate Professor of Economics in the Department of Economics at the University of Warwick, a Research Associate in CeMMAP, and a Turing Fellow at the Alan Turing Institute (the UK's National Institute for Data Science and Artificial Intelligence). She received her PhD from Boston University and has held visiting positions at UC Berkeley, Stanford University, and the Federal Reserve Bank of Boston. Her research interests include Econometrics, Machine Learning, and AI in Economics. Her papers have been published in leading economics and statistics journals such as the Journal of Econometrics, the Journal of the Royal Statistical Society: Series B, and the Annals of Statistics. She won the LABOUR Prize at the Seventh Italian Congress of Econometrics and Empirical Economics, and received an Honorable Mention in the Arnold Zellner Thesis Award Competition by the Journal of Business and Economic Statistics in 2017. Starting in January 2024, she also serves as an Associate Editor of the Journal of Econometrics.

Abstract: We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamic optimisation problem by interacting with the model environment, of which they have no a priori knowledge. Deep reinforcement learning offers a flexible yet principled way to model bounded rationality within this general class of models. We apply our proposed approach to a classical model from the adaptive learning literature in macroeconomics which looks at the interaction of monetary and fiscal policy. We find that, contrary to adaptive learning, the artificially intelligent household can solve the model in all policy regimes.

时间：2025年4月9日 16:00-17:00

地点：复旦大学经济学院805会议室