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TOPIC: | APPLICATIONS OF DEEP LEARNING-BASED PROBABILISTIC APPROACH TO "COMBINATORIAL" PROBLEMS IN ECONOMICS |
ABSTRACTMany “combinatorial” problems in economics arise from the static or discrete timing assumption that condenses a series of simple binary choices scattered randomly over time into a single instance. Leaning on this insight, we transform combinatorial choices into a sequence of binary choices in continuous time. The complexity of combinatorial choices turns into the dimensionality problem of dynamic optimization, which is overcome by applying a deep learning-based probabilistic approach. Two examples are provided for demonstration: 1) an exporting firm sporadically selects destinations among 100 potential interdependent markets; 2) a dynamic input-output network formation model involving 37 sectors. Keywords:Combinatorial choice, network formation, the curse of dimensionality, backward stochastic differential equation, deep learning. JEL Codes: C63, C67, D85, F23 Click here to view the CV. Click here to view the paper. | |  | PRESENTERJi Huang The Chinese University of Hong Kong | RESEARCH FIELDSMacro-finance Banking Numerical solutions of high-dimensional dynamic models | DATE:9 April 2024 (Tuesday) | TIME:4pm - 5.30pm | VENUE:Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903 |
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