Abstract:
Texts such as news articles, social media posts, contracts, financial reports, laws, and regulations are deeply embedded in the real-world dynamics of the economy and business. Traditionally, economic studies have had limited means of systematically and quantitatively analyzing such textual data, primarily through text mining and certain qualitative methods, making it difficult to conduct complex, context-aware analysis. However, generative AI, including large language models (LLMs), has the potential to dramatically transform this landscape. This talk explores how generative AI can contribute to policy analysis, with a focus on applications in economic policy studies, including monetary and fiscal policy analysis.
Biography:
Dr. Masahiro Sato is an Associate Professor at the Graduate School of International Cultural Studies, Tohoku University. He received a Master of Arts and Sciences from the University of Tokyo in 2001, an M.A. in Economics from Georgetown University in 2006, and a Ph.D. in Economics from Kyoto University in 2015. He worked for the Japanese government as a policy officer from 2001 to 2011 and from 2014 to 2017, and served as an Associate Professor at Kyoto University from 2011 to 2014. His research focuses on economic theory and machine learning.
Texts such as news articles, social media posts, contracts, financial reports, laws, and regulations are deeply embedded in the real-world dynamics of the economy and business. Traditionally, economic studies have had limited means of systematically and quantitatively analyzing such textual data, primarily through text mining and certain qualitative methods, making it difficult to conduct complex, context-aware analysis. However, generative AI, including large language models (LLMs), has the potential to dramatically transform this landscape. This talk explores how generative AI can contribute to policy analysis, with a focus on applications in economic policy studies, including monetary and fiscal policy analysis.