SLAI Seminar 29th Session will be discussing the topic on " AI for Social Science and Economic World Models ",  from 10am to 11:30am, March 17th (Tuesday) at Room B401, online participation is welcome.

 (Tencent Meeting ID: 975-860-042)

About the Speaker:

Lin William Cong is the President’s Chair Professor at Nanyang Technological University, with joint appointments at Nanyang Business School (Associate Dean and Professor in Finance), the College of Computing and Data Science (Professor in Data Science and AI), and the Global Institute of Finance, Technology, and Society. Previously, he served as the Rudd Family Endowed Chair Professor of Management and Finance at Cornell University, and as a Finance professor at the University of Chicago. He is an editor or associate editor at leading Management Science and Finance journals, a faculty scientist at the Initiative for Cryptocurrencies & Contracts (IC3), a research associate or senior fellow at the National Bureau of Economic Research (NBER), European Centre of Economic Policy Research (CEPR), and the Asian Bureau of Finance and Economic Research (ABFER).

Cong’s research spans finance, digital economics, information economics, AI, FinTech, digital economy, and entrepreneurship, and has been featured in Bloomberg, CNN, the Economist, Washington Post, etc. His team has pioneered AI for economics, information design in finance, and has laid the foundations of tokenomics, economic world models, blockchain economics, and oracle economics, behavioral economics of AI, and has developed data analytics for detecting market manipulation and better FinTech regulation, among others.

Cong is one of the most published and cited FinTech researchers, a top 5 young economist ranked by IDEA, and the 6th highest cited financial theorist among all those graduated in the 21st century. His research has been recognized with over 100 conference best paper prizes and competitive grants. He has also been invited to deliver keynote speeches at numerous international conferences and to advise various startups, investment firms, and non-profit organizations, including serving as the inaugural Senior Economist at Chainlink and the Chief Scientist at HoloBit. He has also been an advisor or trainer for government agencies such as Asset Management Association of China, the SEC, U.S. Department of Justice and Department of Treasury, New York State Superintendent’s Office and Department of Financial Services, and the Bank of Canada.

Cong earned a Ph.D. in Finance and a MS in Statistics from Stanford University. He also holds dual degrees from Harvard University where he graduated summa cum laude and top in the Physics department (perfect GPA), with an A.M. in Physics, an A.B. in Math & Physics, a minor in Economics, and a language citation in French.

Abstract: I overview three major directions/stages of how modern AI can be used for advancing research in economics and, more broadly, in the social sciences. Collectively, they not only constitute the first non-text-based GenAI models in economics or finance, but also form a new research paradigm involving Economic World Models that differ from existing models.

Specifically,

(i) I first introduce goal-oriented algorithms in large modeling spaces involving transformer-based reinforcement learning or not-so-large but interpretable panel trees, which are particularly suited for optimal decision-making (e.g., flexible investment management and mass-customized robo-advising) and for characterizing grouped heterogeneity in panel data (e.g., generating test portfolios or latent factors for better asset pricing or evaluating extant models, and understanding differential factors across asset groups or macroeconomic regimes, and differential return predictability).

(ii) I then discuss combining deep learning with robust control to build Economic World Models to study corporate decision-making that entails complex, high-dimensional, and non-linear stochastic control. Our AlphaManager not only better explains and predicts corporate outcomes in- and out-of-sample but also prescribes key managerial actions that significantly outperform. We validate our framework in a well-known moral hazard setting, and empirically document rich heterogeneity in model ambiguity, prediction performance, and policy efficacy in the cross section of U.S. public firms and over time. Furthermore, we use inverse reinforcement learning to learn managerial objectives and use model ambiguity measures to understand the limit to data-driven approach in corporate finance research.

(iii) Finally, I discuss how we can move towards richer Economic World Models involving AI-agent-based modeling/simulation, by (a) introducing and characterizing data-driven generative equilibrium for counterfactual analyses using the example of online lending, (b) understanding the behavior of AI agents using experiments from the psychology and behavioral economics literature, regardless of whether they represent humans or constitute a new population in the economy, and (c) bridging theory and data-driven methodologies by inventing Structured-Knowedge-Informed Neural Networks.