AI is already a ubiquitous presence in the financial system, most obviously in the guise of algorithmic trading, but also in the automation of credit issuance and other finance-related decisions. Understanding the implications of AI on the financial system, both generally and with respect to specific techniques, requires modeling the complexity of financial activity and the computational ingredients in decision making. Agent-based modeling (ABM) affords direct representation of these computational elements, and accommodates the heterogeneity and complexity characteristic of financial environments. Although ABM is typically presented as an alternative to mainstream economic approaches, I argue that it is actually compatible and even complementary with standard frameworks, and demonstrate this with recent finance-related studies conducted in my research group. We focus on two domains: algorithmic trading in financial markets, and effects of banking regulation on stability.

Bio: Prof. Michael P. Wellman is Professor of Computer Science & Engineering at the University of Michigan. He received a PhD from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. From 1988 to 1992, Wellman conducted research in these areas at the USAF’s Wright Laboratory. For the past 25 years, his research has focused on computational market mechanisms and game-theoretic reasoning methods, with applications in electronic commerce, finance, and cyber-security. As Chief Market Technologist for TradingDynamics, Inc., he designed configurable auction technology for dynamic business-to-business commerce. Wellman previously served as Chair of the ACM Special Interest Group on Electronic Commerce (SIGecom), and as Executive Editor of the Journal of Artificial Intelligence Research. He is a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery.