Sophia Kazinnik (Stanford): FOMC In Silico: A Multi-Agent System for Monetary Policy Decision Modeling

Please join us for a seminar of the Advanced Financial Technologies Laboratory (AFTLab):

Time: Thursday, January 15, 2026, 5:00pm-6:00pm

Location: Huang 305

Speaker: Sophia Kazinnik (Stanford)

Title: FOMC In Silico: A Multi-Agent System for Monetary Policy Decision Modeling

Abstract: We develop a multi-agent framework for modeling the Federal Open Market Committee (FOMC) decision making process. The framework combines two approaches: an LLM-based simulation and a Monte Carlo implementation of a generalized Bayesian voting model. Both begin from identical prior beliefs about the appropriate interest rate for each committee member, formed using real-time data and member profiles. In a simulation replicating the July 2025 FOMC meeting, both tracks deliver rates near the 4.25-4.50% range's upper end (4.42% LLM, 4.38% MC). Political pressure scenario increases dissent and dispersion: the LLM track averages 4.38% and shows dissent in 88% of meetings; the MC track averages 4.39% and shows dissent in 61% of meetings. A negative jobs revision scenario moves outcomes lower: LLM at 4.30% (dissent in 74% of meeting), and MC at 4.32% (dissent in 62% of meeting), with final decisions remaining inside the 4.25-4.50% range. The framework isolates small, scenario-dependent wedges between behavioral and rational baselines, offering an in silico environment for counterfactual evaluation in monetary policy.

Bio: Sophia Kazinnik is a research scholar at the Stanford Digital Economy Lab, where she explores the intersection of artificial intelligence and economics. Prior to joining Stanford, Sophia worked as an economist and quantitative analyst at the Federal Reserve Bank of Richmond, where she was part of the Quantitative Supervision and Research group. While there, she contributed to supervisory projects targeting cyber and operational risks and developed NLP tools for supervisory purposes. Broadly, her research focuses on applying Natural Language Processing (NLP) methods and Generative AI models to economic research, with particular emphasis on policy and central bank communication, financial stability, and cyber risk. Sophia holds a bachelor’s degree in economics from Tel Aviv University in Israel and earned her doctoral degree in economics from the University of Houston.