Proposed Reinforcement Learning Microfoundations of Behavioral Phenomena Project

Understanding belief formation is an increasingly central question in Macroeconomics. I am interested in doing so because I want to understand what conditions allow agents’ beliefs to compound and contribute to market crashes, banking panics, untethered inflation, failure of institutions, and the outbreak of conflict and crime when misjudging threats. The dominant paradigm of full- information rational expectations (FIRE) is insufficient to think about these conditions because we know belief evolution is inconsistent with FIRE (Coibion and Gorodnichenko 2015). I believe I need to understand and model agents’ belief formation and choice behavior in a manner consistent with empirical evidence to have any shot at matching historical episodes of instability/shifts in beliefs and choice behavior. I will tackle this question with a research project, where I utilize new tools from reinforcement learning to build more empirically accurate models of how agents learn and how the economy will evolve.