In the past decade, researchers in psychology and neuroscience studying human decision-making have increasingly adopted a framework that combines two systems, namely “model-free” and “model-based” learning. This framework is imported into a simple financial setting, study its properties, and link it to a range of applications. Nick Barberis shows that it provides a foundation for extrapolative demand and experience effects; resolves a puzzling disconnect between investor allocations and beliefs in both the frequency domain and the cross-section; helps explain the dispersion in stock market allocations across investors as well as the inertia in these allocations over time; and sheds light on the persistence of household investment mistakes. More broadly, the framework offers a way of thinking about individual behavior that is grounded in recent evidence on the computations that the brain undertakes when estimating the value of a course of action.
Nick Barberis, Stephen and Camille Schramm Professor of Finance at Yale University
Free and open to the public; registration required. Hosted by the Cognitive and Behavioral Economics Initiative and the Center for Decision Sciences at Columbia University.