“Structure Learning and the Origin of Reference Points,” Sam Gershman, Harvard University
BEHAVIORAL SCIENCES WORKSHOP
Abstract: Modern theories of decision making emphasize the reference-dependency of decision making under risk. In particular, people tend to be risk-averse for outcomes greater than their reference point, and risk-seeking for outcomes less than their reference point. A key question is where reference points come from. A common assumption is that reference points correspond to expectations about outcomes, but it is unclear whether people rely on a single global expectation, or multiple local expectations. If the latter, how do people determine which expectation to apply in a particular situation? I argue that people discover reference points using a form of Bayesian structure learning, which partitions outcomes into distinct contexts, each with its own reference point corresponding to the expected outcome in that context. Consistent with this theory, experiments demonstrate that dramatic change in the distribution of outcomes can induce the discovery of a new reference point, with systematic effects on risk preferences. By contrast, when changes are gradual, a single reference point is continuously updated.
Sam Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, where he worked with Ken Norman and Yael Niv. From 2013-2015, he was a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT, working with Josh Tenenbaum and Nancy Kanwisher. Sam Gershman is currently an Associate Professor in the Department of Psychology and Center for Brain Science at Harvard. His research focuses on computational cognitive neuroscience approaches to learning, memory and decision making.
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Cosponsored by the Center for the Study of American Politics (CSAP) and the School of Management’s International Center for Finance and the Lynne & Andrew Redleaf Foundation.