BEHAVIORAL SCIENCES WORKSHOP
Abstract: I discuss how insights from computational linguistics and machine learning can be used to build models of human judgment and decision making with realistic knowledge representations about the world. In addition to specifying the cognitive mechanisms people use to form beliefs and preferences, these models also represent the information on which these mechanisms operate. Subsequently, they are able to deliberate over and respond to naturalistic decision problems, and moreover, mimic human responses to these problems. These models shed light on the processes at play in everyday decisions, and illustrate a novel approach to studying behavior.
Sudeep Bhatia is Assistant Professor of Psychology at the University of Pennsylvania. He studies the cognitive basis of human judgment and decision making with the use of mathematical and computational models. There are two interrelated components of his research program. The first involves understanding how people sample and aggregate information in order to form preferences and beliefs: he extends psychological research on perceptual decision making and memory retrieval to explain behavioral findings in domains such as multiattribute choice, risky choice, and probability judgment. The second component involves specifying the information that is sampled and aggregated in order to form preferences and beliefs. Particularly, Professor Bhatia applies methodological insights from semantic memory research and computational linguistics to uncover knowledge representations for objects, attributes, and events that are the focus of everyday judgment and decision tasks.
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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.