Social, Behavioral and Experimental Economics Seminar: David Huffman, Cornell University
Abstract
We document a mechanism through which wrong mental models and beliefs survive in data-rich environments: by remaining silent about the patterns they deem irrelevant, wrong models direct attention away from the patterns that refute them. We examine this in firm pricing under consumer intertemporal substitution (IS), where shifts in purchase timing add to the sales spike on the discount day and produce dips before and after. A simple model in which lower prices lead to higher sales is silent about the dips and attributes the spike entirely to new demand. In survey and administrative data from 13,000 gas station managers, more than 20 percent completely neglect IS, and those holding the simple model perceive demand as more elastic and set lower prices, which leads to lower profits. Online experiments provide causal evidence. Holding the data fixed, subjects assigned the simple model are less likely to recall the dips and more likely to neglect IS. Two interventions further support the mechanism. Prompting attention to the dips lowers neglect of IS, and encountering a second model dislodges the incumbent, but only if it draws attention to data features neglected by the incumbent.
About the SBEE Seminar Series
The Social, Behavioral and Experimental Economics seminar series brings together a community of economics scholars from three units at the University of Michigan — the School of Information, the Department of Economics and the Ross Business School — whose research aims to broaden the understanding of the social, economic and political consequences of real-life decisions and behaviors.
Top researchers from around the globe come to Michigan to present their work at the SBEE seminar series, exploring the intersection of economics, psychology, computer science and information science.
The seminar series is organized by U-M faculty members Yan Chen (UMSI), Alain Cohn (UMSI), Erin Krupka (UMSI), Stephen Leider (Ross), Christine Exley (Econ), A. Yesim Orhun (Ross), Tanya Rosenblat (UMSI), Karthik Srinivasan (UMSI) and Basit Zafar (Econ). Todd Stuart and Robin Kocher serve as seminar coordinators.
Featured Speaker
David Huffman
Cornell University
David Huffman is the Ashley Family Professor in Behavioral Economics at the Cornell SC Johnson College of Business. He serves as editor-in-chief of Frontiers in Behavioral Economics, co-editor at Experimental Economics, and associate editor at Management Science. He is an IZA Fellow, a CESifo network member, and an associate member of Nuffield College, Oxford. Previously, he was a distinguished professor at the University of Pittsburgh and an associate professor at the University of Oxford, where he also served as tutorial fellow at St. Catherine’s College. He earned his PhD in economics from UC Berkeley and a BA with high honors in economics from Oberlin College.
Huffman’s research lies at the intersection of behavioral economics, labor economics, and personnel economics. His work has been published in leading journals including the American Economic Review, Quarterly Journal of Economics, Review of Economic Studies, Journal of the European Economic Association, Management Science, American Economic Journal: Microeconomics, and Journal of Labor Economics. He has partnered with firms across various industries to deliver business-relevant insights.
One strand of his research examines the determinants of human decision-making — particularly preferences regarding risk, time, and social interactions, as well as trust and motivated beliefs — documenting how these vary globally and across cultures. Another applies behavioral insights to questions such as how workers respond to incentives, how relational contracting operates across institutional environments, and mechanisms underlying gender differences in labor market outcomes. His recent work focuses on behavioral economics within firms, particularly how behavioral biases and bounded rationality influence strategic behavior, and the mechanisms that lead decision makers to maintain persistently biased beliefs even in data-rich environments. He employs diverse methodologies including field experiments, laboratory experiments, surveys, and econometric analysis of large datasets using machine learning and natural language processing.
Contact: [email protected]