Mar. 8, Tony Kwasnica, Ph.D.A Laboratory Comparison of Auctions and Sequential Mechanisms

Abstract: When bidders incur a cost to learn their valuations, bidder entry can impact auction performance. Two common selling mechanisms in this environment are an English auction, and a sequential bidding process. Bulow and Klemperer (2009) show, theoretically, that sellers should prefer the auction, because it generates higher expected revenues, while bidders should prefer the sequential mechanism, because it generates higher expected bidder profits.  We compare the two  mechanisms in a controlled laboratory environment, varying the entry cost, and find that, contrary to the theoretical predictions, average seller revenues tend to be the higher under the sequential mechanism, while average bidder profits are approximately the same. We identify three systematic behavioral deviations from the theoretical model: (1) bidders do not enter the auction 100% of the time, (2) in the sequential mechanism, bidders do not set pre-emptive bids according to the predicted threshold strategy, and (3) subsequent bidders tend to over-enter in response to pre-emptive bids by first bidders. We develop a model of noisy bidder entry costs that is consistent with these behaviors, and show that our model organizes the experimental data well.Dr. Tony Kwasnica, a guest of the IFREE/ESI Lecture Series

Bio: Anthony M. Kwasnica is Associate Professor of Business Economics and Director of the Laboratory for Economic Management and Auctions (LEMA) at Penn State University. He studies the role of information in economic systems. He conducts both theoretical and experimental (laboratory) research on how individuals and institutions respond to these situations. In addition to teaching undergraduate courses in mathematical modeling and decision analysis, Dr. Kwasnica has taught microeconomics, macroeconomics and decision-making under uncertainty to executives and MBAs at both The Smeal College of Business and The Kellogg School of Management.  Dr. Kwasnica received his PhD in Social Science from the California Institute of Technology.

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