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Published March 4, 2016 | Updated + Accepted Version
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Quantal Response and Nonequilibrium Beliefs Explain Overbidding in Maximum-Value Auctions


We report an experiment on a simple common value auction to investigate the extent to which bidding can be explained by quantal response equilibrium, in combination with different assumptions about the structure of bidder beliefs|the cursed equilibrium model and models that posit levels of strategic sophistication. Using a structural estimation approach, we find a close correspondence between the theoretical predictions of those models and experimental behavior. The basic pattern of average bids in the data consists of a combination of overbidding for low signals, and value-bidding for higher signals. The logit QRE model with heterogeneous bidders fits this pattern reasonably well. Combining quantal response with either cursed beliefs (CE-QRE) or a level-k of strategic sophistication (LK-QRE, CH-QRE) leads to a close match with the data. All these variations on quantal response models predict minimal differences of average bidding behavior across different versions of the game, consistent with the experimental findings. Finally, we reanalyze data from an earlier experiment on the same auction by Ivanov, Levin and Niederle (2010). While their data exhibit much more variance compared with ours, nonetheless, we still find that these models also fit their data reasonably well, even in the presence of extreme overbidding observed in that experiment. Overall, our study indicates that the winner curse phenomenon in this auction is plausibly attributable to limits on strategic thinking combined with quantal response.

Additional Information

January 13, 2011. Added June 2011. Updated December 11, 2014. We are grateful for comments from seminar audiences at the 2010 North-American ESA Meeting in Tucson, the Security Market Auctions and IPOs Conference at Northwestern University, the University of Southern California, and Stanford University. We thank Asen Ivanov, Dan Levin, and Muriel Niederle for sharing their data and for detailed comments on an earlier draft. Palfrey thanks NSF and Russell Sage Foundation for financial support.

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Accepted Version - sswp1349.pdf

Updated - sswp_1349.pdf


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