The effects of proxy bidding and minimum bid increments within eBay auctions

We present a mathematical model of the eBay auction protocol and perform a detailed analysis of the effects that the eBay proxy bidding system and the minimum bid increment have on the auction properties. We first consider the revenue of the auction, and we show analytically that when two bidders with independent private valuations use the eBay proxy bidding system there exists an optimal value for the minimum bid increment at which the auctioneer's revenue is maximized. We then consider the sequential way in which bids are placed within the auction, and we show analytically that independent of assumptions regarding the bidders' valuation distribution or bidding strategy the number of visible bids placed is related to the logarithm of the number of potential bidders. Thus, in many cases, it is only a minority of the potential bidders that are able to submit bids and are visible in the auction bid history (despite the fact that the other hidden bidders are still effectively competing for the item). Furthermore, we show through simulation that the minimum bid increment also introduces an inefficiency to the auction, whereby a bidder who enters the auction late may find that its valuation is insufficient to allow them to advance the current bid by the minimum bid increment despite them actually having the highest valuation for the item. Finally, we use these results to consider appropriate strategies for bidders within real world eBay auctions. We show that while last-minute bidding (sniping) is an effective strategy against bidders engaging in incremental bidding (and against those with common values), in general, delaying bidding is disadvantageous even if delayed bids are sure to be received before the auction closes. Thus, when several bidders submit last-minute bids, we show that rather than seeking to bid as late as possible, a bidder should try to be the first sniper to bid (i.e., it should “snipe before the snipers”).

[1]  Kevin Leyton-Brown,et al.  Bidding agents for online auctions with hidden bids , 2007, Machine Learning.

[2]  Nicholas R. Jennings,et al.  Sellers Competing for Buyers in Online Markets: Reserve Prices, Shill Bids, and Auction Fees , 2007, IJCAI.

[3]  José M. Vidal,et al.  Agents on the Web: Online Auctions , 1999, IEEE Internet Comput..

[4]  Ali Hortaçsu,et al.  Economic Insights from Internet Auctions , 2004 .

[5]  J. Morgan,et al.  ...Plus Shipping and Handling: Revenue (Non) Equivalence in Field Experiments on eBay , 2006 .

[6]  Unjy Song,et al.  Nonparametric Estimation of an eBay Auction Model with an Unknown Number of Bidders* , 2004 .

[7]  Philip A. Haile,et al.  Inference with an Incomplete Model of English Auctions , 2000, Journal of Political Economy.

[8]  David Lucking-Reiley AUCTIONS ON THE INTERNET: WHAT'S BEING AUCTIONED, AND HOW?* , 2003 .

[9]  Axel Ockenfels,et al.  Chapter 12 Online Auctions , 2006 .

[10]  Marcus Fontoura,et al.  Law-governed peer-to-peer auctions , 2002, WWW '02.

[11]  David H. Reiley Auctions on the Internet: What's Being Auctioned, and How? , 2000 .

[12]  Guido Governatori,et al.  A probabilistic approach to automated bidding in alternative auctions , 2002, WWW '02.

[13]  Ronald M. Harstad,et al.  An Alternating Recognition Model of English Auctions , 2000 .

[14]  Xin Guo,et al.  An optimal strategy for sellers in an online auction , 2002, TOIT.

[15]  Paul R. Milgrom,et al.  A theory of auctions and competitive bidding , 1982 .

[16]  Ali Hortaçsu,et al.  Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights from Ebay Auctions , 2003 .

[17]  K. Johansson THE LONGEST INCREASING SUBSEQUENCE IN A RANDOM PERMUTATION AND A UNITARY RANDOM MATRIX MODEL , 1998 .

[18]  Ashish Sureka,et al.  Mining eBay: Bidding Strategies and Shill Detection , 2002, WEBKDD.

[19]  A. Roth,et al.  Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet , 2002 .

[20]  Alvin E. Roth,et al.  Late and Multiple Bidding in Second Price Internet Auctions: Theory and Evidence Concerning Different Rules for Ending an Auction , 2003, Games Econ. Behav..

[21]  Ravi Bapna,et al.  When snipers become predators: can mechanism design save online auctions? , 2003, CACM.

[22]  Sarit Kraus,et al.  Optimal design of English auctions with discrete bid levels , 2005, EC '05.

[23]  Axel Ockenfels,et al.  Online Auctions , 2006 .

[24]  Michael H. Rothkopf,et al.  On the role of discrete bid levels in oral auctions , 1994 .

[25]  Nicholas R. Jennings,et al.  Developing a bidding agent for multiple heterogeneous auctions , 2003, TOIT.