Electronic Thesis/Dissertation
 

THE SECOND CHANCE OFFER: SELLER AND BIDDER STRATEGIES

Open Access

The second chance offer is a common seller practice on eBay. It consists of price discrimination against the losing bidder, who is offered an identical item at the value of his or her highest bid. Prior work has shown that, if the price discrimination is certain--that is, the items are always offered to bidders at their highest losing bids--bidders can predict it, and it results in revenue loss for the seller. This dissertation hence allows the seller to randomize his strategy. It examines a similar, more general problem: a seller has k items. They are sold to n bidders in a two-stage game. The first stage is a sealed-bid private-value auction with n bidders. The second stage is a take-it-or-leave-it offer to each of k-1 losing bidders; randomized between a fixed-price offer and a second-chance offer. Showing that analytic techniques do not provide complete solutions because bidding strategies are not always monotonic increasing, this dissertation uses genetic algorithm simulations to determine the Bayesian (near-Nash) equilibrium strategies for bidders and sellers, for n = 8 and different values of k. It analyzes item scarcity and two types of auction mechanisms for the first stage: first-price auction and second-price auction. It tests the approach on real eBay data, and a rational bidding tool is implemented to illustrate the practical use of this model on eBay. This dissertation's use of randomized seller strategies and genetic algorithm simulations is unique in the study of the second-chance offer.

Author Language Keyword Date created Type of Work License
  • All rights reserved
Rights statement GW Unit Degree Advisor Committee Member(s) Persistent URL

Notice to Authors

If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.

Thumbnail Title Date Uploaded Visibility Actions
Preview of Sun_gwu_0075A_10055.pdf Sun_gwu_0075A_10055.pdf 2018-01-16 Open Access