Abstract
Online auctions are a profitable, exciting, and dynamic part of electronic commerce, and behave in ways, which do not match classical auction theory. This paper models an online auction in terms of a Markov chain on a state space defined by the current price of the item and the number of bidders who have been previously “bumped”. It provides a mathematical model, some approximations, which were necessary to convert it into a tractable problem and solutions to a small and a medium-sized theoretical auction. The model results were validated through a comparison with real-world online auction data, showing promise as a predictor of final auction prices. The results of the auction model are also useful in solving an optimization problem that incorporates inventory management considerations in determining optimal auction size.
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