Synchronous and asynchronous auction models for dynamic spectrum access
ICDCN'06 Proceedings of the 8th international conference on Distributed Computing and Networking
Generating realistic online auction data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
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The online Yankee auction sells multiple units of the same good to multiple buyers using an ascending and open auction mechanism. One of the important controllable factors of the Yankee auction is the minimum bid increment, specified at the beginning of an auction. Bidders can bid only in the multiples of this bid increment even if they are willing to bid higher than the minimum required bid. This paper presents a simulation approach to optimize sellers' revenue, which is based on theoretical properties of Yankee auctions and utilizes data from real auctions to replicate a given auction. The simulation model can be configured to change the value of bid increment and explore whether a given auction used the optimal bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from optimal choice of bid increment, resulting in substantial losses in a market with already tight margins.