TY - GEN
T1 - Computationally efficient and revenue optimized auctioneer's strategy for expanding auctions
AU - Shehory, Onn
AU - Dror, Eran
PY - 2006
Y1 - 2006
N2 - An expanding auction - a special type of an ascending-price open-cry auction - allows the auctioneer to dynamically add (identical) items to the single item offered initially. Expanding auctions are becoming an increasingly popular business-toconsumer auction mechanism. The auctioneer's revenue from an expanding auction depends, in large part, on its schedule for increasing the number of units offered. As we show, the naïve strategies commonly used for increasing the number of items offered in contemporary e-commerce implementations of expanding auctions are sub-optimal. In this study we provide a strategy that, given some assumptions about buyers' behaviors, maximizes the expected revenues of the auctioneer in an expanding auction. We model the auction process as a state graph in which nodes are auction states and edges are transitions. With this model, finding the optimal strategy is equivalent to solving a search problem on the state graph. We prove that the search problem to be solved, although seemingly exponentially complex, is actually linearly bounded. Based on this result, we introduce an informed decision strategy that optimizes the auctioneer's revenue.
AB - An expanding auction - a special type of an ascending-price open-cry auction - allows the auctioneer to dynamically add (identical) items to the single item offered initially. Expanding auctions are becoming an increasingly popular business-toconsumer auction mechanism. The auctioneer's revenue from an expanding auction depends, in large part, on its schedule for increasing the number of units offered. As we show, the naïve strategies commonly used for increasing the number of items offered in contemporary e-commerce implementations of expanding auctions are sub-optimal. In this study we provide a strategy that, given some assumptions about buyers' behaviors, maximizes the expected revenues of the auctioneer in an expanding auction. We model the auction process as a state graph in which nodes are auction states and edges are transitions. With this model, finding the optimal strategy is equivalent to solving a search problem on the state graph. We prove that the search problem to be solved, although seemingly exponentially complex, is actually linearly bounded. Based on this result, we introduce an informed decision strategy that optimizes the auctioneer's revenue.
KW - Auctioneer's revenue
KW - Expanding auction
UR - http://www.scopus.com/inward/record.url?scp=34247223714&partnerID=8YFLogxK
U2 - 10.1145/1160633.1160845
DO - 10.1145/1160633.1160845
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AN - SCOPUS:34247223714
SN - 1595933034
SN - 9781595933034
T3 - Proceedings of the International Conference on Autonomous Agents
SP - 1175
EP - 1182
BT - Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
T2 - Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Y2 - 8 May 2006 through 12 May 2006
ER -