Uncertainty in Artificial Intelligence
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Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions
Anna Osepayshvili, Michael Wellman, Daniel Reeves, Jeffrey K. MacKie-Mason
Simultaneous ascending auctions present agents with the exposure problem: bidding to acquire a bundle risks the possibility of obtaining an undesired subset of the goods. Auction theory provides little guidance for dealing with this problem. We present a new family of decisiontheoretic bidding strategies that use probabilistic predictions of final prices. We focus on selfconfirming price distribution predictions, which by definition turn out to be correct when all agents bid decision-theoretically based on them. Bidding based on these is provably not optimal in general, but our experimental evidence indicates the strategy can be quite effective compared to other known methods.
Pages: 441-449
PS Link:
PDF Link: /papers/05/p441-osepayshvili.pdf
AUTHOR = "Anna Osepayshvili and Michael Wellman and Daniel Reeves and Jeffrey K. MacKie-Mason",
TITLE = "Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "441--449"

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