Peter Stone's Selected Publications

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Adaptive Mechanism Design: A Metalearning Approach

David Pardoe, Peter Stone, Maytal Saar-Tsechansky, and Kerem Tomak. Adaptive Mechanism Design: A Metalearning Approach. In The Eighth International Conference on Electronic Commerce, pp. 92–102, August 2006.
ICEC 2006. Contains material from Adaptive Auctions: Learning to Adjust to Bidders, Workshop on Information Technologies and Systems (WITS), 2005.

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Abstract

Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders often exhibit unknown and variable behavior, making them difficult to model and complicating the design process. To address this challenge, we explore the use of an adaptive auction mechanism: one that learns to adjust its parameters in response to past empirical bidder behavior so as to maximize an objective function such as auctioneer revenue. In this paper, we give an overview of our general approach and then present an instantiation in a specific auction scenario. In addition, we show how predictions of possible bidder behavior can be incorporated into the adaptive mechanism through a metalearning process. The approach is fully implemented and tested. Results indicate that the adaptive mechanism is able to outperform any single fixed mechanism, and that the addition of metalearning improves performance substantially.

BibTeX Entry

@InProceedings{ICEC06,
	author="David Pardoe and Peter Stone and Maytal Saar-Tsechansky and Kerem Tomak",
	title="Adaptive Mechanism Design: A Metalearning Approach",
	booktitle="The Eighth International Conference on Electronic Commerce",	
	month="August",year="2006",
	pages="92--102",
	abstract={
                  Auction mechanism design has traditionally been a
                  largely analytic process, relying on assumptions
                  such as fully rational bidders.  In practice,
                  however, bidders often exhibit unknown and variable
                  behavior, making them difficult to model and
                  complicating the design process.  To address this
                  challenge, we explore the use of an adaptive auction
                  mechanism: one that \emph{learns} to adjust its
                  parameters in response to past empirical bidder
                  behavior so as to maximize an objective function
                  such as auctioneer revenue.  In this paper, we give
                  an overview of our general approach and then present
                  an instantiation in a specific auction scenario.  In
                  addition, we show how predictions of possible bidder
                  behavior can be incorporated into the adaptive
                  mechanism through a \emph{metalearning} process.
                  The approach is fully implemented and tested.
                  Results indicate that the adaptive mechanism is able
                  to outperform any single fixed mechanism, and that
                  the addition of metalearning improves performance
                  substantially.
		  },
	wwwnote={<a href="http://icec06.net/">ICEC 2006</a>.  Contains material from <a
href="http://www.cs.utexas.edu/~pstone/Papers/2005wits.pdf"><b>Adaptive Auctions:  Learning to Adjust to Bidders</b></a>,  Workshop on Information Technologies and Systems (<a href="http://wits2005.ecom.arizona.edu/">WITS</a>), 2005.},
}

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