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Adaptive Mechanism Design: A Metalearning Approach.
David Pardoe,
Peter Stone, Maytal
Saar-Tsechansky, and Kerem Tomak.
In The Eighth International Conference
on Electronic Commerce, pp. 92–102, August 2006.
ICEC 2006. Contains material
from
[PDF]334.2kB [postscript]511.6kB
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.
@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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