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Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge.
David
Pardoe, Peter Stone, Maytal
Saar-Tsechansky, Tayfun Keskin, and Kerem Tomak.
Informs Journal
on Computing, 22(3):353–370, 2010.
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Electronic auction markets are economic information systems that facilitate transactions between buyers and sellers. Whereas auction design has traditionally been an analytic process that relies on theory-driven assumptions such as bidders' rationality, bidders often exhibit unknown and variable behaviors. In this paper we present a data-driven adaptive auction mechanism that capitalizes on key properties of electronic auction markets, such as the large transaction volume, access to information, and the ability to dynamically alter the mechanism's design to acquire information about the benefits from different designs and adapt the auction mechanism online in response to actual bidders' behaviors. Our auction mechanism does not require an explicit representation of bidder behavior to infer about design profitability--a key limitation of prior approaches when they address complex auction settings. Our adaptive mechanism can also incorporate prior general knowledge of bidder behavior to enhance the search for effective designs. The data-driven adaptation and the capacity to use prior knowledge render our mechanisms particularly useful when there is uncertainty regarding bidders' behaviors or when bidders' behaviors change over time. Extensive empirical evaluations demonstrate that the adaptive mechanism outperforms any single fixed mechanism design under a variety of settings, including when bidders' strategies evolve in response to the seller's adaptation; our mechanism's performance is also more robust than that of alternatives when prior general information about bidders' behaviors differs from the encountered behaviors.
@article{INFORMS10-pardoe,
author = {David Pardoe and Peter Stone and Maytal Saar-Tsechansky and Tayfun Keskin and Kerem Tomak},
title = {{Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge}},
journal = {Informs Journal on Computing},
volume = {22},
number = {3},
pages = {353-370},
year = {2010},
abstract = {
Electronic auction markets are economic information systems that
facilitate transactions between buyers and sellers. Whereas auction
design has traditionally been an analytic process that relies on
theory-driven assumptions such as bidders' rationality, bidders often
exhibit unknown and variable behaviors. In this paper we present a
data-driven adaptive auction mechanism that capitalizes on key
properties of electronic auction markets, such as the large transaction
volume, access to information, and the ability to dynamically alter the
mechanism's design to acquire information about the benefits from
different designs and adapt the auction mechanism online in response to
actual bidders' behaviors. Our auction mechanism does not require an
explicit representation of bidder behavior to infer about design
profitability--a key limitation of prior approaches when they address
complex auction settings. Our adaptive mechanism can also incorporate
prior general knowledge of bidder behavior to enhance the search for
effective designs. The data-driven adaptation and the capacity to use
prior knowledge render our mechanisms particularly useful when there is
uncertainty regarding bidders' behaviors or when bidders' behaviors
change over time. Extensive empirical evaluations demonstrate that the
adaptive mechanism outperforms any single fixed mechanism design under a
variety of settings, including when bidders' strategies evolve in
response to the seller's adaptation; our mechanism's performance is also
more robust than that of alternatives when prior general information
about bidders' behaviors differs from the encountered behaviors.
},
url = {http://joc.journal.informs.org/cgi/content/abstract/ijoc.1090.0353v1},
}
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