Peter Stone's Selected Publications

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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. Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge. Informs Journal on Computing, 22(3):353–370, 2010.

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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.

BibTeX Entry

@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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