Designing Intelligent Software Agents for Auctions with Limited Information Feedback

Gediminas Adomavicius (), Alok Gupta () and Dmitry Zhdanov ()
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Gediminas Adomavicius: Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Alok Gupta: Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455
Dmitry Zhdanov: School of Business, University of Connecticut, Storrs, Connecticut 06269

Information Systems Research, 2009, vol. 20, issue 4, 507-526

Abstract: This paper presents analytical, computational, and empirical analyses of strategies for intelligent bid formulations in online auctions. We present results related to a weighted-average ascending price auction mechanism that is designed to provide opaque feedback information to bidders and presents a challenge in formulating appropriate bids. Using limited information provided by the mechanism, we design strategies for software agents to make bids intelligently. In particular, we derive analytical results for the important characteristics of the auction, which allow estimation of the key parameters; we then use these theoretical results to design several bidding strategies. We demonstrate the validity of designed strategies using a discrete event simulation model that resembles the mechanisms used in treasury bills auctions, business-to-consumer (B2C) auctions, and auctions for environmental emission allowances. In addition, using the data generated by the simulation model, we show that intelligent strategies can provide a high probability of winning an auction without significant loss in surplus.

Keywords: online auctions; intelligent agents; software agents; limited information feedback; bidding strategies; discrete event simulation; heuristics; parameter estimation (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:20:y:2009:i:4:p:507-526

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