TY - JOUR
T1 - Designing intelligent software agents for auctions with limited information feedback
AU - Adomavicius, Gediminas
AU - Gupta, Alok
AU - Zhdanov, Dmitry
PY - 2009/12
Y1 - 2009/12
N2 - 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.
AB - 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.
KW - Bidding strategies
KW - Discrete event simulation
KW - Heuristics
KW - Intelligent agents
KW - Limited information feedback
KW - Online auctions
KW - Parameter estimation
KW - Software agents
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U2 - 10.1287/isre.1080.0172
DO - 10.1287/isre.1080.0172
M3 - Article
AN - SCOPUS:77954260125
SN - 1047-7047
VL - 20
SP - 507
EP - 526
JO - Information Systems Research
JF - Information Systems Research
IS - 4
ER -