TY - GEN
T1 - Distributed representations of bids in lowest unique bid auctions
AU - Xu, Yida
AU - Tembine, Hamidou
N1 - Funding Information:
We gratefully acknowledge support from U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/6
Y1 - 2018/7/6
N2 - In this paper, we study multi-item lowest unique bid auctions (LUBA) with resubmission under budget constraints. A representation of bids that can perceive the rich strategic meaning in the world of multi-item LUBA with resubmission is learned by an efficient method-Skip-gram model. We created and maintained a dataset which contains the relevant information of LUBA, including the winning bid combination, budget constraints, and the number of participated bidders. An Android-based application is developed and contributes the construction of the dataset. The quantitative analysis displays that the representation cluster can reflect similarities in terms of numerical information, cultural and aesthetic preference, and other bidder's behaviors. The learned representation can serve as a guide for bidders who seek to maximize their payoff and feed into a sequence generation model, such as recurrent neural network, to produce the bids combination.
AB - In this paper, we study multi-item lowest unique bid auctions (LUBA) with resubmission under budget constraints. A representation of bids that can perceive the rich strategic meaning in the world of multi-item LUBA with resubmission is learned by an efficient method-Skip-gram model. We created and maintained a dataset which contains the relevant information of LUBA, including the winning bid combination, budget constraints, and the number of participated bidders. An Android-based application is developed and contributes the construction of the dataset. The quantitative analysis displays that the representation cluster can reflect similarities in terms of numerical information, cultural and aesthetic preference, and other bidder's behaviors. The learned representation can serve as a guide for bidders who seek to maximize their payoff and feed into a sequence generation model, such as recurrent neural network, to produce the bids combination.
KW - LUBA
KW - auction
KW - embedding space
KW - game theory
UR - http://www.scopus.com/inward/record.url?scp=85050888536&partnerID=8YFLogxK
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U2 - 10.1109/CCDC.2018.8407670
DO - 10.1109/CCDC.2018.8407670
M3 - Conference contribution
AN - SCOPUS:85050888536
T3 - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
SP - 3173
EP - 3178
BT - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th Chinese Control and Decision Conference, CCDC 2018
Y2 - 9 June 2018 through 11 June 2018
ER -