Distributed representations of bids in lowest unique bid auctions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3173-3178
Number of pages6
ISBN (Electronic)9781538612439
DOIs
StatePublished - Jul 6 2018
Event30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China
Duration: Jun 9 2018Jun 11 2018

Publication series

NameProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

Other

Other30th Chinese Control and Decision Conference, CCDC 2018
CountryChina
CityShenyang
Period6/9/186/11/18

Keywords

  • LUBA
  • auction
  • embedding space
  • game theory

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Decision Sciences (miscellaneous)

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