Generative model of bid sequences in lowest unique bid auctions

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

Abstract

Lowest unique bid auction (LUBA) sites are gaining popularity on the Internet in recent years. In this paper, we study LUBA with resubmission in discrete bid spaces. A long-standing goal in the field of Internet auction is to develop agents that can perceive and understand the strategy information behind the mechanism and can guide us to behave in a fast, frugal and smart way. We marry ideas from recurrent neural network and data to learn a generative model for generating winning bid sequences. A sequence of winning bids in Internet auctions can be viewed as a sequence of events and modeled by generative models. We learn a model that is able to capture the long dependencies in a winning bid sequence. The generated data obtained from our model and the ground truth dataset share similar distributions.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3179-3184
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
  • game theory
  • generative model
  • recurrent neural network

ASJC Scopus subject areas

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

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  • Cite this

    Xu, Y., & Tembine, H. (2018). Generative model of bid sequences in lowest unique bid auctions. In Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018 (pp. 3179-3184). (Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC.2018.8407671