Lowest unique bid auctions with resubmission opportunities

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

Abstract

The recent online platforms propose multiple items for bidding. The state of the art, however, is limited to the analysis of one item auction. In this paper we study multi-item lowest unique bid auctions (LUBA) in discrete bid spaces under budget constraints. We show the existence of mixed Bayes-Nash equilibria for an arbitrary number of bidders and items. The equilibrium is explicitly computed in two bidder setup with resubmission possibilities. In the general setting we propose a distributed strategic learning algorithm to approximate equilibria. Computer simulations indicate that the error quickly decays in few number of steps by means of speedup techniques. When the number of bidders per item follows a Poisson distribution, it is shown that the seller can get a non-negligible revenue on several items, and hence making a partial revelation of the true value of the items.

Original languageEnglish (US)
Title of host publicationICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Jaap van den Herik
PublisherSciTePress
Pages330-337
Number of pages8
ISBN (Electronic)9789897582752
DOIs
StatePublished - 2018
Event10th International Conference on Agents and Artificial Intelligence, ICAART 2018 - Funchal, Madeira, Portugal
Duration: Jan 16 2018Jan 18 2018

Publication series

NameICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence
Volume2

Other

Other10th International Conference on Agents and Artificial Intelligence, ICAART 2018
CountryPortugal
CityFunchal, Madeira
Period1/16/181/18/18

Keywords

  • Auction
  • Game Theory
  • Imitative Learning
  • LUBA
  • Reinforcement Learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence

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