Optimal bidding algorithms against cheating in multiple-object auctions

Ming Yang Kao, Junfeng Qi, Lei Tan

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


This paper studies some basic problems in a multiple-object auction model using methodologies from theoretical computer science. We are especially concerned with situations where an adversary bidder knows the bidding algorithms of all the other bidders. In the two-bidder case, we derive an optimal randomized bidding algorithm, by which the disadvantaged bidder can procure at least half of the auction objects despite the adversary's a priori knowledge of his algorithm. In the general k-bidder case, if the number of objects is a multiple of k, an optimal randomized bidding algorithm is found. If the k − 1 disadvantaged bidders employ that same algorithm, each of them can obtain at least 1/k of the objects regardless of the bidding algorithm the adversary uses. These two algorithms are based on closed-form solutions to certain multivariate probability distributions. In situations where a closed-form solution cannot be obtained, we study a restricted class of bidding algorithms as an approximation to desired optimal algorithms.

Original languageEnglish (US)
Title of host publicationComputing and Combinatorics - 3rd Annual International Conference COCOON 1997, Proceedings
EditorsTao Jiang, D.T. Lee
PublisherSpringer Verlag
Number of pages10
ISBN (Print)354063357X, 9783540633570
StatePublished - 1997
Event3rd Annual International Computing and Combinatorics Conference, COCOON 1997 - Shanghai, China
Duration: Aug 20 1997Aug 22 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd Annual International Computing and Combinatorics Conference, COCOON 1997

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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