Characterization of revenue monotonicity in combinatorial auctions

Taiki Todo, Atsushi Iwasaki, Makoto Yokoo

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

2 Citations (Scopus)

Abstract

An auction mechanism consists of an allocation rule and a payment rule. There have been several studies on characterizing strategy-proof allocation rules; if the allocation rule satisfies a condition called weak-monotonicity, an appropriate payment rule is guaranteed to exist. One desirable property that an auction mechanism should satisfy is revenue monotonicity; a seller's revenue is guaranteed to weakly increase as the number of bidders grows. In this paper, we first identify a simple condition called summation-monotonicity for characterizing strategy-proof and revenue monotone allocation rules. To the best of our knowledge, this is the first attempt to characterize revenue monotone allocation rules. Based on this characterization, we also examine the connections between revenue monotonicity and false-name-proofness, which means a bidder cannot increase his utility by submitting multiple bids under fictitious names. In a single-item auction, we show that they are basically equivalent; a mechanism is false-name-proof if and only if it is strategy-proof and revenue monotone. On the other hand, we show these two conditions cannot coexist in combinatorial auctions under some minor condition.

Original languageEnglish
Title of host publicationProceedings - 2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010
Pages383-390
Number of pages8
DOIs
Publication statusPublished - Dec 13 2010
Event2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010 - Toronto, ON, Canada
Duration: Aug 31 2010Sept 3 2010

Publication series

NameProceedings - 2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010
Volume2

Other

Other2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010
Country/TerritoryCanada
CityToronto, ON
Period8/31/109/3/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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