The power of fair pricing mechanisms

Christine Chung*, Katrina Ligett, Kirk Pruhs, Aaron L. Roth

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We explore the revenue capabilities of truthful, monotone ("fair") allocation and pricing functions for resource-constrained auction mechanisms within a general framework that encompasses unlimited supply auctions, knapsack auctions, and auctions with general non-decreasing convex production cost functions. We study and compare the revenue obtainable in each fair pricing scheme to the profit obtained by the ideal omniscient multi-price auction. We show (1) for capacitated knapsack auctions, no constant pricing scheme can achieve any approximation to the optimal profit, but proportional pricing is as powerful as general monotone pricing, and (2) for auction settings with arbitrary bounded non-decreasing convex production cost functions, we present a proportional pricing mechanism which achieves a poly-logarithmic approximation. Unlike existing approaches, all of our mechanisms have fair (monotone) prices, and all of our competitive analysis is with respect to the optimal profit extraction.

Original languageAmerican English
Title of host publicationLATIN 2010
Subtitle of host publicationTheoretical Informatics - 9th Latin American Symposium, Proceedings
Number of pages11
StatePublished - 2010
Externally publishedYes
Event9th Latin American Theoretical Informatics Symposium, LATIN 2010 - Oaxaca, Mexico
Duration: 19 Apr 201023 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6034 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Latin American Theoretical Informatics Symposium, LATIN 2010


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