Approximating gains-from-trade in bilateral trading

Liad Blumrosen*, Yehonatan Mizrahi

*Corresponding author for this work

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

23 Scopus citations

Abstract

We consider the design of platforms that facilitate trade between a single seller and a single buyer. The most efficient mechanisms for such settings are complex and sometimes even intractable, and we therefore aim to design simple mechanisms that perform approximately well. We devise a mechanism that always guarantees at least 1/e of the optimal expected gain-from-trade for every set of distributions (assuming monotone hazard rate of the buyer’s distribution). Our main mechanism is extremely simple, and achieves this approximation in Bayes-Nash equilibrium. Moreover, our mechanism approximates the optimal gain-from-trade, which is a strictly harder task than approximating efficiency. Our main impossibility result shows that no Bayes-Nash incentive compatible mechanism can achieve better approximation than 2/e to the optimal gain from trade. We also bound the power of Bayes- Nash incentive compatible mechanisms for approximating the expected efficiency.

Original languageAmerican English
Title of host publicationWeb and Internet Economics - 12th International Conference, WINE 2016, Proceedings
EditorsAdrian Vetta, Yang Cai
PublisherSpringer Verlag
Pages400-413
Number of pages14
ISBN (Print)9783662541098
DOIs
StatePublished - 2016
Event12th International Conference on Web and Internet Economics, WINE 2016 - Montreal, Canada
Duration: 11 Jun 201614 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10123 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Web and Internet Economics, WINE 2016
Country/TerritoryCanada
CityMontreal
Period11/06/1614/07/16

Bibliographical note

Publisher Copyright:
© Springer-Verlag GmbH Germany 2016.

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