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 language | English |
---|---|
Title of host publication | Web and Internet Economics - 12th International Conference, WINE 2016, Proceedings |
Editors | Adrian Vetta, Yang Cai |
Publisher | Springer Verlag |
Pages | 400-413 |
Number of pages | 14 |
ISBN (Print) | 9783662541098 |
DOIs | |
State | Published - 2016 |
Event | 12th International Conference on Web and Internet Economics, WINE 2016 - Montreal, Canada Duration: 11 Jun 2016 → 14 Jul 2016 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 10123 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Conference on Web and Internet Economics, WINE 2016 |
---|---|
Country/Territory | Canada |
City | Montreal |
Period | 11/06/16 → 14/07/16 |
Bibliographical note
Publisher Copyright:© Springer-Verlag GmbH Germany 2016.