Approximately optimal auctions for correlated bidders

We consider the design of dominant strategy incentive compatible, revenue-maximizing auctions for an indivisible good, when bidders' values are drawn from a correlated distribution. For independent distributions, Myerson showed that the optimal auction for risk-neutral bidders remains incentive...

Full description

Saved in:
Bibliographic Details
Published in:Games and economic behavior Vol. 92; pp. 349 - 369
Main Authors: Dobzinski, Shahar, Fu, Hu, Kleinberg, Robert
Format: Journal Article
Language:English
Published: Duluth Elsevier Inc 01.07.2015
Academic Press
Subjects:
Algorithms
Approximation Algorithms
Attitudes
Auctions
Bayesian mechanism design
Bidders
Game theory
Incentives
Probability distribution
Profit maximization
Revenue
Risk
Risk assessment
Studies
Time
Bayesian mechanism design
Approximation Algorithms
D82
ISSN:0899-8256, 1090-2473
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider the design of dominant strategy incentive compatible, revenue-maximizing auctions for an indivisible good, when bidders' values are drawn from a correlated distribution. For independent distributions, Myerson showed that the optimal auction for risk-neutral bidders remains incentive compatible regardless of bidders' risk attitudes. We show that, for correlated distributions, the same is true when only two bidders are involved, whereas for more bidders, randomization can generate strictly more revenue. However, for risk-neutral bidders, we show this gain is never more than a 53-factor. This is a consequence of two results of independent interest: (1) a polynomial-time derandomization of auctions for two bidders; (2) a polynomial-time computable deterministic auction that 53-approximates the optimal revenue extractable from risk-neutral bidders. Moreover, we give a polynomial-time algorithm to compute optimal auctions for a constant number of bidders, and for any number of bidders we give polynomial-time algorithms with approximation factors arbitrarily close to 32.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0899-8256
1090-2473
DOI:10.1016/j.geb.2013.03.010