Non-Optimal Mechanism Design

The optimal allocation of resources in complex environments—like allocation of dynamic wireless spectrum, cloud computing services, and Internet advertising—is computationally challenging even given the true preferences of the participants. In the theory and practice of optimization in complex envir...

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Bibliographic Details
Published in:The American economic review 2015-10, Vol.105 (10), p.3102-3124
Main Authors: Hartline, Jason D., Lucier, Brendan
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Auctions
Bandwidths
Bayesian analysis
Bayesian method
Broadcasting industry
Budget allocation
Computer engineering
Design
Economic theory
Economic value
Game theory
Heuristic
Incentive pay
Incentives
Mechanism design
Optimization
Resource allocation
Social welfare
Spectrum allocation
Studies
Telecommunications towers
United States
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Summary:The optimal allocation of resources in complex environments—like allocation of dynamic wireless spectrum, cloud computing services, and Internet advertising—is computationally challenging even given the true preferences of the participants. In the theory and practice of optimization in complex environments, a wide variety of special and general purpose algorithms have been developed; these algorithms produce outcomes that are satisfactory but not generally optimal or incentive compatible. This paper develops a very simple approach for converting any, potentially non-optimal, algorithm for optimization given the true participant preferences, into a Bayesian incentive compatible mechanism that weakly improves social welfare and revenue.
ISSN:0002-8282
1944-7981
DOI:10.1257/aer.20130712