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.
ASJC Scopus subject areas
- Economics and Econometrics