2009/09/30 by Jason D. Hartline, Brendan Lucier · 26 citations
Business, Management and Accounting · Computer Science · Decision Sciences · Engineering · Mathematics · #Artificial intelligence #Auction Theory and Applications #Computer science #Consumer Market Behavior and Pricing #Distributed computing #Economics #Engineering #Game Theory and Applications #Incentive #Incentive compatibility #Mathematical optimization #Mathematics #Mechanism (biology) #Mechanism design #Microeconomics #Operations research #Optimal allocation #Revenue #Simple (philosophy) #Social Welfare #The Internet #Variety (cybernetics) #cs.GT
paper · pdf · doi:10.1257/aer.20130712
published in American Economic Review 105(10), 3102-3124 (American Economic Association)
arxiv created 2011/02/23 · openalex publication_date 2015/10/01 · openalex created_date 2016/06/24 · arxiv updated 2017/08/21 · openalex updated_date 2026/08/06
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. (JEL D82, H82, L82)