2010/05/07 by Vineet Abhishek, Abhishek, Vineet, Bruce Hajek +1
Business, Management and Accounting · Computer Science · Decision Sciences · Social Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #cs.GT
paper · pdf · doi:10.48550/arxiv.1005.1121
25 pages and 1 figure. A short version of this will appear in the 49th IEEE Conference on Decision and Control (CDC), 2010
openalex publication_date 2010/05/07 · arxiv created 2010/09/13 · arxiv updated 2010/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study efficiency loss in Bayesian revenue optimal auctions. We quantify this as the worst case ratio of loss in the realized social welfare to the social welfare that can be realized by an efficient auction. Our focus is on auctions with single-parameter buyers and where buyers' valuation sets are finite. For binary valued single-parameter buyers with independent (not necessarily identically distributed) private valuations, we show that the worst case efficiency loss ratio (ELR) is no worse than it is with only one buyer; moreover, it is at most 1/2. Moving beyond the case of binary valuations but restricting to single item auctions, where buyers' private valuations are independent and identically distributed, we obtain bounds on the worst case ELR as a function of number of buyers, cardinality of buyers' valuation set, and ratio of maximum to minimum possible values that buyers can have for the item.