- Title
- Informationally Robust Optimal Auction Design
- Author(s)
- Dirk Bergemann Dirk Bergemann (Yale University)
- Benjamin Brooks Benjamin Brooks (University of Chicago)
- Stephen Morris Stephen Morris (Princeton University)
- Abstract
- A single unit of a good is to be sold by auction to one of two buyers. The good has either a high value or a low value, with known prior probabilities. The designer of the auction knows the prior over values but is uncertain about the correct model of the buyers' beliefs. The designer evaluates a given auction design by the lowest expected revenue that would be generated across all models of buyers' information that are consistent with the common prior and across all Bayesian equilibria. An optimal auction for such a seller is constructed, as is a worst-case model of buyers' information. The theory generates upper bounds on the seller's optimal payoff for general many-player and common-value models.
- Creation Date
- 2016-12
- Section URL ID
- Paper Number
- 084_2016
- URL
- http://detc.princeton.edu/wp-content/uploads/2017/01/wp084_2016_Bergemann-Brooks-Morris_Informationally-Robust-Optimal-Auction-Design.pdf
- File Function
- Jel
- C720, D440, D820, D830
- Keyword(s)
- Optimal auctions, common values, information structure, mo del uncertainty, ambiguity aversion, robustness, Bayes correlated equilibrium, revenue maximization, revenue equivalence, information rent
- Suppress
- false
- Series
- 10