The Second-Knowledge Mechanism
Assuming a Bayesian is the traditional way to model uncertainty in settings of incomplete information. This assumption, however, is very strong, and may not hold in many real applications. Accordingly, we put forward (1) a set-theoretic way to model the knowledge that a player might have about his opponents, together with (2) a new class of mechanisms ---not equilibrium-based--- capable of leveraging such more conservative knowledge in a robust way.
In auctions of a single good, we show that one such mechanism can perfectly guarantee a revenue benchmark (always in between the second highest and the highest valuation) that no classical mechanism can even approximate in any robust way.
Joint Work with Jing Chen