Title: Information Elicitation for Bayesian Auctions,
Abstract: In this paper we design information elicitation mechanisms for Bayesian
auctions. While in Bayesian mechanism design the distributions of the players'
private types are often assumed to be common knowledge, information elicitation
considers the situation where the players know the distributions better than
the decision maker. To weaken the information assumption in Bayesian auctions,
we consider an information structure where the knowledge about the
distributions is arbitrarily scattered among the players. In such an
unstructured information setting, we design mechanisms for unit-demand auctions
and additive auctions that aggregate the players' knowledge, generating revenue
that are constant approximations to the optimal Bayesian mechanisms with a
common prior. Our mechanisms are 2-step dominant-strategy truthful and the
revenue increases gracefully with the amount of knowledge the players
collectively have.