Auction design with data-driven misspecifications: Inefficiency in private value auctions with correlation

We study the existence of efficient auctions in private value settings in which some bidders form their expectations about the distribution of their competitor's bids based on the accessible data from past similar auctions consisting of bids and ex post values. We consider steady states in such environments with a mix of rational and data-driven bidders, and we allow for correlation across bidders in the signal distributions about the ex post values. After reviewing the working of the approach in second-price and first-price auctions, we establish our main result that there is no efficient auction in such environments.