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

Philippe Jehiel () and Konrad Mierendorff

Theoretical Economics, 2024, vol. 19, issue 4

Abstract: 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 show our main result that there is no efficient auction in such environments.

Keywords: Belief Formation; Auctions; Efficiency; Analogy-based Expectations (search for similar items in EconPapers)
JEL-codes: D44 D82 D90 (search for similar items in EconPapers)
Date: 2024-11-14
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Related works:
Working Paper: Auction design with data-driven misspecifications: Inefficiency in private value auctions with correlation (2024)
Working Paper: Auction design with data-driven misspecifications: Inefficiency in private value auctions with correlation (2024)
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