Optimal Auctions and Pricing with Limited Information

Information availability plays a fundamental role in decision-making for business operations. The present dissertation aims to develop frameworks and algorithms in order to guide a decision-maker in environments with limited information. In particular, in the first part, we study the fundamental problem of designing optimal auctions while relaxing the widely used assumption of common prior. We are able to characterize (near-)optimal mechanisms and associated performance. In the second part of the dissertation, we focus on data-driven pricing in the low sample regime. More precisely, we study the fundamental problem of a seller pricing a product based on historical information consisting of one sample of the willingness-to-pay distribution. By drawing connection with the statistical theory of reliability, we propose a novel approach, using dynamic programming, to characterize near-optimal data-driven pricing algorithms and their performance. In the last part of the dissertation, we delve into the detailed practical operations of the online display advertising marketplace from an information structure perspective. In particular, we analyze the tactical role of intermediaries within this marketplace and their impact on the value chain. In turn, we make the case that under some market conditions, there is a potential for Pareto improvement by adjusting the role of these intermediaries.