Optimal Auction Design under Costly Learning
Kemal Ozbek
TL;DR
The paper investigates auction design when bidders can costly-learn their own valuations in an independent private values setting. It demonstrates that a two-stage mechanism, with VCG at stage-2 and a verifiable stage-1 contract/fee structure, aligns revenue and welfare by making the pre-auction information game a Bayesian exact potential game, ensuring welfare-maximizing learning equilibria. The authors show that the optimal mechanism extracts information rents through a chain-closure fee while keeping stage-2 efficiency, and that verification or auditing can affect the feasible fee structure and revenue. This yields a practical benchmark unifying efficient allocation with revenue optimization in environments where information acquisition is endogenous and costly.
Abstract
We study optimal auction design in an independent private values environment where bidders can endogenously -- but at a cost -- improve information about their own valuations. The optimal mechanism is two-stage: at stage-1 bidders register an information acquisition plan and pay a transfer; at stage-2 they bid, and allocation and payments are determined. We show that the revenue-optimal stage-2 rule is the Vickrey--Clarke--Groves (VCG) mechanism, while stage-1 transfers implement the optimal screening of types and absorb information rents consistent with incentive compatibility and participation. By committing to VCG ex post, the pre-auction information game becomes a potential game, so equilibrium information choices maximize expected welfare; the stage-1 fee schedule then transfers an optimal amount of payoff without conditioning on unverifiable cost scales. The design is robust to asymmetric primitives and accommodates a wide range of information technologies, providing a simple implementation that unifies efficiency and optimal revenue in environments with endogenous information acquisition.