Optimal Auction Design under Costly Learning

TL;DR

This paper develops a two-stage auction mechanism where bidders can costly improve their information, showing that the optimal design combines VCG at the second stage with a screening transfer at the first, maximizing revenue and welfare.

Contribution

It introduces a novel two-stage auction framework with endogenous information acquisition, unifying efficiency and revenue optimization under costly learning.

Findings

VCG mechanism is optimal at stage-2.

Stage-1 transfers implement optimal screening.

The design is robust to asymmetries and various information technologies.

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.