Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness

Rahul Deb, University of Toronto and Anne-Katrin Roesler, University of Toronto

A monopolist seller of multiple goods screens a buyer whose type vector is initially unknown to both but drawn from a commonly known prior distribution. The seller chooses a mechanism to maximize her worst-case profits against all possible signals from which the buyer can learn about his values for the goods. We show that it is robustly optimal for the seller to bundle goods with identical demands (these are goods that can be permuted without changing the buyer’s prior type distribution). Consequently, pure bundling is robustly optimal for exchangeable prior distributions. For exchangeable priors, pure bundling is also optimal for the seller in the information environment (with the reverse timing) where an information designer, with the objective of maximizing consumer surplus, first selects a signal for the buyer, and then the seller chooses an optimal mechanism in response. We derive a formal relationship between the seller’s problem in both information environments.