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

TL;DR

This paper characterizes the optimal mechanisms for a monopolist selling multiple goods when buyers learn about their types through signals, comparing buyer-optimal and robust informational scenarios, both resulting in pure bundling solutions.

Contribution

It derives the seller's optimal mechanisms under two different information environments, revealing that both cases favor pure bundling strategies.

Findings

Optimal mechanisms are pure bundling in both scenarios

Buyer-optimal outcome maximizes consumer surplus

Robust mechanism minimizes seller profits under worst-case signals

Abstract

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller's optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer's objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller's profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.