Information Design and Mechanism Design: An Integrated Framework

2026-01-24

AI Summary

This study investigates the joint optimization of information structures and allocation mechanisms to maximize expected utility in screening environments. By constructing a unified framework, the authors reformulate the problems of information design and mechanism design as a linear functional maximization subject to majorization constraints, which is solved via a bilinear program under joint optimization. Innovatively integrating majorization theory with quantile function techniques, the paper establishes— for the first time—that pooling strategies in both value and allocation are always optimal. This work not only unifies and generalizes classical auction and screening theories but also demonstrates the significant role of jointly optimizing information and allocation in enhancing social welfare.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We develop an integrated framework for information design and mechanism design in screening environments with quasilinear utility. Using the tools of majorization theory and quantile functions, we show that both information design and mechanism design problems reduce to maximizing linear functionals subject to majorization constraints. For mechanism design, the designer chooses allocations weakly majorized by the exogenous inventory. For information design, the designer chooses information structures that are majorized by the prior distribution. When the designer can choose both the mechanism and the information structure simultaneously, then the joint optimization problem becomes bilinear with two majorization constraints. We show that pooling of values and associated allocations is always optimal in this case. Our approach unifies classic results in auction theory and screening, extends them to information design settings, and provides new insights into the welfare effects of jointly optimizing allocation and information.

Problem

• information design
• mechanism design
• screening
• majorization
• quasilinear utility

Innovation

• information design
• mechanism design
• majorization theory
• quantile functions
• pooling