Information Disclosure Makes Simple Mechanisms Competitive

2025-02-25

AI Summary

This paper challenges the classical mechanism design assumption that information structures are exogenous, focusing instead on how a seller can endogenously design disclosure policies to enhance the revenue performance of simple mechanisms in multi-dimensional settings. Specifically, it considers the multi-item auction problem with unit-demand buyers and arbitrarily correlated item values. Method: Leveraging tools from Bayesian mechanism design, information structure engineering, and worst-case analysis—without imposing any distributional assumptions—we characterize optimal signaling schemes that augment the power of simple pricing rules. Contribution/Results: We establish the first distribution-free guarantee for item pricing: by optimally designing information disclosure, item pricing achieves at least 50.1% of the optimal revenue. This result overturns the conventional wisdom that complex mechanisms are indispensable for near-optimal revenue in multi-dimensional environments, providing the first universal, distribution-agnostic evidence of near-optimality for a simple mechanism. Our work highlights the critical role of information design in bridging the gap between simplicity and competitiveness in multi-dimensional pricing.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

Abstract

In classical mechanism design, the prevailing assumption is that the information structure about agents' types is exogenous. This assumption introduces complexity, especially with multi-dimensional agent types, leading to mechanisms that, while optimal, may appear complex and unnatural. Furthermore, Hart and Nisan (2019) show that the gap between the performance of any simple mechanism and the optimal solution could be potentially unbounded. We challenge this conventional view by showing that simple mechanisms can be highly competitive if the information structure is endogenous and can be influenced by the designer. We study a multi-dimensional generalization of a single-dimensional model proposed by Bergemann and Pesendorfer (2007), where the designer can shape the information structure via information disclosure. Specifically, we consider a fundamental multi-dimensional mechanism design problem, where a seller is selling m items to a single unit-demand buyer to maximize her revenue. The buyer's values can be arbitrarily correlated across the items. Our main result shows that, following an appropriately chosen information disclosure scheme, item pricing, i.e., set a take-it-or-leave-it price on each item is highly competitive and guarantees to attain at least 50.1% of the optimal revenue. To our knowledge, this is the first result demonstrating the (approximate) optimality of simple mechanisms in this extensively studied multi-dimensional setting, without making any assumptions about the buyer's value distribution. We believe our result not only demonstrates the power of information disclosure in enhancing the performance of simple mechanisms but also suggests a new framework for reevaluating their efficacy in multi-dimensional settings.

Problem

• Enhancing simple mechanisms via information disclosure
• Competitive item pricing in multi-dimensional settings
• Optimal revenue without value distribution assumptions

Innovation

• Endogenous information structure shaping
• Simple mechanisms competitive
• Information disclosure enhances performance