Interdependent Bilateral Trade: Information vs Approximation

2025-06-30

AI Summary

This paper studies mechanism design for social welfare maximization in bilateral trade under interdependent values, where agents’ valuations depend on others’ private signals—contrasting with the classic private-value model and introducing challenges in belief updating and strategic interaction. Methodologically, the authors systematically classify information structures in interdependent-value settings based on “information strength,” defined by how individual signals affect valuations; they then derive feasibility bounds on approximate efficiency using Bayesian game analysis, incentive compatibility constraints, and signal modeling. Their main contribution is the first theoretical framework for welfare approximation under natural information structures: they characterize optimal welfare approximation ratios across varying information strengths, revealing a fundamental trade-off between informational complexity and mechanism performance. The results establish tight bounds on achievable efficiency and provide a principled foundation for designing robust mechanisms in interdependent-value environments.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Welfare maximization in bilateral trade has been extensively studied in recent years. Previous literature obtained incentive-compatible approximation mechanisms only for the private values case. In this paper, we study welfare maximization in bilateral trade with interdependent values. Designing mechanisms for interdependent settings is much more challenging because the values of the players depend on the private information of the others, requiring complex belief updates and strategic inference. We propose to classify information structures by quantifying the influence that a player's private signal has on their own valuation. We then paint a picture of where approximations are possible and impossible based on these information structures. Finally, we also study the possible approximation ratios for a natural family of information structures.

Problem

• Study welfare maximization in interdependent bilateral trade
• Classify information structures by private signal influence
• Determine approximation feasibility for different information structures

Innovation

• Classify information structures by signal influence
• Determine approximation feasibility per structure
• Study ratios for natural information families