Learning Truthful Mechanisms without Discretization

2025-06-28

AI Summary

This paper addresses the scalability and efficiency degradation in automated mechanism design caused by discretization of outcome spaces. To this end, we propose TEDI—the first end-to-end framework that learns truthful (incentive-compatible) and utility-maximizing mechanisms *without* outcome-space discretization. TEDI models mechanisms as menus and employs a Partial GroupMax network to approximate partially convex pricing rules, ensuring expressive completeness, dimension-agnosticism, and theoretical truthfulness guarantees. Unbiased differentiable optimization is achieved via covariance subtraction and continuous sampling. Evaluated across diverse auction domains, TEDI matches or surpasses state-of-the-art methods in mechanism performance while maintaining stable runtime efficiency as problem scale increases—significantly enhancing the feasibility and practicality of learning large-scale truthful mechanisms.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This paper introduces TEDI (Truthful, Expressive, and Dimension-Insensitive approach), a discretization-free algorithm to learn truthful and utility-maximizing mechanisms. Existing learning-based approaches often rely on discretization of outcome spaces to ensure truthfulness, which leads to inefficiency with increasing problem size. To address this limitation, we formalize the concept of pricing rules, defined as functions that map outcomes to prices. Based on this concept, we propose a novel menu mechanism, which can be equivalent to a truthful direct mechanism under specific conditions. The core idea of TEDI lies in its parameterization of pricing rules using Partial GroupMax Network, a new network architecture designed to universally approximate partial convex functions. To learn optimal pricing rules, we develop novel training techniques, including covariance trick and continuous sampling, to derive unbiased gradient estimators compatible with first-order optimization. Theoretical analysis establishes that TEDI guarantees truthfulness, full expressiveness, and dimension-insensitivity. Experimental evaluation in the studied auction setting demonstrates that TEDI achieves strong performance, competitive with or exceeding state-of-the-art methods. This work presents the first approaches to learn truthful mechanisms without outcome discretization, thereby enhancing algorithmic efficiency. The proposed concepts, network architecture, and learning techniques might offer potential value and provide new insights for automated mechanism design and differentiable economics.

Problem

• Learn truthful mechanisms without discretization for efficiency
• Propose menu mechanism equivalent to truthful direct mechanism
• Develop novel training techniques for unbiased gradient estimation

Innovation

• TEDI algorithm learns truthful mechanisms without discretization
• Uses Partial GroupMax Network for convex function approximation
• Employs covariance trick and continuous sampling techniques