Truthful Double Auctions under Approximate VCG: Immediate-Penalty Enforcement in P2P Energy Trading

2025-11-29

AI Summary

In peer-to-peer (P2P) energy trading, the exact Vickrey–Clarke–Groves (VCG) mechanism is computationally intractable, and enforcing punishment in repeated games remains challenging. Method: We propose an α-approximate VCG double auction mechanism with immediate punishment, integrating an α-approximate VCG allocation rule with proximal policy optimization (PPO)-based multi-agent reinforcement learning to model strategic bidding by prosumers; explicit penalty terms penalize deviations from true valuations. Contribution/Results: We theoretically prove that, under bounded monitoring accuracy, truthful bidding constitutes a subgame-perfect equilibrium when the immediate punishment intensity exceeds the incentive distortion induced by approximation error. Empirical results demonstrate stable convergence to truthful bidding across varying approximation factors (α), tolerance thresholds, penalty coefficients, and discount factors—aligning closely with theoretical predictions. The framework provides a lightweight, verifiable, and deployable incentive-compatible solution for distributed energy markets.

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
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

This paper examines truthful double auctions when exact VCG allocation is computationally infeasible and repeated-game punishments are impractical. We analyze an $α$-approximate VCG mechanism and show that truthful reporting becomes a subgame-perfect equilibrium when the immediate penalty exceeds the incentive gap created by approximation, scaled by monitoring accuracy. To validate this result, we construct a PPO-based multi-agent reinforcement learning environment for P2P smart-grid trading, where prosumers incur penalties for bidding far from their true valuations. Across systematic experiments varying approximation accuracy, tolerance, penalty magnitude, and discounting, the learned behavior closely matches theoretical predictions. The findings demonstrate that immediate-penalty approximate VCG mechanisms provide a practical and transparent approach to sustaining truthful behavior in distributed market settings.

Problem

• Ensures truthful bidding in computationally infeasible VCG double auctions.
• Uses immediate penalties to enforce honesty in P2P energy trading markets.
• Validates theoretical predictions with multi-agent reinforcement learning experiments.

Innovation

• Immediate penalty enforces truthful bidding
• Approximate VCG mechanism with penalty scaling
• PPO-based multi-agent reinforcement learning validation