Noncooperative Equilibrium Selection via a Trading-based Auction

2025-02-05

AI Summary

Non-cooperative multi-agent systems often suffer from multiple Nash equilibria due to conflicting agent preferences, leading to suboptimal outcomes or safety risks; conventional coordination approaches typically require explicit inter-agent communication or disclosure of private valuations. To address this, we propose TACo—the first decentralized equilibrium selection framework grounded in transactional auctions—requiring neither explicit communication nor revelation of private valuations. TACo integrates a distributed auction mechanism with iterative strategy updates, providing provably finite-step convergence. Theoretical analysis guarantees global cost optimality and fair resource allocation. Numerical experiments demonstrate strict termination, the lowest median total cost among compared methods, and significantly improved fairness over existing baselines.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

Abstract

Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, agents acting in their own self-interest can settle on different equilibria, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across all agents, while allocating resources significantly more fairly than baseline approaches.

Problem

• Resolves coordination in noncooperative multi-agent systems
• Enables consensus without direct communication
• Minimizes total cost and improves fairness

Innovation

• Decentralized trading auction algorithm
• Iterative choice selection process
• A priori bounded agreement steps