Beyond Human Intervention: Algorithmic Collusion through Multi-Agent Learning Strategies

2025-01-28

AI Summary

This paper addresses key challenges in algorithmic collusion—namely, exploitable agent policies, poor convergence, and insufficient real-world robustness—by proposing a multi-agent reinforcement learning framework. Methodologically, it introduces the first integration of counterfactual multi-objective optimization with explicit opponent modeling, enabling autonomous pricing agents to dynamically infer and respond to competitors’ behaviors without centralized coordination or human intervention. The primary contribution lies in departing from conventional single-objective, black-box training paradigms: the framework achieves rapid convergence to stable high-price equilibria in oligopolistic markets while exhibiting strong resilience to environmental perturbations and strategic deviations. Empirical results demonstrate significantly improved long-term profit stability and practical deployability. Collectively, this work provides both a novel theoretical instrument and empirical foundation for analyzing and regulating AI-driven tacit collusion.

Technology Category

Multiagent Systems: Mechanism DesignGame Theory and Economic Paradigms: Adversarial LearningSearch and Optimization: Learning to Search

Application Category

Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactionsResponsible Web: Human-perceived consequences of algorithmic deployment on the webSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was put forward, where the human action in the pricing process is replaced by automated agents. Although experiments have shown that collusive market equilibria can be reached through such techniques, without the need for human intervention, many of the techniques developed remain susceptible to exploitation by other players, making them difficult to implement in practice. In this article, we explore a situation where an agent has a multi-objective strategy, and not only learns to unilaterally exploit market dynamics originating from other algorithmic agents, but also learns to model the behaviour of other agents directly. Our results show how common critiques about the viability of algorithmic collusion in real-life settings can be overcome through the usage of slightly more complex algorithms.

Problem

• Artificial Intelligence
• Market Simulation
• Collaborative Strategies

Innovation

• Machine Learning
• Algorithmic Trading
• Collaborative Strategies