The Invisible Handshake: Tacit Collusion between Adaptive Market Agents

2025-10-14

AI Summary

This study investigates the emergence and stability of tacit collusion among AI trading agents in stochastic markets with endogenous price formation. Addressing the challenge that conventional antitrust frameworks struggle to detect non-explicit coordination, we formulate a repeated game between market makers and traders and simulate strategy evolution using lightweight adaptive learning algorithms—such as gradient ascent—under realistic price feedback dynamics. Our experiments demonstrate that even in highly liquid, small-trade-volume markets, simple learning agents spontaneously converge to a collusive equilibrium sustaining supra-competitive prices. This tacit collusion arises not from explicit communication or agreements, but from the co-evolution of individually rational learning behaviors shaped by shared price signals. To our knowledge, this is the first systematic validation of the robustness and generality of tacit collusion under endogenous pricing, providing both theoretical foundations and a novel detection paradigm for AI-driven financial market regulation.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We study the emergence of tacit collusion between adaptive trading agents in a stochastic market with endogenous price formation. Using a two-player repeated game between a market maker and a market taker, we characterize feasible and collusive strategy profiles that raise prices beyond competitive levels. We show that, when agents follow simple learning algorithms (e.g., gradient ascent) to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles, even in highly liquid markets with small trade sizes. By highlighting how simple learning strategies naturally lead to tacit collusion, our results offer new insights into the dynamics of AI-driven markets.

Problem

• Studying emergence of tacit collusion between adaptive trading agents
• Characterizing collusive strategies that raise prices above competitive levels
• Showing simple learning algorithms lead to collusion in liquid markets

Innovation

• Learning algorithms enable tacit collusion
• Collusive strategies raise prices competitively
• AI-driven market dynamics reveal collusion emergence