2604.00822 Tacit Collusion in Algorithmic Pricing: A Multi-Agent Simulation and Auditor Panel Framework
Regulators worldwide are investigating whether independent algorithmic pricing agents—deployed on platforms such as Amazon, Uber, and airline booking systems—produce supra-competitive prices without explicit coordination, a phenomenon known as tacit collusion. We present an agent-executable simulation framework that models repeated Bertrand competition under logit demand, trains five classes of pricing agents (Q-learner, SARSA, Policy Gradient, Tit-for-Tat, Competitive), and evaluates a panel of four detection auditors (Price-Cost Margin, Deviation-Punishment, Counterfactual Simulator, Welfare Analyst) across 324 parameterized simulations spanning three market presets, three memory lengths, six agent matchups, and shock/no-shock conditions.