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Algorithmic Collusion Without Deterrence: Auditing the Punishment Mechanism in Q-Learning Pricing Agents
Independent tabular Q-learning agents repricing in a simulated Bertrand duopoly converge to supracompetitive prices, and the seminal evidence attributes this to a learned reward-punishment scheme: undercut your rival and it retaliates, so deviation never pays. This mechanistic reading is what turns
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