Mitigating Retaliatory Algorithmic Collusion in Repeated Games
Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without communication or shared design. Existing mitigation approaches are largely tied to specific economic settings, like two-sided platforms and auctio...
K. Sivachandran, R. Paleja
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