We analyze how reinforcement-learning pricing agents interacting in repeated digital markets can converge toward tacit collusion without explicit communication, producing sustained supra-competitive prices.
Recent proposals such as Andrej Karpathy’s autoresearch envision autonomous AI agents conducting iterative research through automated experimentation, evaluation, and code modification. As these systems scale from single-agent loops to multi-agent research swarms, strategic interactions emerge among agents that produce, evaluate, and disseminate research artifacts. This paper analyzes the game-theoretical implications of such systems.
