Artificial Intelligence and Spontaneous Coupling
Paper joint with G. Mantegazza
Abstract:
We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism, responsible for the emergence of algorithmic collusion, that enables algorithms to periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel, spontaneous coupling, relies on an endogenous statistical linkage in the algorithms’ estimates. The model’s parameters predict whether the statistical linkage will appear, and what market structures facilitate algorithmic collusion. We show that spontaneous coupling can sustain collusion in prices and market shares, complementing numerical findings in the literature. Finally, we apply our results to design algorithmic markets.