论文标题
人工智能和自发勾结
Artificial Intelligence and Spontaneous Collusion
论文作者
论文摘要
我们开发了一个可访问的模型，用于研究学习算法之间的战略相互作用。我们发现了一种导致算法勾结出现的机制。我们观察到，算法会定期协调比静态NASH平衡更有利可图的行动。这个新颖的辅助通道依赖于算法的估计值中的内源统计联系，我们称之为自发耦合。该模型的参数预测统计联系是否将出现，哪些市场结构有助于算法勾结。我们表明，自发耦合可以维持价格和市场份额的串通，从而补充了文献中的实验结果。最后，我们将结果应用于设计算法市场。
We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism responsible for the emergence of algorithmic collusion. We observe that algorithms periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel relies on an endogenous statistical linkage in the algorithms' estimates which we call spontaneous coupling. 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 experimental findings in the literature. Finally, we apply our results to design algorithmic markets.
