Increasingly, pricing algorithms are supplanting human decision making in real marketplaces. To inform the competition policy debate on the possible consequences of this development, we experiment with pricing algorithms powered by Artificial Intelligence (AI) in controlled environments (computer simulations), studying the interaction among a number of Q-learning algorithms in a workhorse oligopoly model of price competition with Logit demand and constant marginal costs. In this setting the algorithms consistently learn to charge supra-competitive prices, without communicating with one another. The high prices are sustained by classical collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand and to changes in the number of players.

中文翻译：

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人工智能，算法定价和合谋

定价算法正逐渐取代实际市场中的人为决策。为了告知竞争政策有关此发展可能带来的结果的争论，我们在受控环境（计算机模拟）中试验了由人工智能（AI）提供支持的定价算法，研究了工作型寡头垄断模型中许多Q学习算法之间的交互作用Logit需求和恒定边际成本带来的价格竞争。在这种情况下，算法始终学习如何收取超竞争价格，而无需彼此通信。高昂的价格是通过经典的合谋策略维持的，在有限的惩罚阶段之后逐步恢复合作。这一发现对于成本或需求的不对称性以及参与者数量的变化具有鲁棒性。