Artificial Intelligence, Algorithmic Pricing, and Collusion
通过实验研究人工智能（Q学习）算法在寡头重复价格竞争中的行为，发现算法无需沟通即可学会收取超竞争价格，并通过有限惩罚后逐步恢复合作的合谋策略维持高价，且该结果在成本或需求不对称、玩家数量变化及不确定性下依然稳健。
Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by 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, changes in the number of players, and various forms of uncertainty.