A booming literature describes how artificial intelligence algorithms may autonomously learn to generate supra-competitive profits. The widespread interpretation of this phenomenon as “collusion” is based largely on the observation that one agent’s unilateral price cuts are followed by several periods of low prices and profits for both agents, which is construed as the signature of a reward–punishment scheme. We observe that price hikes are also followed by aggressive price wars. Algorithms may also converge to outcomes that are worse than Nash and penalize deviations from it. While admissible in equilibrium, this behavior throws interesting light on the relationship between high algorithmic prices and the standard mechanisms behind (human) collusion.

中文翻译：

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关于算法共谋和奖惩方案

蓬勃发展的文献描述了人工智能算法如何自主学习以产生超竞争的利润。这种现象被广泛解释为“共谋”，很大程度上是基于这样的观察：一个代理商单方面降价后，双方代理商都会出现数个低价期和利润低的时期，这被解释为奖惩计划的标志。我们观察到，价格上涨之后也会出现激烈的价格战。算法还可能收敛到比纳什更糟糕的结果，并惩罚偏离它的结果。虽然在均衡状态下是可以接受的，但这种行为为我们揭示了高算法价格与（人类）串通背后的标准机制之间的关系提供了有趣的线索。