论文标题
学会减轻经济平台上的AI勾结
Learning to Mitigate AI Collusion on Economic Platforms
论文作者
论文摘要
在线电子商务平台上的算法定价引起了对Tacit合谋的关注，在这种情况下，强化学习算法学会以分散的方式设定合格价格，而无非是利润反馈。这就提出了一个问题，即是否可以通过设计合适的“购买盒子”来防止合格定价，即通过设计管理电子商务网站要素的规则，这些元素将特定产品和价格推向消费者。在本文中，我们证明了平台也可以使用增强学习（RL）来学习有效防止RL卖家勾结的盒子规则。为此，我们采用了Stackelberg Pomdps的方法，并在学习强大的规则方面取得了成功，这些规则继续提供高消费者的福利，以及采用不同行为模型或对商品的分发费用的卖家。
Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized manner and through nothing more than profit feedback. This raises the question as to whether collusive pricing can be prevented through the design of suitable "buy boxes," i.e., through the design of the rules that govern the elements of e-commerce sites that promote particular products and prices to consumers. In this paper, we demonstrate that reinforcement learning (RL) can also be used by platforms to learn buy box rules that are effective in preventing collusion by RL sellers. For this, we adopt the methodology of Stackelberg POMDPs, and demonstrate success in learning robust rules that continue to provide high consumer welfare together with sellers employing different behavior models or having out-of-distribution costs for goods.
