Learning to Mitigate AI Collusion on Economic Platforms
This addresses the issue of AI-driven collusion for consumers and platform regulators, presenting an incremental approach by applying existing RL methods to a new application in economic platform design.
The paper tackles the problem of algorithmic pricing leading to tacit collusion on e-commerce platforms by using reinforcement learning to design buy box rules that prevent collusion, demonstrating success in maintaining high consumer welfare across various seller models and cost distributions.
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.