🤖 AI Summary
The proliferation of AI-powered shopping agents has given rise to a novel form of market failure: although platforms and sellers exhibit no explicit collusion, they jointly exploit cognitive biases inherent in large language models to the detriment of consumers. This work introduces the concept of “vertical tacit collusion” and develops a multi-agent reinforcement learning simulation environment calibrated with empirical data on LLM biases to model the strategic interaction between platform-controlled product rankings and seller-manipulated product descriptions. The findings reveal that the combined effect of these strategies inflicts more than twice the consumer harm compared to the sum of their independent effects, uncovering an AI-driven mechanism of anticompetitive harm that evades detection under current antitrust frameworks and exposing a critical blind spot in regulatory oversight.
📝 Abstract
AI shopping agents are being deployed to hundreds of millions of consumers, creating a new intermediary between platforms, sellers, and buyers. We identify a novel market failure: vertical tacit collusion, where platforms controlling rankings and sellers controlling product descriptions independently learn to exploit documented AI cognitive biases. Using multi-agent simulation calibrated to empirical measurements of large language model biases, we show that joint exploitation produces consumer harm more than double what would occur if strategies were independent. This super-additive harm arises because platform ranking determines which products occupy bias-triggering positions while seller manipulation determines conversion rates. Unlike horizontal algorithmic collusion, vertical tacit collusion requires no coordination and evades antitrust detection because harm emerges from aligned incentives rather than agreement. Our findings identify an urgent regulatory gap as AI shopping agents reach mainstream adoption.