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The use of repricing software has become ubiquitous in recent years. One example is the case of gasoline markets in which the widespread adoption of repricing software by large companies has attracted the attention of both academia and the media (Assad et al, 2020; The Economist, 2017). The use of repricing software is not limited to large firms, however. The emergence of peer-to-peer marketplaces such as Amazon, eBay, and Airbnb has given rise to a rich landscape of software companies that offer affordable off-the-shelf repricing solutions for small sellers.

In the context of online platforms, sheer speed is often advertised as the main benefit of using repricing software: Automatically monitoring and reacting to rival prices allows to relentlessly undercut slower sellers, who set prices manually. This, in turn, allows sellers to capture consumer demand because online platforms prominently feature the seller with the lowest price. While fierce price competition is likely to benefit customers through lower prices, the possibility that repricers might be powered by intelligent algorithms has also raised concerns that they might autonomously learn sophisticated strategies to charge higher prices, ie, that they might autonomously learn to collude.