The digital revolution has led to a significant growth in companies’ ability to capture, store and analyse data about their customers, competitors and the wider world, through faster processors, cloud storage, and advances in machine learning. Increasingly, companies are using this information to develop algorithms that set prices for them.
This discussion paper examines how the automation of pricing through algorithms can affect competitive outcomes in markets, and result in different consumers being charged different amounts for the same good or service.
There has been extensive recent press coverage of the risk that price-setting algorithms, using artificial intelligence (AI), have the potential to collude among themselves, to the detriment of consumers. Academics, Ariel Ezrachi and Maurice Stucke, were among the first to point out this risk, and their work has influenced several recent speeches and comments by representatives of competition authorities, including the European Commissioner for Competition, Margrethe Vestager and the OECD.
At the same time, others suggest that the use of algorithms can be efficient and pro-competitive, leading to outcomes that benefit consumers through faster adjustments to prevailing market circumstances.
This discussion paper explores these two contrasting positions. While the impact of algorithms that use simple rules or formulae to set prices can be assessed in a relatively straightforward way, it is more difficult to judge the more advanced algorithms. These increasingly use AI to adapt and learn as they experience new situations. The way in which AI-driven algorithms learn is highly complex, and, typically, you can’t ask them why they did something. It is not possible for an outsider to ‘reverse engineer’ the algorithm.