Pricing Algorithms and Collusion Practical Law The Journal
Algorithmic pricing systems differ from traditional, manual price-setting practices as they can almost immediately:
Assimilate and process significant amounts of information relating to competitor prices, demand, the price and availability of substitutes, and even customer personal data.
Respond to changes in the market or competitor pricing.
Set prices to achieve a business objective consistently across all sales.
This increased capacity to process mass amounts of information and data to execute price changes can allow companies to compete more effectively by responding to changes in the market quickly. However, the use of AI in pricing systems has raised concerns, particularly in relation to antitrust and competition laws, including because:
AI systems could facilitate or discretely give effect to price-fixing arrangements.
AI systems could make detection of price-fixing arrangements harder.
The proliferation of AI systems could result in supracompetitive prices for products.
Background on Algorithmic Pricing Systems
Although there is no firm definition of an intelligent machine, the term AI is widely used to refer to software systems that can replicate some functions typically associated with human thought processes, like learning and making decisions or predictions about future behavior. AI systems are distinct from systems that only perform repetitive tasks involving data processing that are difficult or time consuming for humans to perform.
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