"These systems continuously optimize prices and match demand and supply to consider competitive factors, demand patterns and personal browsing histories of consumers. For instance, if you received a discount ticket notification from Delta airline, it could be because of Delta's algorithmic pricing enginewhere their system automatically picked up on the fact that a competitor decreased their price, so the algorithm adjusts the airline's own prices accordingly, and vice versa."
"A 2024 joint statement, the U.S. Department of Justice (DOJ), the Federal Trade Commission (FTC), the U.K. Competition and Markets Authority, and the European Commission states that the agencies should "remain "vigilant" about "the risk that algorithms can allow competitors to share competitively sensitive information, fix prices, or collude on other terms or business strategies in violation of our competition laws.""
Algorithmic dynamic pricing is widely used by major platforms to continuously optimize prices using consumer behavior, competitive factors, and demand-supply matching. These systems can adjust prices in real time in response to competitors, exemplified by airlines changing fares after rival price shifts. Reinforcement learning increasingly powers dynamic pricing by using trial-and-error to maximize cumulative rewards. Collusion occurs when firms coordinate through reward-punishment schemes to achieve supra-competitive outcomes. Regulators including the DOJ, FTC, CMA, and European Commission warn that algorithms can enable the sharing of sensitive information, price fixing, or other coordinated strategies that violate competition laws.
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