Artificial Intelligence, Algorithmic Competition and Market Structures

The use of artificial intelligence (AI) in the form of pricing algorithms to increase profits is becoming ubiquitous. However, the literature has focused on specific markets and algorithms so far, but it is unclear what happens across algorithms and markets. To analyze the business and economic impact of pricing algorithms, we build a computational model that considers two sophisticated AI algorithms (Q-learning and Particle Swarm Optimization) competing in prices in three different market structures (Logit, Hotelling, and linear demand models). From a social perspective, we find that PSO outperforms Q-learning, which tends to set supracompetitive prices. However, small changes in the algorithm designs may drive them to set more competitive prices, implying that a proper analysis of algorithmic competition requires considering the details of the algorithms and the market structure. When firms compete on algorithms, algorithms may generate price dispersion. Additionally, when facing a traditional competitor that uses a best-response function, algorithms tend to set supracompetitive prices, and both firms earn extra profits, but the traditional competitor benefits the most. Overall, the article contributes to understanding algorithmic competition, discusses implications for managers and policymakers, and identifies opportunities for future research.