RT Journal Article
T1 AI pricing algorithms under platform competition
A1 Sánchez Cartas, Juan Manuel
A1 Katsamakas, Evangelos
AB Platforms play an essential role in the modern economy. At the same time, due to advances in artificial intelligence (AI), algorithms are becoming more widely used for pricing and other business functions. Previous literature examined algorithmic pricing, but not in the context of network effects and platforms. Moreover, platform competition literature has not considered how algorithms may affect competition. We study the performance of AI pricing algorithms (Q-learning and Particle Swarm Optimization) and naïve algorithms (price-matching) under platform competition. We find that algorithms set an optimal price structure that internalizes network effects. However, no algorithm is always the best because profitability depends on the type of competing algorithms and market characteristics, such as differentiation and network effects. Additionally, algorithms learn autonomously when an equilibrium is unstable and avoid it. When algorithm adoption is an endogenous strategic decision, several algorithms can be adopted in equilibrium; we characterize the conditions for the various outcomes and show that the equilibrium and platform profits are sensitive to algorithm design changes. Overall, our research suggests that AI algorithms can be effective in the presence of network effects, and platforms are likely to adopt a variety of algorithms. Lastly, we reflect on the business value of AI and identify opportunities for future research at the intersection of AI algorithms and platforms.
PB Springer
SN 1389-5753
YR 2024
FD 2024
LK https://hdl.handle.net/20.500.14352/101835
UL https://hdl.handle.net/20.500.14352/101835
LA eng
NO Sanchez-Cartas, J.M., Katsamakas, E. AI pricing algorithms under platform competition. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09821-w
NO Conferencia de Rectores de las Universidades Españolas
NO Consejo Superior de Investigaciones Científicas 
DS Docta Complutense
RD 7 abr 2025