Person:
Sánchez Cartas, Juan Manuel

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First Name
Juan Manuel
Last Name
Sánchez Cartas
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Económicas y Empresariales
Department
Análisis Económico y economía cuantitativa
Area
Fundamentos del Análisis Económico
Identifiers
UCM identifierScopus Author IDDialnet ID

Search Results

Now showing 1 - 2 of 2
  • Publication
    Platform acquisitions, product imitation and openness
    (Springer, 2023-07-21) Sánchez Cartas, Juan Manuel
    Public authorities have shown concern about the possible harmful effects of platforms acquiring or imitating complementary services sold on their platforms. The effect of these practices on the restrictions on participation, development, or use of platform services (platform openness) has started to attract the attention of policymakers and researchers alike, but the evidence is still limited. We build a model that considers the trade-off that a monopoly platform faces when deciding whether to acquire or imitate a complementor and how such a decision influences openness and welfare. We show that a platform always has an incentive to acquire or imitate complementors. Which one is preferred depends on whether the increase in platform value (acquisition) offsets the market expansion effect (imitation). We find that acquisitions reduce openness and welfare but may generate more valuable complements while imitation increases openness and welfare but may harm third-party developers.
  • Publication
    AI pricing algorithms under platform competition
    (Springer, 2024-02) Sánchez Cartas, Juan Manuel; Katsamakas, Evangelos
    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.