The rise of algorithmic pricing in online retail platforms has attracted
significant interest in how autonomous software agents interact under
competition. This article explores the potential emergence of algorithmic
collusion – supra-competitive pricing outcomes that arise without explicit
agreements – as a consequence of repeated interactions between learning agents.
Most of the literature focuses on oligopoly pricing environments modeled as
repeated Bertrand competitions, where firms use online learning algorithms to
adapt prices over time. While experimental research has demonstrated that
specific reinforcement learning algorithms can learn to maintain prices above
competitive equilibrium levels in simulated environments, theoretical
understanding of when and why such outcomes occur remains limited. This work
highlights the interdisciplinary nature of this challenge, which connects
computer science concepts of online learning with game-theoretical literature
on equilibrium learning. We examine implications for the Business & Information
Systems Engineering (BISE) community and identify specific research
opportunities to address challenges of algorithmic competition in digital
marketplaces.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: