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<p>This study conducts a series of simulated experiments in which airlines use deep Q-learning (DQL) algorithms to dynamically control the class of quantity and price pairs offered to the market through the booking horizon to better understand the algorithmic collusion problem. We show that, in a monopoly market, DQL algorithm can learn stochastic demand without any prior knowledge and achieve optimal monopoly revenue. In a duopoly market, with limited information requirements, DQL algorithms can contribute to the same knowledge pool, coordinate airlines’ behaviors, learn to collude and share the monopoly profit equally. Compared with the expected marginal seat revenue (EMSR)-b heuristics, DQL algorithms are more adaptive for learning new demand stochasticity and are more likely to sustain collusive outcomes.</p>

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Can dynamic pricing algorithm facilitate tacit collusion? An experimental study using deep reinforcement learning in airline revenue management

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Chengyan Gu

摘要

This study conducts a series of simulated experiments in which airlines use deep Q-learning (DQL) algorithms to dynamically control the class of quantity and price pairs offered to the market through the booking horizon to better understand the algorithmic collusion problem. We show that, in a monopoly market, DQL algorithm can learn stochastic demand without any prior knowledge and achieve optimal monopoly revenue. In a duopoly market, with limited information requirements, DQL algorithms can contribute to the same knowledge pool, coordinate airlines’ behaviors, learn to collude and share the monopoly profit equally. Compared with the expected marginal seat revenue (EMSR)-b heuristics, DQL algorithms are more adaptive for learning new demand stochasticity and are more likely to sustain collusive outcomes.