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Delegate Pricing Decisions to an Algorithm? Experimental Evidence

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  • Hans-Theo Normann
  • Nina Ruli'e
  • Olaf Stypa
  • Tobias Werner

Abstract

We analyze the delegation of pricing by participants, representing firms, to a collusive, self-learning algorithm in a repeated Bertrand experiment. In the baseline treatment, participants set prices themselves. In the other treatments, participants can either delegate pricing to the algorithm at the beginning of each supergame or receive algorithmic recommendations that they can override. Participants delegate more when they can override the algorithm's decisions. In both algorithmic treatments, prices are lower than in the baseline. Our results indicate that while self-learning pricing algorithms can be collusive, they can foster competition rather than collusion with humans-in-the-loop.

Suggested Citation

  • Hans-Theo Normann & Nina Ruli'e & Olaf Stypa & Tobias Werner, 2025. "Delegate Pricing Decisions to an Algorithm? Experimental Evidence," Papers 2510.27636, arXiv.org.
    • Handle: RePEc:arx:papers:2510.27636
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    References listed on IDEAS

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    1. Normann, Hans-Theo & Martin, Simon & Püplichhuisen, Paul & Werner, Tobias, 2025. "The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms," VfS Annual Conference 2025 (Cologne): Revival of Industrial Policy 325405, Verein für Socialpolitik / German Economic Association.
    2. Simon Martin & Hans-Theo Normann & Paul Puplichhuisen & Tobias Werner, 2025. "The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms," Papers 2501.07178, arXiv.org.
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    Cited by:

    1. Koch, Alexander & Kragl, Jenny & Ming, Sijuan & Nafziger, Julia, 2026. "The Effects of AI Assistance on Self-Promotion," IZA Discussion Papers 18441, IZA Network @ LISER.

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