IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2510.27636.html

Delegate Pricing Decisions to an Algorithm? Experimental Evidence

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2510.27636
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    3. Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
    4. Pedro Dal Bó & Guillaume R. Fréchette, 2018. "On the Determinants of Cooperation in Infinitely Repeated Games: A Survey," Journal of Economic Literature, American Economic Association, vol. 56(1), pages 60-114, March.
    5. Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform Design When Sellers Use Pricing Algorithms," Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
    6. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2024. "Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning," Papers 2406.02437, arXiv.org.
    7. Giacomo Calzolari & Philip Hanspach, 2025. "Pricing Algorithms Out of the Box: A Study of the Repricing Industry," Journal of Competition Law and Economics, Oxford University Press, vol. 21(2), pages 163-187.
    8. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    9. Wolfram Barfuss & Janusz Meylahn, 2022. "Intrinsic fluctuations of reinforcement learning promote cooperation," Papers 2209.01013, arXiv.org, revised Feb 2023.
    10. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    11. Simon Martin & Hans-Theo Normann & Paul Püplichhuisen & Tobias Werner, 2025. "The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms," CESifo Working Paper Series 11629, CESifo.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Normann, Hans-Theo & Rulié, Nina & Stypa, Olaf & Werner, Tobias, 2025. "Delegate Pricing Decisions to an Algorithm? Experimental Evidence," VfS Annual Conference 2025 (Cologne): Revival of Industrial Policy 325417, Verein für Socialpolitik / German Economic Association, revised 2025.
    2. Gorny, Paul M. & Schäfer, Louis, 2025. "Adaptivity and Revealed Robot Aversion in Human-Robot Collaboration: A Field-in-the-Lab Experiment," MPRA Paper 126663, University Library of Munich, Germany.
    3. John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.
    4. Sara Fish & Yannai A. Gonczarowski & Ran I. Shorrer, 2024. "Algorithmic Collusion by Large Language Models," Papers 2404.00806, arXiv.org, revised Sep 2025.
    5. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).
    6. Brice Corgnet, 2023. "An Experimental Test of Algorithmic Dismissals," Working Papers 23-02, Chapman University, Economic Science Institute.
    7. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    8. Ghidoni, Riccardo & Suetens, Sigrid, 2019. "Empirical Evidence on Repeated Sequential Games," Other publications TiSEM ff3a441f-e196-4e45-ba59-c, Tilburg University, School of Economics and Management.
    9. Send, Jonas & Serena, Marco, 2022. "An empirical analysis of insistent bargaining," Journal of Economic Psychology, Elsevier, vol. 90(C).
    10. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    11. Lorenzo Cominelli & Gianluca Rho & Caterina Giannetti & Federico Cozzi & Alberto Greco & Graziano A. Manduzio & Philipp Chapkovski & Michalis Drouvelis & Enzo Pasquale Scilingo, 2024. "Emotions in hybrid financial markets," Discussion Papers 2024/311, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    12. Masaki Aoyagi & Guillaume Frechette & Sevgi Yuksel, 2021. "Beliefs in Repeated Games," ISER Discussion Paper 1119r, Institute of Social and Economic Research, The University of Osaka.
    13. Dimitris Bertsimas & Agni Orfanoudaki, 2021. "Algorithmic Insurance," Papers 2106.00839, arXiv.org, revised Dec 2022.
    14. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    15. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    16. Bryce McLaughlin & Jann Spiess, 2022. "Algorithmic Assistance with Recommendation-Dependent Preferences," Papers 2208.07626, arXiv.org, revised Oct 2025.
    17. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    18. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    19. Jan René Judek, 2024. "Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion," FinTech, MDPI, vol. 3(1), pages 1-11, January.
    20. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2510.27636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.