IDEAS home Printed from https://ideas.repec.org/p/mpg/wpaper/2021_11.html
   My bibliography  Save this paper

Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets

Author

Listed:
  • Hans-Theo Normann

    (Heinrich Heine University, Düsseldorf)

  • Martin Sternberg

    (Max Planck Institute for Research on Collective Goods, Bonn)

Abstract

This paper investigates pricing in laboratory markets when human players interact with an algorithm. We compare the degree of competition when exclusively humans interact to the case of one firm delegating its decisions to an algorithm, an n-player generalization of tit-for-tat. We further vary whether participants know about the presence of the algorithm. When one of three firms in a market is an algorithm, we observe significantly higher prices compared to human-only markets. Firms employing an algorithm earn significantly less profit than their rivals. (Un)certainty about the actual presence of an algorithm does not significantly affect collusion, although humans seem to perceive algorithms as more disruptive.

Suggested Citation

  • Hans-Theo Normann & Martin Sternberg, 2021. "Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_11, Max Planck Institute for Research on Collective Goods, revised 13 Apr 2022.
    • Handle: RePEc:mpg:wpaper:2021_11
    as

    Download full text from publisher

    File URL: https://www.coll.mpg.de/pdf_dat/2021_11online.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John C. Harsanyi & Reinhard Selten, 1988. "A General Theory of Equilibrium Selection in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582384, April.
    2. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
      • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
    3. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    4. Jonathan Schulz & Uwe Sunde & Petra Thiemann & Christian Thoeni, 2019. "Selection into Experiments: Evidence from a Population of Students," Discussion Papers 2019-09, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    5. Andreas Freitag & Catherine Roux & Christian Thöni, 2021. "Communication And Market Sharing: An Experiment On The Exchange Of Soft And Hard Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(1), pages 175-198, February.
    6. Huck, Steffen & Normann, Hans-Theo & Oechssler, Jorg, 2004. "Two are few and four are many: number effects in experimental oligopolies," Journal of Economic Behavior & Organization, Elsevier, vol. 53(4), pages 435-446, April.
    7. Jan Potters & Sigrid Suetens, 2013. "Oligopoly Experiments In The Current Millennium," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 439-460, July.
    8. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    9. Sören Krach & Frank Hegel & Britta Wrede & Gerhard Sagerer & Ferdinand Binkofski & Tilo Kircher, 2008. "Can Machines Think? Interaction and Perspective Taking with Robots Investigated via fMRI," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-11, July.
    10. Matthias Blonski & Peter Ockenfels & Giancarlo Spagnolo, 2011. "Equilibrium Selection in the Repeated Prisoner's Dilemma: Axiomatic Approach and Experimental Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 3(3), pages 164-192, August.
    11. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    12. Maria Bigoni & Marco Casari & Andrzej Skrzypacz & Giancarlo Spagnolo, 2015. "Time Horizon and Cooperation in Continuous Time," Econometrica, Econometric Society, vol. 83, pages 587-616, March.
    13. Niklas Horstmann & Jan Krämer & Daniel Schnurr, 2018. "Number Effects and Tacit Collusion in Experimental Oligopolies," Journal of Industrial Economics, Wiley Blackwell, vol. 66(3), pages 650-700, September.
    14. Charness, Gary & Rigotti, Luca & Rustichini, Aldo, 2016. "Social surplus determines cooperation rates in the one-shot Prisoner's Dilemma," Games and Economic Behavior, Elsevier, vol. 100(C), pages 113-124.
    15. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    16. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    17. Christoph Engel, 2015. "Tacit Collusion: The Neglected Experimental Evidence," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 12(3), pages 537-577, September.
    18. Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
    19. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    20. Roux, Catherine & Thöni, Christian, 2015. "Collusion among many firms: The disciplinary power of targeted punishment," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 83-93.
    21. Drew Fudenberg & David G. Rand & Anna Dreber, 2012. "Slow to Anger and Fast to Forgive: Cooperation in an Uncertain World," American Economic Review, American Economic Association, vol. 102(2), pages 720-749, April.
    22. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
    23. 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.
    24. Martin J. Osborne, 2006. "Strategic and extensive games," Working Papers tecipa-231, University of Toronto, Department of Economics.
    25. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    26. Blanco, Mariana & Engelmann, Dirk & Koch, Alexander K. & Normann, Hans-Theo, 2014. "Preferences and beliefs in a sequential social dilemma: a within-subjects analysis," Games and Economic Behavior, Elsevier, vol. 87(C), pages 122-135.
    27. Fonseca, Miguel A. & Normann, Hans-Theo, 2012. "Explicit vs. tacit collusion—The impact of communication in oligopoly experiments," European Economic Review, Elsevier, vol. 56(8), pages 1759-1772.
    28. Pedro Dal Bo & Guillaume R. Frochette, 2011. "The Evolution of Cooperation in Infinitely Repeated Games: Experimental Evidence," American Economic Review, American Economic Association, vol. 101(1), pages 411-429, February.
    29. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    30. Yves Breitmoser, 2015. "Cooperation, but No Reciprocity: Individual Strategies in the Repeated Prisoner's Dilemma," American Economic Review, American Economic Association, vol. 105(9), pages 2882-2910, September.
    31. Ahn, T K & Ostrom, Elinor & Shupp, Robert & Walker, James, 2001. "Cooperation in PD Games: Fear, Greed, and History of Play," Public Choice, Springer, vol. 106(1-2), pages 137-155, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Normann, Hans-Theo & Sternberg, Martin, 2022. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," DICE Discussion Papers 392, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Fourberg, Niklas & Marques-Magalhaes, Katrin & Wiewiorra, Lukas, 2022. "They are among us: Pricing behavior of algorithms in the field," WIK Working Papers 6, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, Bad Honnef.
    3. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    4. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.

    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 & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    2. Normann, Hans-Theo & Sternberg, Martin, 2022. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," DICE Discussion Papers 392, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    3. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    4. Emanuel Vespa & Taylor Weidman & Alistair J. Wilson, 2021. "Testing Models of Strategic Uncertainty: Equilibrium Selection in Repeated Games," Papers 2101.05900, arXiv.org.
    5. Aleksandar B. Todorov, 2022. "Algorithmic pricing and concerted behaviour – competitive challenges?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 90-107.
    6. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    7. Bernhard Kasberger & Simon Martin & Hans-Theo Normann & Tobias Werner, 2024. "Algorithmic Cooperation," CESifo Working Paper Series 11124, CESifo.
    8. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Young Researchers Working Papers 1, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    9. Gonzalo Ballestero, 2022. "Collusion and Artificial Intelligence: A Computational Experiment with Sequential Pricing Algorithms under Stochastic Costs," Working Papers 118, Red Nacional de Investigadores en Economía (RedNIE).
    10. Maximilian Andres, 2024. "Equilibrium selection in infinitely repeated games with communication," CEPA Discussion Papers 75, Center for Economic Policy Analysis.
    11. Fabian Dvorak & Sebastian Fehrler, 2024. "Negotiating Cooperation under Uncertainty: Communication in Noisy, Indefinitely Repeated Interactions," American Economic Journal: Microeconomics, American Economic Association, vol. 16(3), pages 232-258, August.
    12. 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.
    13. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    14. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    15. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    16. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    17. Eugenio Proto & Aldo Rustichini & Andis Sofianos, 2020. "Intelligence, Errors and Strategic Choices in the Repeated Prisoners Dilemma," Working Papers 2020_07, Business School - Economics, University of Glasgow.
    18. Tetsuya Kawamura & Tiffany Tsz Kwan Tse, 2022. "Intelligence promotes cooperation in long-term interaction: experimental evidence in infinitely repeated public goods games," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 927-946, October.
    19. Darai, D. & Roux, C. & Schneider, F., 2019. "Mergers, Mavericks, and Tacit Collusion," Cambridge Working Papers in Economics 1984, Faculty of Economics, University of Cambridge.
    20. Casoria, Fortuna & Ciccone, Alice, 2021. "Do upfront investments increase cooperation? A laboratory experiment," European Economic Review, Elsevier, vol. 140(C).

    More about this item

    Keywords

    algorithms; collusion; human-computer interaction; laboratory experiments;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

    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:mpg:wpaper:2021_11. 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: Marc Martin (email available below). General contact details of provider: https://edirc.repec.org/data/mppggde.html .

    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.