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Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets

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  • Normann, Hans-Theo
  • Sternberg, Martin

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 do seem to perceive algorithms as more disruptive.

Suggested Citation

  • 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).
  • Handle: RePEc:zbw:dicedp:392
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    1. Charness, Gary & Rabin, Matthew, 2001. "Understanding Social Preferences with Simple Tests," Department of Economics, Working Paper Series qt4qz9k8vg, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. Haucap, Justus, 2021. "Mögliche Wohlfahrtswirkungen eines Einsatzes von Algorithmen," DICE Ordnungspolitische Perspektiven 109, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    8. Jan Potters & Sigrid Suetens, 2013. "Oligopoly Experiments In The Current Millennium," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 439-460, July.
    9. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    10. Pablo Hernandez-Lagos & Dylan Minor & Dana Sisak, 2017. "Do people who care about others cooperate more? Experimental evidence from relative incentive pay," Experimental Economics, Springer;Economic Science Association, vol. 20(4), pages 809-835, December.
    11. Dorothée Honhon & Kyle Hyndman, 2020. "Flexibility and Reputation in Repeated Prisoner’s Dilemma Games," Management Science, INFORMS, vol. 66(11), pages 4998-5014, November.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Doruk İriş & Luís Santos-Pinto, 2013. "Tacit Collusion under Fairness and Reciprocity," Games, MDPI, vol. 4(1), pages 1-16, February.
    17. J. Keith Murnighan & Alvin E. Roth, 1983. "Expecting Continued Play in Prisoner's Dilemma Games," Journal of Conflict Resolution, Peace Science Society (International), vol. 27(2), pages 279-300, June.
    18. 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.
    19. 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.
    20. Christoph Engel, 2015. "Tacit Collusion: The Neglected Experimental Evidence," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 12(3), pages 537-577, September.
    21. 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.
    22. 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.
    23. 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.
    24. Gary Charness & Matthew Rabin, 2002. "Understanding Social Preferences with Simple Tests," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(3), pages 817-869.
    25. 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.
    26. Julian Romero & Yaroslav Rosokha, 2019. "The Evolution of Cooperation: The Role of Costly Strategy Adjustments," American Economic Journal: Microeconomics, American Economic Association, vol. 11(1), pages 299-328, February.
    27. 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.
    28. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. David P. Byrne & Nicolas de Roos, 2019. "Learning to Coordinate: A Study in Retail Gasoline," American Economic Review, American Economic Association, vol. 109(2), pages 591-619, February.
    34. Pedro Dal Bó & Guillaume R. Fréchette, 2019. "Strategy Choice in the Infinitely Repeated Prisoner's Dilemma," American Economic Review, American Economic Association, vol. 109(11), pages 3929-3952, November.
    35. 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.
    36. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    37. Duffy, John & Xie, Huan, 2016. "Group size and cooperation among strangers," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 55-74.
    38. Gangadharan, Lata & Nikiforakis, Nikos, 2009. "Does the size of the action set matter for cooperation?," Economics Letters, Elsevier, vol. 104(3), pages 115-117, September.
    39. 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.
    40. 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.
    41. Friederike Mengel, 2018. "Risk and Temptation: A Meta‐study on Prisoner's Dilemma Games," Economic Journal, Royal Economic Society, vol. 128(616), pages 3182-3209, December.
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    Cited by:

    1. 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.
    2. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).

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    More about this item

    Keywords

    algorithms; collusion; human-computer interaction; labora-tory 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

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