IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/13405.html
   My bibliography  Save this paper

Artificial intelligence, algorithmic pricing and collusion

Author

Listed:
  • Calzolari, Giacomo
  • Calvano, Emilio
  • Denicolo, Vincenzo
  • Pastorello, Sergio

Abstract

Increasingly, pricing algorithms are supplanting human decision making in real marketplaces. To inform the competition policy debate on the possible consequences of this development, we experiment with pricing algorithms powered by Artificial Intelligence (AI) in controlled environments (computer simulations), studying the interaction among a number of Q-learning algorithms in a workhorse oligopoly model of price competition with Logit demand and constant marginal costs. In this setting the algorithms consistently learn to charge supra-competitive prices, without communicating with one another. The high prices are sustained by classical collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand and to changes in the number of players.

Suggested Citation

  • Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2018. "Artificial intelligence, algorithmic pricing and collusion," CEPR Discussion Papers 13405, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13405
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP13405
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. David J. Cooper & Kai-Uwe K?hn, 2014. "Communication, Renegotiation, and the Scope for Collusion," American Economic Journal: Microeconomics, American Economic Association, vol. 6(2), pages 247-278, May.
    2. Yuliy Sannikov & Andrzej Skrzypacz, 2007. "Impossibility of Collusion under Imperfect Monitoring with Flexible Production," American Economic Review, American Economic Association, vol. 97(5), pages 1794-1823, December.
    3. Leufkens, Kasper & Peeters, Ronald, 2011. "Price dynamics and collusion under short-run price commitments," International Journal of Industrial Organization, Elsevier, vol. 29(1), pages 134-153, January.
    4. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    5. Rajeev K. Tyagi, 1999. "On the relationship between product substitutability and tacit collusion," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(6), pages 293-298.
    6. Barlo, Mehmet & Carmona, Guilherme & Sabourian, Hamid, 2016. "Bounded memory Folk Theorem," Journal of Economic Theory, Elsevier, vol. 163(C), pages 728-774.
    7. Ho, Teck H. & Camerer, Colin F. & Chong, Juin-Kuan, 2007. "Self-tuning experience weighted attraction learning in games," Journal of Economic Theory, Elsevier, vol. 133(1), pages 177-198, March.
    8. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    9. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    10. 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.
    11. Maskin, Eric & Tirole, Jean, 1988. "A Theory of Dynamic Oligopoly, II: Price Competition, Kinked Demand Curves, and Edgeworth Cycles," Econometrica, Econometric Society, vol. 56(3), pages 571-599, May.
    12. Drew Fudenberg & David K. Levine, 2016. "Whither Game Theory? Towards a Theory of Learning in Games," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 151-170, Fall.
    13. Drew Fudenberg & David K Levine, 2016. "Whither Game Theory?," Levine's Working Paper Archive 786969000000001307, David K. Levine.
    14. 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.
    15. Nirvikar Singh & Xavier Vives, 1984. "Price and Quantity Competition in a Differentiated Duopoly," RAND Journal of Economics, The RAND Corporation, vol. 15(4), pages 546-554, Winter.
    16. 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.
    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. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
    2. Terry E. Daniel & Eyran J. Gisches & Amnon Rapoport, 2009. "Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks," American Economic Review, American Economic Association, vol. 99(5), pages 2149-2176, December.
    3. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    4. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
    5. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    6. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    7. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard C., 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 63, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
    8. Dalton, Michael & Landry, Peter, 2020. "‘Overattention’ to first-hand experience in hiring decisions: Evidence from professional basketball," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 98-113.
    9. Jan Potters & Sigrid Suetens, 2013. "Oligopoly Experiments In The Current Millennium," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 439-460, July.
    10. Pablo S. Castro & Ajit Desai & Han Du & Rodney Garratt & Francisco Rivadeneyra, 2021. "Estimating Policy Functions in Payments Systems Using Reinforcement Learning," Staff Working Papers 21-7, Bank of Canada.
    11. 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.
    12. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    13. Aloys Prinz, 2019. "Learning (Not) to Evade Taxes," Games, MDPI, vol. 10(4), pages 1-18, September.
    14. Nicklisch, Andreas & Köke, Sonja & Lange, Andreas, 2016. "Is Adversity a School of Wisdom? Experimental Evidence on Cooperative Protection Against Stochastic Losses," VfS Annual Conference 2016 (Augsburg): Demographic Change 145716, Verein für Socialpolitik / German Economic Association.
    15. 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.
    16. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    17. Todd Guilfoos & Andreas Duus Pape, 2020. "Estimating Case-Based Learning," Games, MDPI, vol. 11(3), pages 1-25, September.
    18. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 475-503, December.
    19. Jieyao Ding & Andreas Nicklisch, 2013. "On the Impulse in Impulse Learning," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2013_02, Max Planck Institute for Research on Collective Goods.
    20. Chernov, G. & Susin, I., 2019. "Models of learning in games: An overview," Journal of the New Economic Association, New Economic Association, vol. 44(4), pages 77-125.

    More about this item

    Keywords

    Artificial intelligence; Pricing-algorithms; Collusion; Reinforcement learning; Q-learning;
    All these keywords.

    JEL classification:

    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:cpr:ceprdp:13405. 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: the person in charge (email available below). General contact details of provider: https://www.cepr.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.