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Artificial intelligence, algorithmic pricing and collusion

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  • Calvano, Emilio
  • Calzolari, Giacomo
  • Denicolò, 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

  • Calvano, Emilio & Calzolari, Giacomo & Denicolò, 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
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    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. D. Fudenberg & D. K. Levine., 2017. "Whither game theory? Towards a theory of learning in games," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 5.
    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.
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    More about this item

    Keywords

    artificial intelligence; Collusion; Pricing-Algorithms; Q-Learning; Reinforcement Learning;
    All these keywords.

    JEL classification:

    • 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
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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