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Q-learning agents in a Cournot oligopoly model

Citations

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Cited by:

  1. Xingchen Xu & Stephanie Lee & Yong Tan, 2023. "Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems," Papers 2309.14548, arXiv.org.
  2. Bingyan Han, 2021. "Understanding algorithmic collusion with experience replay," Papers 2102.09139, arXiv.org, revised Mar 2021.
  3. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
  4. Fourberg, Niklas & Marques Magalhaes, Katrin & Wiewiorra, Lukas, 2023. "They Are Among Us: Pricing Behavior of Algorithms in the Field," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277958, International Telecommunications Society (ITS).
  5. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.
  6. Solferino, Nazaria & Solferino, Viviana & Taurino, Serena Fiona, 2015. "The economic analysis of a Q-learning model of Cooperation with punishment," MPRA Paper 66605, University Library of Munich, Germany.
  7. 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.
  8. Tharakunnel, Kurian & Bhattacharyya, Siddhartha, 2009. "Single-leader-multiple-follower games with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1593-1603, August.
  9. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
  10. Hanaki, Nobuyuki & Ishikawa, Ryuichiro & Akiyama, Eizo, 2009. "Learning games," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1739-1756, October.
  11. Soria, Jorge & Moya, Jorge & Mohazab, Amin, 2023. "Optimal mining in proof-of-work blockchain protocols," Finance Research Letters, Elsevier, vol. 53(C).
  12. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  13. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
  14. Kshitija Taywade & Brent Harrison & Judy Goldsmith, 2022. "Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand," Papers 2201.00486, arXiv.org.
  15. 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.
  16. Bingyan Han, 2022. "Can maker-taker fees prevent algorithmic cooperation in market making?," Papers 2211.00496, arXiv.org.
  17. Timo Klein, 2018. "Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing," Tinbergen Institute Discussion Papers 18-056/VII, Tinbergen Institute, revised 01 Nov 2020.
  18. David M. Newbery & Thomas Greve, 2013. "The Strategic Robustness of Mark-up Equilibria," Cambridge Working Papers in Economics 1341, Faculty of Economics, University of Cambridge.
  19. Rhodes, Andrew & Johnson, Justin & Wildenbeest, Matthijs, 2020. "Platform Design When Sellers Use Pricing Algorithms," CEPR Discussion Papers 15504, C.E.P.R. Discussion Papers.
  20. Nazaria Solferino & Viviana Solferino & Serena F. Taurino, 2018. "The economics analysis of a Q-learning model of cooperation with punishment and risk taking preferences," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 601-613, October.
  21. Calvano, Emilio & Calzolari, Giacomo & Denicolò, Vincenzo & Pastorello, Sergio, 2023. "Algorithmic collusion: Genuine or spurious?," International Journal of Industrial Organization, Elsevier, vol. 90(C).
  22. Tong Zhang & B. Brorsen, 2011. "Oligopoly firms with quantity-price strategic decisions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(2), pages 157-170, November.
  23. Junyi Xu, 2021. "Reinforcement Learning in a Cournot Oligopoly Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1001-1024, December.
  24. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
  25. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
  26. 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.
  27. César García-Díaz & Gábor Péli & Arjen van Witteloostuijn, 2020. "The coevolution of the firm and the product attribute space," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-25, June.
  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. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
  30. 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.
  31. Segismundo S. Izquierdo & Luis R. Izquierdo, 2015. "The “Win-Continue, Lose-Reverse” Rule In Oligopolies: Robustness Of Collusive Outcomes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-23, August.
  32. Yaroslav Rosokha & Kenneth Younge, 2020. "Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 569-582, July.
  33. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2018. "Multi-unit multiple bid auctions in balancing markets: an agent-based Q-learning approach," EWI Working Papers 2018-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
  34. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
  35. Frédéric Marty & Thierry Warin, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," CIRANO Working Papers 2023s-26, CIRANO.
  36. 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.
  37. Jeschonneck, Malte, 2021. "Collusion among autonomous pricing algorithms utilizing function approximation methods," DICE Discussion Papers 370, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  38. Juan Manuel Sánchez-Cartas & Alberto Tejero & Gonzalo León, 2021. "Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events," Sustainability, MDPI, vol. 13(5), pages 1-14, February.
  39. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
  40. 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.
  41. Daniele Condorelli & Massimiliano Furlan, 2023. "Cheap Talking Algorithms," Papers 2310.07867, arXiv.org, revised Dec 2023.
  42. Bingyan Han, 2022. "Cooperation between Independent Market Makers," Papers 2206.05410, arXiv.org.
  43. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2021. "Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach," Energy Economics, Elsevier, vol. 93(C).
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