Deep Q-learning of Prices in Oligopolies: The Number of Competitors Matters
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- 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.
- Huck, Steffen & Normann, Hans-Theo & Oechssler, Jörg, 2001. "Two are Few and Four are Many: Number Effects in Experimental Oligopolies," Bonn Econ Discussion Papers 12/2001, University of Bonn, Bonn Graduate School of Economics (BGSE).
- Matthias Hettich, 2021. "Algorithmic Collusion: Insights from Deep Learning," CQE Working Papers 9421, Center for Quantitative Economics (CQE), University of Muenster.
- Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
- 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.
- 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.
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.- 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.
- Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
- Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
- 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).
- Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
- Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
- Aleksei Pastushkov, 2024. "Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents," Papers 2411.05032, arXiv.org.
- Buchali, Katrin & Grüb, Jens & Muijs, Matthias & Schwalbe, Ulrich, 2023. "Strategic choice of price-setting algorithms," Hohenheim Discussion Papers in Business, Economics and Social Sciences 01-2023, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
- Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
- Buchali, Katrin & Grüb, Jens & Muijs, Matthias & Schwalbe, Ulrich, 2023. "Strategic Choice of Price-Setting Algorithms," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277695, Verein für Socialpolitik / German Economic Association.
- Hanspach, Philip & Sapi, Geza & Wieting, Marcel, 2024. "Algorithms in the marketplace: An empirical analysis of automated pricing in e-commerce," Information Economics and Policy, Elsevier, vol. 69(C).
- Justin P. Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023.
"Platform Design When Sellers Use Pricing Algorithms,"
Econometrica, Econometric Society, vol. 91(5), pages 1841-1879, September.
- Johnson, Justin Pappas & Rhodes, Andrew & Wildenbeest, Matthijs, 2020. "Platform Design when Sellers Use Pricing Algorithms," TSE Working Papers 20-1146, Toulouse School of Economics (TSE).
- Rhodes, Andrew & Johnson, Justin & Wildenbeest, Matthijs, 2020. "Platform Design When Sellers Use Pricing Algorithms," CEPR Discussion Papers 15504, C.E.P.R. Discussion Papers.
- Justin Pappas Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform design when sellers use pricing algorithms," Post-Print hal-04226232, HAL.
- Eshwar Ram Arunachaleswaran & Natalie Collina & Sampath Kannan & Aaron Roth & Juba Ziani, 2024. "Algorithmic Collusion Without Threats," Papers 2409.03956, arXiv.org, revised Dec 2024.
- Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).
- Jason D. Hartline & Sheng Long & Chenhao Zhang, 2024. "Regulation of Algorithmic Collusion," Papers 2401.15794, arXiv.org, revised Sep 2024.
- Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
- 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.
- Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
- Colombo, Stefano & Filippini, Luigi & Pignataro, Aldo, 2024. "Information sharing, personalized pricing, and collusion," Information Economics and Policy, Elsevier, vol. 66(C).
- Leonardo Madio & Aldo Pignataro, 2022. "Collusion sustainability with a capacity constrained firm," "Marco Fanno" Working Papers 0295, Dipartimento di Scienze Economiche "Marco Fanno".
More about this item
Keywords
algorithmic price setting; deep Q-network; oligopoly; supracompetitive prices;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2025-01-06 (Artificial Intelligence)
- NEP-CMP-2025-01-06 (Computational Economics)
- NEP-COM-2025-01-06 (Industrial Competition)
- NEP-IND-2025-01-06 (Industrial Organization)
- NEP-REG-2025-01-06 (Regulation)
Statistics
Access and download statisticsCorrections
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:gre:wpaper:2024-32. 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: Patrice Bougette (email available below). General contact details of provider: https://edirc.repec.org/data/credcfr.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.