IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v18y2015i05n06ns0219525915500137.html
   My bibliography  Save this article

The “Win-Continue, Lose-Reverse” Rule In Oligopolies: Robustness Of Collusive Outcomes

Author

Listed:
  • SEGISMUNDO S. IZQUIERDO

    (Department of Industrial Organization, Universidad de Valladolid, EII, 47011 Spain)

  • LUIS R. IZQUIERDO

    (Department of Civil Engineering, Universidad de Burgos, 09001 Spain)

Abstract

The so-called “Win-Continue, Lose-Reverse” (WCLR) rule is a simple iterative procedure that can be used to choose a value for any numeric variable (e.g., setting a price or a production level). The rule dictates that one should evaluate the effect on profits of the last adjustment made to the value (e.g., a price variation), and keep on changing the value in the same direction if the adjustment led to greater profits, or reverse the direction of change otherwise. Somewhat surprisingly, this simple rule has been shown to lead to collusive outcomes in Cournot oligopolies, even though its application requires no information about the other firms’ profits or choices. In this paper, we show that the convergence of the WCLR rule toward collusive outcomes can be very sensitive to small independent perturbations in the cost functions or in the income functions of the firms. These perturbations typically push the process toward the Nash equilibrium of the one-shot game. We also explore the behavior of WCLR against other strategies and demonstrate that WCLR is easily exploitable. We then conduct a similar analysis of the WCLR rule in a differentiated Bertrand model, where firms compete in prices.

Suggested Citation

  • 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.
  • Handle: RePEc:wsi:acsxxx:v:18:y:2015:i:05n06:n:s0219525915500137
    DOI: 10.1142/S0219525915500137
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219525915500137
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219525915500137?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
    2. Keen, Steve & Standish, Russell, 2006. "Profit maximization, industry structure, and competition: A critique of neoclassical theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 81-85.
    3. Friedman, Daniel & Huck, Steffen & Oprea, Ryan & Weidenholzer, Simon, 2015. "From imitation to collusion: Long-run learning in a low-information environment," Journal of Economic Theory, Elsevier, vol. 155(C), pages 185-205.
    4. Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou, 2015. "Advances in Artificial Economics," Post-Print hal-03209315, HAL.
    5. Steffen Huck & Hans-Theo Normann & Joerg Oechssler, 2004. "Through Trial and Error to Collusion," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 205-224, February.
    6. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Timo Klein, 2018. "Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing," Tinbergen Institute Discussion Papers 18-056/VII, Tinbergen Institute, revised 01 Nov 2020.
    2. 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.

    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. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
    2. Jörg Oechssler & Alex Roomets & Stefan Roth, 2016. "From imitation to collusion: a replication," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 2(1), pages 13-21, May.
    3. Jasmina Arifovic & Liang Dia & Nobuyuki Hanaki, 2023. "An individual evolutionary learning model meets Cournot," ISER Discussion Paper 1200, Institute of Social and Economic Research, Osaka University.
    4. Huck, Steffen & Leutgeb, Johannes & Oprea, Ryan, 2017. "Payoff information hampers the evolution of cooperation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 8, pages 1-1.
    5. 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.
    6. Armstrong, Mark & Huck, Steffen, 2010. "Behavioral economics as applied to firms: a primer," MPRA Paper 20356, University Library of Munich, Germany.
    7. Masiliūnas, Aidas & Nax, Heinrich H., 2020. "Framing and repeated competition," Games and Economic Behavior, Elsevier, vol. 124(C), pages 604-619.
    8. Cerboni Baiardi, Lorenzo & Naimzada, Ahmad K., 2019. "An oligopoly model with rational and imitation rules," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 254-278.
    9. Nobuyuki Hanaki & Aidas Masiliunas, 2021. "Market Concentration and Incentives to Collude in Cournot Oligopoly Experiments," ISER Discussion Paper 1131, Institute of Social and Economic Research, Osaka University.
    10. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
    11. Lorenzo Cerboni Baiardi & Ahmad K. Naimzada, 2019. "An evolutionary Cournot oligopoly model with imitators and perfect foresight best responders," Metroeconomica, Wiley Blackwell, vol. 70(3), pages 458-475, July.
    12. Oechssler, Jörg & Roomets, Alex & Roth, Stefan, 2015. "From Imitation to Collusion - A Comment," Working Papers 0588, University of Heidelberg, Department of Economics.
    13. Francesco Fallucchi & Jan Niederreiter & Massimo Riccaboni, 2021. "Learning and dropout in contests: an experimental approach," Theory and Decision, Springer, vol. 90(2), pages 245-278, March.
    14. Alós-Ferrer, Carlos & Ritschel, Alexander, 2021. "Multiple behavioral rules in Cournot oligopolies," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 250-267.
    15. Bigoni, Maria, 2010. "What do you want to know? Information acquisition and learning in experimental Cournot games," Research in Economics, Elsevier, vol. 64(1), pages 1-17, March.
    16. 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.
    17. Bervoets, Sebastian & Bravo, Mario & Faure, Mathieu, 2020. "Learning with minimal information in continuous games," Theoretical Economics, Econometric Society, vol. 15(4), November.
    18. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    19. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    20. Andreas Nicklisch, 2011. "Learning strategic environments: an experimental study of strategy formation and transfer," Theory and Decision, Springer, vol. 71(4), pages 539-558, October.

    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:wsi:acsxxx:v:18:y:2015:i:05n06:n:s0219525915500137. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    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.