IDEAS home Printed from https://ideas.repec.org/p/zbw/dicedp/375.html
   My bibliography  Save this paper

Collusive compensation schemes aided by algorithms

Author

Listed:
  • Martin, Simon
  • Schmal, W. Benedikt

Abstract

Sophisticated collusive compensation schemes such as assigning future market shares or direct transfers are frequently observed in detected cartels. We show formally why these schemes are useful for dampening deviation incentives when colluding firms are temporary asymmetric. The relative attractiveness of each of these schemes is shaped by firms' ability to predict future market conditions, possibly aided by algorithms. Prices and profits are inverse u-shaped in prediction ability. Assigning future market shares is optimal when prediction ability is intermediate, and otherwise direct transfers are optimal. Competition authority's limited resources should be utilized to respond to these changing market conditions.

Suggested Citation

  • Martin, Simon & Schmal, W. Benedikt, 2021. "Collusive compensation schemes aided by algorithms," DICE Discussion Papers 375, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  • Handle: RePEc:zbw:dicedp:375
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/248280/1/1783490926.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Joseph E. Harrington & Andrzej Skrzypacz, 2011. "Private Monitoring and Communication in Cartels: Explaining Recent Collusive Practices," American Economic Review, American Economic Association, vol. 101(6), pages 2425-2449, October.
    2. Marc Bourreau & Yutec Sun & Frank Verboven, 2021. "Market Entry, Fighting Brands, and Tacit Collusion: Evidence from the French Mobile Telecommunications Market," American Economic Review, American Economic Association, vol. 111(11), pages 3459-3499, November.
    3. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    4. Normann, Hans-Theo & Tan, Elaine S., 2013. "Effects of different cartel policies: Evidence from the German power-cable industry," DICE Discussion Papers 108, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    5. Spagnolo, Giancarlo, 2004. "Divide et Impera: Optimal Leniency Programmes," CEPR Discussion Papers 4840, C.E.P.R. Discussion Papers.
    6. O’Connor, Jason & Wilson, Nathan E., 2021. "Reduced demand uncertainty and the sustainability of collusion: How AI could affect competition," Information Economics and Policy, Elsevier, vol. 54(C).
    7. Bos, Iwan & Marini, Marco A. & Saulle, Riccardo D., 2020. "Cartel formation with quality differentiation," Mathematical Social Sciences, Elsevier, vol. 106(C), pages 36-50.
    8. Paul Belleflamme & Francis Bloch, 2004. "Market sharing agreements and collusive networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 387-411, May.
    9. Ari Hyytinen & Frode Steen & Otto Toivanen, 2019. "An Anatomy of Cartel Contracts," The Economic Journal, Royal Economic Society, vol. 129(621), pages 2155-2191.
    10. Joseph E. Harrington & Myong-Hun Chang, 2009. "Modeling the Birth and Death of Cartels with an Application to Evaluating Competition Policy," Journal of the European Economic Association, MIT Press, vol. 7(6), pages 1400-1435, December.
    11. Rotemberg, Julio J & Saloner, Garth, 1986. "A Supergame-Theoretic Model of Price Wars during Booms," American Economic Review, American Economic Association, vol. 76(3), pages 390-407, June.
    12. 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.
    13. Robert Clark & Jean-Fran?ois Houde, 2013. "Collusion with Asymmetric Retailers: Evidence from a Gasoline Price-Fixing Case," American Economic Journal: Microeconomics, American Economic Association, vol. 5(3), pages 97-123, August.
    14. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    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. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    3. Schmal, Wolfgang Benedikt, 2024. "Polycentric governance in collusive agreements," Freiburg Discussion Papers on Constitutional Economics 24/1, Walter Eucken Institut e.V..

    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. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    2. Isogai, Shigeki & Shen, Chaohai, 2023. "Multiproduct firm’s reputation and leniency program in multimarket collusion," Economic Modelling, Elsevier, vol. 125(C).
    3. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    4. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    5. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    6. Stephanie Assad & Emilio Calvano & Giacomo Calzolari & Robert Clark & Vincenzo Denicolò & Daniel Ershov & Justin Johnson & Sergio Pastorello & Andrew Rhodes & Lei Xu & Matthijs Wildenbeest, 2021. "Autonomous algorithmic collusion: economic research and policy implications," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 459-478.
    7. 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.
    8. 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).
    9. Jorge Lemus & Fernando Luco, 2021. "Price Leadership and Uncertainty About Future Costs," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 305-337, June.
    10. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    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. Porter, Robert H., 2020. "Mergers and coordinated effects," International Journal of Industrial Organization, Elsevier, vol. 73(C).
    13. 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).
    14. Aleksandar B. Todorov, 2022. "Algorithmic pricing and concerted behaviour – competitive challenges?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 90-107.
    15. Roberta Dessì & Salvatore Piccolo, 2008. "Two is Company, N is a Crowd? Merchant Guilds and Social Capital," CSEF Working Papers 202, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy, revised 12 Jul 2009.
    16. Abito, Jose Miguel & Chen, Cuicui, 2023. "A partial identification framework for dynamic games," International Journal of Industrial Organization, Elsevier, vol. 87(C).
    17. Emons, Winand, 2020. "The effectiveness of leniency programs when firms choose the degree of collusion," International Journal of Industrial Organization, Elsevier, vol. 70(C).
    18. Salvatore Piccolo & Giancarlo Spagnolo, 2014. "Debt, Managers and Cartels," CSEF Working Papers 365, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    19. Holler, Emanuel & Rickert, Dennis, 2022. "How resale price maintenance and loss leading affect upstream cartel stability: Anatomy of a coffee cartel," International Journal of Industrial Organization, Elsevier, vol. 85(C).
    20. Hellwig, Michael & Hüschelrath, Kai, 2018. "When Do Firms Leave Cartels? Determinants And The Impact On Cartel Survival," International Review of Law and Economics, Elsevier, vol. 54(C), pages 68-84.

    More about this item

    Keywords

    algorithmic collusion; market forecasting; prediction ability; firm asymmetry; compensation schemes;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

    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:zbw:dicedp:375. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/diduede.html .

    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.