[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04226232.html
   My bibliography  Save this paper

Platform design when sellers use pricing algorithms

Author

Listed:
  • Justin Pappas Johnson

    (CORNELL UNIVERSITY PORTLAND USA - Partenaires IRSTEA - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture)

  • Andrew Rhodes

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Matthijs Wildenbeest

    (Indiana University [Bloomington] - Indiana University System)

Abstract
We investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and increase its own payoff. We consider demand‐steering rules that reward firms that cut prices with additional exposure to consumers. We examine the impact of these rules both in theory and by using simulations with artificial intelligence pricing algorithms (specifically Q‐learning algorithms, which are commonly used in computer science). Our theoretical results indicate that these policies (which require little information to implement) can have strongly beneficial effects, even when sellers are infinitely patient and seek to collude. Similarly, our simulations suggest that platform design can benefit consumers and the platform, but that achieving these gains may require policies that condition on past behavior and treat sellers in a nonneutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to low prices.

Suggested Citation

  • Justin Pappas Johnson & Andrew Rhodes & Matthijs Wildenbeest, 2023. "Platform design when sellers use pricing algorithms," Post-Print hal-04226232, HAL.
    • Handle: RePEc:hal:journl:hal-04226232
    DOI: 10.3982/ECTA19978
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Michael Dinerstein & Liran Einav & Jonathan Levin & Neel Sundaresan, 2018. "Consumer Price Search and Platform Design in Internet Commerce," American Economic Review, American Economic Association, vol. 108(7), pages 1820-1859, July.
    2. Alexandre de Cornière & Greg Taylor, 2019. "A model of biased intermediation," RAND Journal of Economics, RAND Corporation, vol. 50(4), pages 854-882, December.
    3. 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.
    4. 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.
    5. 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.
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    7. Tat-How Teh & Julian Wright, 2022. "Intermediation and Steering: Competition in Prices and Commissions," American Economic Journal: Microeconomics, American Economic Association, vol. 14(2), pages 281-321, May.
    8. Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
    9. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    10. Leslie M. Marx & Greg Shaffer, 2010. "Slotting Allowances and Scarce Shelf Space," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(3), pages 575-603, September.
    11. Palfrey, Thomas R, 1983. "Bundling Decisions by a Multiproduct Monopolist with Incomplete Information," Econometrica, Econometric Society, vol. 51(2), pages 463-483, March.
    12. Cary A. Deck & Bart J. Wilson, 2003. "Automated Pricing Rules in Electronic Posted Offer Markets," Economic Inquiry, Western Economic Association International, vol. 41(2), pages 208-223, April.
    13. Roman Inderst & Marco Ottaviani, 2012. "Competition through Commissions and Kickbacks," American Economic Review, American Economic Association, vol. 102(2), pages 780-809, April.
    14. Daniel P. O'Brien & Greg Shaffer, 1997. "Nonlinear Supply Contracts, Exclusive Dealing, and Equilibrium Market Foreclosure," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(4), pages 755-785, December.
    15. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    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.
    17. Dana, James D., 2012. "Buyer groups as strategic commitments," Games and Economic Behavior, Elsevier, vol. 74(2), pages 470-485.
    18. Andrei Hagiu & Bruno Jullien, 2011. "Why do intermediaries divert search?," RAND Journal of Economics, RAND Corporation, vol. 42(2), pages 337-362, June.
    19. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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).
    3. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    4. 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.
    5. 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).
    6. Bingyan Han, 2021. "Understanding algorithmic collusion with experience replay," Papers 2102.09139, arXiv.org, revised Mar 2021.
    7. Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).
    8. 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).
    9. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    10. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    11. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    12. Dirk Bergemann & Alessandro Bonatti, 2024. "Data, Competition, and Digital Platforms," American Economic Review, American Economic Association, vol. 114(8), pages 2553-2595, August.
    13. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    14. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.
    15. 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).
    16. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    17. Jason D. Hartline & Sheng Long & Chenhao Zhang, 2024. "Regulation of Algorithmic Collusion," Papers 2401.15794, arXiv.org, revised Sep 2024.
    18. Martin Peitz, 2023. "Governance and Regulation of Platforms," CRC TR 224 Discussion Paper Series crctr224_2023_480, University of Bonn and University of Mannheim, Germany.
    19. Huang, Yangguang & Xie, Yu, 2023. "Search algorithm, repetitive information, and sales on online platforms," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    20. 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

    JEL classification:

    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
    • L00 - Industrial Organization - - General - - - General

    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:hal:journl:hal-04226232. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.