IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i5p2542-d506458.html
   My bibliography  Save this article

Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events

Author

Listed:
  • Juan Manuel Sánchez-Cartas

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

  • Alberto Tejero

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

  • Gonzalo León

    (Campus de Montegancedo, Universidad Politécnica de Madrid, 28223 Madrid, Spain)

Abstract

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2542-:d:506458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/5/2542/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/5/2542/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    4. Hess, Thomas & Matt, Christian & Benlian, Alexander & Wiesböck, Florian, 2016. "Options for Formulating a Digital Transformation Strategy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 81032, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 399-417, November.
    6. 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.
    7. Xavier Vives, 2008. "Innovation And Competitive Pressure," Journal of Industrial Economics, Wiley Blackwell, vol. 56(3), pages 419-469, December.
    8. 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.
    9. Michael K. Maschek, 2015. "Particle Swarm Optimization in Agent‐Based Economic Simulations of the Cournot Market Model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 133-152, April.
    10. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    11. Cheng, Hsing Kenneth & Tang, Qian Candy, 2010. "Free trial or no free trial: Optimal software product design with network effects," European Journal of Operational Research, Elsevier, vol. 205(2), pages 437-447, September.
    12. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
    13. Hess, Thomas & Matt, Christian & Benlian, Alexander & Wiesböck, Florian, 2016. "Options for Formulating a Digital Transformation Strategy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 82423, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    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. João E. Gata, 2019. "Controlling Algorithmic Collusion: short review of the literature, undecidability, and alternative approaches," Working Papers REM 2019/77, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    3. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    4. 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.
    5. Paolo Crosetto & Alexia Gaudeul, 2011. "Do consumers prefer offers that are easy to compare? An experimental investigation," Jena Economics Research Papers 2011-044, Friedrich-Schiller-University Jena.
    6. Jehiel, Philippe & Singh, Juni, 2021. "Multi-state choices with aggregate feedback on unfamiliar alternatives," Games and Economic Behavior, Elsevier, vol. 130(C), pages 1-24.
    7. Mauersberger, Felix, 2019. "Thompson Sampling: Endogenously Random Behavior in Games and Markets," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203600, Verein für Socialpolitik / German Economic Association.
    8. Hanaki, Nobuyuki & Ishikawa, Ryuichiro & Akiyama, Eizo, 2009. "Learning games," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1739-1756, October.
    9. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
    10. 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.
    11. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    12. 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.
    13. Crosetto, Paolo & Gaudeul, Alexia, 2012. "Do consumers prefer offers that are easy to compare? An experimental investigation," MPRA Paper 41462, University Library of Munich, Germany.
    14. Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    15. Emerson Melo, 2022. "On the uniqueness of quantal response equilibria and its application to network games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 74(3), pages 681-725, October.
    16. Wolpert David & Leslie David S., 2012. "Information Theory and Observational Limitations in Decision Making," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 12(1), pages 1-43, January.
    17. Galbiati, Marco & Soramäki, Kimmo, 2011. "An agent-based model of payment systems," Journal of Economic Dynamics and Control, Elsevier, vol. 35(6), pages 859-875, June.
    18. Swen Nadkarni & Reinhard Prügl, 2021. "Digital transformation: a review, synthesis and opportunities for future research," Management Review Quarterly, Springer, vol. 71(2), pages 233-341, April.
    19. Schipper, Burkhard C., 2021. "Discovery and equilibrium in games with unawareness," Journal of Economic Theory, Elsevier, vol. 198(C).
    20. Hans-Joachim Schramm & Carolin Nicole Czaja & Michael Dittrich & Matthias Mentschel, 2019. "Current Advancements of and Future Developments for Fourth Party Logistics in a Digital Future," Logistics, MDPI, vol. 3(1), pages 1-17, February.

    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:gam:jsusta:v:13:y:2021:i:5:p:2542-:d:506458. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.