IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2401.14757.html
   My bibliography  Save this paper

How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection

Author

Listed:
  • Hannes Wallimann
  • Silvio Sticher

Abstract

We present a classroom game that integrates economics and data-science competencies. In the first two parts of the game, participants assume the roles of firms in a procurement market, where they must either adopt competitive behaviors or have the option to engage in collusion. Success in these parts hinges on their comprehension of market dynamics. In the third part of the game, participants transition to the role of competition-authority members. Drawing from recent literature on machine-learning-based cartel detection, they analyze the bids for patterns indicative of collusive (cartel) behavior. In this part of the game, success depends on data-science skills. We offer a detailed discussion on implementing the game, emphasizing considerations for accommodating diverging levels of preexisting knowledge in data science.

Suggested Citation

  • Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.
    • Handle: RePEc:arx:papers:2401.14757
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2401.14757
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Correa, Manuel & García-Quero, Fernando & Ortega-Ortega, Marta, 2016. "A role-play to explain cartel behavior: Discussing the oligopolistic market," International Review of Economics Education, Elsevier, vol. 22(C), pages 8-15.
    2. Porter, Robert H & Zona, J Douglas, 1993. "Detection of Bid Rigging in Procurement Auctions," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 518-538, June.
    3. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    4. Charles A. Holt, 1999. "Teaching Economics with Classroom Experiments: A Symposium," Southern Economic Journal, John Wiley & Sons, vol. 65(3), pages 603-610, January.
    5. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    7. Rieko Ishii, 2014. "Bid Roundness Under Collusion in Japanese Procurement Auctions," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(3), pages 241-254, May.
    8. David Imhof & Yavuz Karagök & Samuel Rutz, 2018. "Screening For Bid Rigging—Does It Work?," Journal of Competition Law and Economics, Oxford University Press, vol. 14(2), pages 235-261.
    9. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    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. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    2. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    3. Wallimann, Hannes & Sticher, Silvio, 2023. "On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement," Transport Policy, Elsevier, vol. 143(C), pages 121-131.
    4. Huber, Martin & Imhof, David, 2023. "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    5. Silveira, Douglas & de Moraes, Lucas B. & Fiuza, Eduardo P.S. & Cajueiro, Daniel O., 2023. "Who are you? Cartel detection using unlabeled data," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    6. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    7. Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
    8. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    9. Lucas Gomes & Jannis Kueck & Mara Mattes & Martin Spindler & Alexey Zaytsev, 2024. "Collusion Detection with Graph Neural Networks," Papers 2410.07091, arXiv.org.
    10. Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
    11. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    12. Garcia Pires, Armando J. & Skjeret, Frode, 2023. "Screening for partial collusion in retail electricity markets," Energy Economics, Elsevier, vol. 117(C).
    13. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    14. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-7, University of Alberta, Department of Economics.
    15. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
    16. Clark, Robert & Coviello, Decio & de Leverano, Adriano, 2020. "Complementary bidding and the collusive arrangement: Evidence from an antitrust investigation," ZEW Discussion Papers 20-052, ZEW - Leibniz Centre for European Economic Research.
    17. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    18. Martinez-Carrasco, José & ConceiçaÞo, Otavio & Dezolt, Ana Lúcia, 2023. "More Information, Lower Price? Access Market-based Reference Prices and Gains in Public Procurement Efficiency," IDB Publications (Working Papers) 12754, Inter-American Development Bank.
    19. Dmitry I. Ivanov & Alexander S. Nesterov, 2019. "Stealed-bid Auctions: Detecting Bid Leakage via Semi-Supervised Learning," Papers 1903.00261, arXiv.org, revised Nov 2020.
    20. Sylvain Chassang & Kei Kawai & Jun Nakabayashi & Juan Ortner, 2022. "Robust Screens for Noncompetitive Bidding in Procurement Auctions," Econometrica, Econometric Society, vol. 90(1), pages 315-346, January.

    More about this item

    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:arx:papers:2401.14757. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.