IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n5159393.html

Analysis on the Types and Characteristics of Collusion Behavior of Government Investment Project Tenderees

Author

Listed:
  • Sirui Nie
  • Yun Chen
  • Jingjing Li
  • Wenxi Zhu
  • Chongsen Ma

Abstract

The classification of collusion behaviors of government‐invested project tenderers is one of the important methods to describe the characteristics and laws of collusion behaviors and strengthen the governance of collusion. Firstly, the variables that affect the type of collusion behavior are selected and cluster analysis is carried out on the cases of collusion in government investment project bidding. Then use the social network to mine the types and characteristics of the collusion behavior of the tenderee. Finally, a BP neural network automatic identification model is established to quickly discriminate the types of collusion, which can effectively overcome the subjectivity of traditional methods. As a result, three typical types of collusion of tenderees can be obtained: intervention type, opportunity type, and cooperation type. The study found that the three types of collusion behavior have their own characteristics and laws, and the relationship between the variables that affect the type of collusion behavior is complex and affects each other.

Suggested Citation

  • Sirui Nie & Yun Chen & Jingjing Li & Wenxi Zhu & Chongsen Ma, 2022. "Analysis on the Types and Characteristics of Collusion Behavior of Government Investment Project Tenderees," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5159393
    DOI: 10.1155/2022/5159393
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/5159393
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/5159393?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
    ---><---

    References listed on IDEAS

    as
    1. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    2. 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.
    3. Brianzoni, Serena & Coppier, Raffaella & Michetti, Elisabetta, 2019. "Evolutionary effects of non-compliant behavior in public procurement," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 106-118.
    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. 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.
    2. David P. Brown & Andrew Eckert & Douglas Silveira, 2023. "Screening for Collusion in Wholesale Electricity Markets: A Review of the Literature," Working Papers 2023-07, University of Alberta, Department of Economics.
    3. 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.
    4. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).
    5. Aaltio, Aapo & Buri, Riku & Jokelainen, Antto & Lundberg, Johan, 2025. "Complementary bidding and cartel detection: Evidence from Nordic asphalt markets," International Journal of Industrial Organization, Elsevier, vol. 98(C).
    6. David P. Brown & Andrew Eckert, 2022. "Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?," Journal of Industrial Economics, Wiley Blackwell, vol. 70(1), pages 168-216, March.
    7. De Leverano, Adriano, 2023. "Clustered bids in first-price auctions: Collusion or competition?," Economics Letters, Elsevier, vol. 233(C).
    8. Gabrielli, M. Florencia & Willington, Manuel, 2023. "Estimating damages from bidding rings in first-price auctions," Economic Modelling, Elsevier, vol. 126(C).
    9. Raffaella Coppier & Elisabetta Michetti & Anastasiia Panchuk, 2025. "Evolution of dishonest behavior in public procurement: the role of updating control," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 48(2), pages 1613-1639, December.
    10. 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).
    11. Ribeiro, Haroldo V. & Lopes, Diego D. & Pessa, Arthur A.B. & Martins, Alvaro F. & da Cunha, Bruno R. & Gonçalves, Sebastián & Lenzi, Ervin K. & Hanley, Quentin S. & Perc, Matjaž, 2023. "Deep learning criminal networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    12. 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.
    13. Mihály Fazekas & Johannes Wachs, 2020. "Corruption and the Network Structure of Public Contracting Markets across Government Change," Politics and Governance, Cogitatio Press, vol. 8(2), pages 153-166.
    14. Fazekas, Mihály & Tóth, Bence & Wachs, Johannes & Abdou, Aly, 2026. "Public procurement cartels: A large-sample testing of screens using machine learning," International Journal of Industrial Organization, Elsevier, vol. 104(C).
    15. Matilde Cappelletti & Leonardo M. Giuffrida & Gabriele Rovigatti, 2024. "Procuring Survival," Journal of Industrial Economics, Wiley Blackwell, vol. 72(4), pages 1451-1506, December.
    16. 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).
    17. David Imhof & Emanuel W Viklund & Martin Huber, 2025. "Catching Bid-rigging Cartels with Graph Attention Neural Networks," Papers 2507.12369, arXiv.org, revised Jul 2025.
    18. Bischi, Gian Italo & Matsumoto, Akio & Carrera, Edgar J. Sanchez, 2020. "Foreword to the SCED special issue on “Nonlinear Social Dynamics”," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 236-237.
    19. 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.
    20. Hannes Wallimann & Silvio Sticher, 2024. "How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection," Papers 2401.14757, arXiv.org.

    More about this item

    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:wly:complx:v:2022:y:2022:i:1:n:5159393. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.