IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v44y2023i4p2094-2101.html

Evolutionary game analysis of big data discriminatory pricing diffusion based on the supervision of relevant interest parties

Author

Listed:
  • JiWen Chai
  • LiHao Wang

Abstract

The use of big data technology is significant for the market competition of two‐side enterprises. However, big data technology is likely to become a tool for discriminatory pricing, causing damage to the benefit‐relevant parties. This paper constructs a four party evolutionary game model between two‐side enterprises, governments, suppliers, and consumers. Lotka–Volterra model is introduced to explore the evolutionary impact of the supervisory behaviors of the benefit‐relevant parties on the diffusion of big data discriminatory pricing (BDDP) in two‐side enterprises. Using MATLAB simulation tools, the evolution game and diffusion evolution model are mathematically deduced, and the analogue simulation is used for correlation analysis. This paper can help two‐side enterprises, governments, suppliers, and consumers better understand the diffusion law of BDDP and more accurately predict the development of it. At the same time, it provides a quantitative theoretical basis for the reasonable decision‐making of governments, suppliers, and consumers.

Suggested Citation

  • JiWen Chai & LiHao Wang, 2023. "Evolutionary game analysis of big data discriminatory pricing diffusion based on the supervision of relevant interest parties," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2094-2101, June.
    • Handle: RePEc:wly:mgtdec:v:44:y:2023:i:4:p:2094-2101
    DOI: 10.1002/mde.3803
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/mde.3803
    Download Restriction: no
    File URL: https://libkey.io/10.1002/mde.3803?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. Anastasopoulos, Nikolaos P. & Anastasopoulos, Markos P., 2012. "The evolutionary dynamics of audit," European Journal of Operational Research, Elsevier, vol. 216(2), pages 469-476.
    2. R. Edward Freeman & S. Ramakrishna Velamuri, 2006. "A New Approach to CSR: Company Stakeholder Responsibility," Palgrave Macmillan Books, in: Andrew Kakabadse & Mette Morsing (ed.), Corporate Social Responsibility, chapter 1, pages 9-23, Palgrave Macmillan.
    3. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    4. Liu, Weihua & Long, Shangsong & Xie, Dong & Liang, Yanjie & Wang, Jinkun, 2021. "How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model," International Journal of Production Economics, Elsevier, vol. 231(C).
    5. Jennifer F. Reinganum, 1981. "Market Structure and the Diffusion of New Technology," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 618-624, Autumn.
    6. Zhang, Suyong & Wang, Chuanxu & Yu, Chao, 2019. "The evolutionary game analysis and simulation with system dynamics of manufacturer's emissions abatement behavior under cap-and-trade regulation," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 343-355.
    7. Rahman, Atiqur & Loulou, Richard, 2001. "Technology acquisition with technological progress: effects of expectations, rivalry and uncertainty," European Journal of Operational Research, Elsevier, vol. 129(1), pages 159-185, February.
    8. Richard A. Jensen, 2004. "Multiplant Firms and Innovation Adoption and Diffusion," Southern Economic Journal, John Wiley & Sons, vol. 70(3), pages 661-671, January.
    9. Encarnação, Sara & Santos, Fernando P. & Santos, Francisco C. & Blass, Vered & Pacheco, Jorge M. & Portugali, Juval, 2018. "Paths to the adoption of electric vehicles: An evolutionary game theoretical approach," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 24-33.
    10. Dutta, S. & Sarmah, S.P. & Goyal, S.K., 2010. "Evolutionary stability of auction and supply chain contracting: An analysis based on disintermediation in the Indian tea supply chains," European Journal of Operational Research, Elsevier, vol. 207(1), pages 531-538, November.
    11. Curtis R. Taylor, 2004. "Consumer Privacy and the Market for Customer Information," RAND Journal of Economics, The RAND Corporation, vol. 35(4), pages 631-650, Winter.
    12. Il-Horn Hann & Kai-Lung Hui & Sang-Yong T. Lee & Ivan P. L. Png, 2008. "Consumer Privacy and Marketing Avoidance: A Static Model," Management Science, INFORMS, vol. 54(6), pages 1094-1103, June.
    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. Nan Wang & Yu Chang & Xiao Yu Song, 2025. "Simulation Analysis of Big Data Discriminatory Pricing Behavior from the Perspective of Game Theory," SAGE Open, , vol. 15(1), pages 21582440241, January.
    2. Shandong Mou & Kexin Zhong & Yamin Ma, 2023. "Regulating the Big Data-Based Discriminatory Pricing in Platform Retailing: A Tripartite Evolutionary Game Theory Analysis," Mathematics, MDPI, vol. 11(11), pages 1-21, June.
    3. Zhen Xu & Shudan Zheng, 2024. "An evolutionary game-theoretic analysis of the "multi-agent co-governance" system of unfair competition on internet platforms," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-29, June.

    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. Liu, Weihua & Long, Shangsong & Xie, Dong & Liang, Yanjie & Wang, Jinkun, 2021. "How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Shandong Mou & Kexin Zhong & Yamin Ma, 2023. "Regulating the Big Data-Based Discriminatory Pricing in Platform Retailing: A Tripartite Evolutionary Game Theory Analysis," Mathematics, MDPI, vol. 11(11), pages 1-21, June.
    3. Zhaoli Li & Yifan Xu, 2026. "Information sharing under consumer privacy protection in an online marketplace," Annals of Operations Research, Springer, vol. 356(2), pages 985-1020, January.
    4. Kesler, Reinhold & Kummer, Michael E. & Schulte, Patrick, 2017. "Mobile applications and access to private data: The supply side of the Android ecosystem," ZEW Discussion Papers 17-075, ZEW - Leibniz Centre for European Economic Research.
    5. Huang, Xingjun & Lin, Yun & Lim, Ming K. & Zhou, Fuli & Ding, Rui & Zhang, Zusheng, 2022. "Evolutionary dynamics of promoting electric vehicle-charging infrastructure based on public–private partnership cooperation," Energy, Elsevier, vol. 239(PD).
    6. Michael Kummer & Patrick Schulte, 2019. "When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications," Management Science, INFORMS, vol. 65(8), pages 3470-3494, August.
    7. Yongrui Duan & Yuncong Xie & Yihong Hu, 2023. "The value of personal information in vertically differentiated markets with privacy concerns," Annals of Operations Research, Springer, vol. 329(1), pages 425-469, October.
    8. Spiegel, Yossi, 2013. "Commercial software, adware, and consumer privacy," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 702-713.
    9. Philipp Dimakopoulos & Slobodan Sudaric, 2017. "Privacy and Platform Competition," Working Papers 2017003, Berlin Doctoral Program in Economics and Management Science (BDPEMS).
    10. Tommaso Bondi & Omid Rafieian & Yunfei (Jesse) Yao, 2026. "Privacy and Polarization: An Inference-Based Framework," Management Science, INFORMS, vol. 72(2), pages 1389-1409, February.
    11. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    12. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    13. Yao, Xusheng & Shao, Zhiqi & Wang, Ze & Zhu, Zhu & Chen, Zuanxu & Wu, Qingyang, 2025. "Policy incentives and market mechanisms dual-driven framework for new energy vehicles promotion," Energy Policy, Elsevier, vol. 199(C).
    14. Didier Laussel & Ngo Van Long & Joana Resende, 2023. "Profit Effects of Consumers’ Identity Management: A Dynamic Model," Management Science, INFORMS, vol. 69(6), pages 3602-3615, June.
    15. Lynne Pepall & Daniel Richards, 2021. "Targeted Value-Enhancing Advertising and Price Competition," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 59(3), pages 443-459, November.
    16. Bó, Inácio & Chen, Li & Hakimov, Rustamdjan, 2024. "Strategic responses to personalized pricing and demand for privacy: An experiment," Games and Economic Behavior, Elsevier, vol. 148(C), pages 487-516.
    17. Li, Jianpei & Zhang, Wanzhu, 2022. "Behavior-based pricing and signaling of product quality," MPRA Paper 120263, University Library of Munich, Germany, revised 03 Jan 2023.
    18. Bouckaert, J.M.C. & Degryse, H.A., 2006. "Opt In versus Opt Out : A Free-Entry Analysis of Privacy Policies," Discussion Paper 2006-96, Tilburg University, Center for Economic Research.
    19. Skreta, Vasiliki & Doval, Laura, 2021. "Purchase history and product personalization," CEPR Discussion Papers 15969, Centre for Economic Policy Research.
    20. Liu, Dehai & Xiao, Xingzhi & Li, Hongyi & Wang, Weiguo, 2015. "Historical evolution and benefit–cost explanation of periodical fluctuation in coal mine safety supervision: An evolutionary game analysis framework," European Journal of Operational Research, Elsevier, vol. 243(3), pages 974-984.

    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:mgtdec:v:44:y:2023:i:4:p:2094-2101. 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: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    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.