IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p3006-d1187910.html
   My bibliography  Save this article

Multi-Agent Evolutionary Game Analysis of Group Panic Buying in China during the COVID-19 Pandemic

Author

Listed:
  • Xunqing Wang

    (School of Public Administration, Shandong Technology and Business University, Yantai 264005, China)

  • Nan Zhang

    (School of Public Administration, Shandong Technology and Business University, Yantai 264005, China)

  • Hang Zhou

    (School of Public Administration, Shandong Technology and Business University, Yantai 264005, China)

  • Xinpeng Huang

    (School of Public Administration, Shandong Technology and Business University, Yantai 264005, China)

  • Rundong Luo

    (School of Business, Shandong University, Weihai 264209, China)

Abstract

With the global outbreak of COVID-19, the panic-buying incidents triggered by the variants of the Omicron strain have severely affected the normal social order. This paper considers the complex interest game and interactive relationship among multiple subjects in the mass-panic buying event caused by rumors and constructs a three-party evolution game model of local government, rumor-monger, and public. The strategy-selection process of each subject based on evolutionary game theory is studied, and the strategy selection of three game subjects in different situations and related influencing factors are analyzed. Taking the example of the montmorillonite powder panic buying caused by the XBB virus strain rumor in China, the evolutionary game model constructed in this study is simulated and analyzed. The study shows that the evolutionary process of the mass panic-buying event is characterized by six stages: the initial stage; the outbreak stage; the spread stage; the climax stage; the relief stage; and the recovery stage. There are four stable points in the evolutionary game of the three game subjects, namely (no intervention, no rumor, no panic buying), (no intervention, rumor, no panic buying), (intervention, no rumor, no panic buying), and (intervention, rumor, no panic buying). The strategy of government intervention will be adjusted according to the strategy selection of the public and the rumor-monger. Under the mechanism of reward and punishment of the higher-level government, increasing the punishment and reward intensity of the higher-level government will promote the local government to intervene in the rumor-mongering event faster, but increasing the reward intensity has a more significant impact on the intervention behavior of the local government than punishment, and increasing punishment intensity has a more significant impact on the non-rumor-mongering behavior of the rumor-monger than reward. The parameters of social risk-bearing cost, risk transmission coefficient, rumor-mongering income and cost, and public drug purchase cost have different degrees of influence on the evolutionary behavior of game subjects. Therefore, this study provides new ideas for effectively responding to mass panic buying events in the context of public emergencies.

Suggested Citation

  • Xunqing Wang & Nan Zhang & Hang Zhou & Xinpeng Huang & Rundong Luo, 2023. "Multi-Agent Evolutionary Game Analysis of Group Panic Buying in China during the COVID-19 Pandemic," Mathematics, MDPI, vol. 11(13), pages 1-24, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:3006-:d:1187910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/3006/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/13/3006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yaodong Yang & Huaqing Ren & Han Zhang, 2022. "Understanding Consumer Panic Buying Behaviors during the Strict Lockdown on Omicron Variant: A Risk Perception View," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    2. Shan, Haiyan & Pi, Wenjie, 2023. "Mitigating panic buying behavior in the epidemic: An evolutionary game perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    3. Guohua He & Zirun Hu, 2022. "A Model of Panic Buying and Workforce under COVID-19," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    4. Youwei Yuan & Lanying Du & Xiumei Li & Fan Chen, 2022. "An Evolutionary Game Model of the Supply Decisions between GNPOs and Hospitals during a Public Health Emergency," Sustainability, MDPI, vol. 14(3), pages 1-23, January.
    5. Chen, Tinggui & Jin, Yumei & Yang, Jianjun & Cong, Guodong, 2022. "Identifying emergence process of group panic buying behavior under the COVID-19 pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    6. Irineu de Brito Junior & Hugo Tsugunobu Yoshida Yoshizaki & Flaviane Azevedo Saraiva & Nathan de Campos Bruno & Roberto Fray da Silva & Celso Mitsuo Hino & Larissa Limongi Aguiar & Isabella Marrey Fer, 2023. "Panic Buying Behavior Analysis according to Consumer Income and Product Type during COVID-19," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    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. Soltanzadeh, Shima & Rafiee, Majid & Weber, Gerhard-Wilhelm, 2024. "Disruption, panic buying, and pricing: A comprehensive game-theoretic exploration," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

    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. Tinggui Chen & Yumei Jin & Bing Wang & Jianjun Yang, 2024. "The government intervention effects on panic buying behavior based on online comment data mining: a case study of COVID-19 in Hubei Province, China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-20, December.
    2. Ma, Xuan & Yu, Deqing & Wang, Ke, 2024. "Unraveling the intricacies of panic buying: An evolutionary game-theoretic exploration of the evolution and intervention," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Pan, Xiaodan & Dresner, Martin & Li, Guang & Mantin, Benny, 2024. "Stocking up on hand sanitizer: Pandemic lessons for retailers and consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    4. Chenrui Lu & Bing Wang & Tinggui Chen & Jianjun Yang, 2022. "A Document Analysis of Peak Carbon Emissions and Carbon Neutrality Policies Based on a PMC Index Model in China," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    5. Guohua He & Zirun Hu, 2022. "A Model of Panic Buying and Workforce under COVID-19," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    6. Pingping Cao & Jin Zheng & Mingyang Li & Yu Fu, 2023. "A Model for the Assignment of Emergency Rescuers Considering Collaborative Information," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    7. Tinggui Chen & Chenhao Tong & Yuhan Bai & Jianjun Yang & Guodong Cong & Tianluo Cong, 2022. "Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China," Mathematics, MDPI, vol. 10(18), pages 1-27, September.
    8. Liu, Yanfeng & Cai, Lanhui & Ma, Fei & Wang, Xueqin, 2023. "Revenge buying after the lockdown: Based on the SOR framework and TPB model," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Dash, Ganesh & Alharthi, Majed & Albarrak, Mansour & Aggarwal, Shalini, 2024. "Saudi millennials’ panic buying behavior during pandemic and post-pandemic: Role of social media addiction and religious values and commitment," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    10. Meng, Jie & Chen, Kai, 2024. "Rethinking preemptive consumption: Building mechanisms of reciprocity, contextuality, and risk hedging across scenarios," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    11. Soltanzadeh, Shima & Rafiee, Majid & Weber, Gerhard-Wilhelm, 2024. "Disruption, panic buying, and pricing: A comprehensive game-theoretic exploration," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    12. Vinoi, Nivin & Shankar, Amit & Mehrotra, Ankit & Kumar, Jitender & Azad, Nasreen, 2024. "Enablers and inhibitors of digital hoarding behaviour. An application of dual-factor theory and regret theory," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    13. Xinshang You & Shuo Zhao & Yanbo Yang & Dongli Zhang, 2022. "Influence of the Government Department on the Production Capacity Reserve of Emergency Enterprises Based on Multi-Scenario Evolutionary Game," Sustainability, MDPI, vol. 14(23), pages 1-35, November.
    14. Agarwal, Reeti & Mehrotra, Ankit & Pant, Manoj Kumar & Alzeiby, Ebtesam Abdullah & Vishnoi, Sushant Kumar, 2024. "Digital photo hoarding in online retail context. An in-depth qualitative investigation of retail consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    15. Yanmin Ouyang & Haoran Zhao, 2022. "Evolutionary Game Analysis of Collaborative Prevention and Control for Public Health Emergencies," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    16. Guojian Ma & Juan Ding & Youqing Lv, 2022. "SEIR Evolutionary Game Model Applied to the Evolution and Control of the Medical Waste Disposal Crisis in China during the COVID-19 Outbreak," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    17. Daniel, Christopher & Hernandez, Tony, 2024. "What retail apocalypse? A Delphi forecast of commercial space demand in the Toronto region," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    18. Sun, Qi & Ma, Junyong & Lu, Qihui & Gao, Yaya & Xu, Weidong, 2024. "System dynamics analysis of Retailer's emergency strategies when facing irrational demand and supply disruption," International Journal of Production Economics, Elsevier, vol. 271(C).
    19. Di, Kaisheng & Chen, Weidong & Shi, Qiumei & Cai, Quanling & Liu, Sichen, 2024. "Analysing the impact of coupled domestic demand dynamics of green and low-carbon consumption in the market based on SEM-ANN," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    20. Lin, Yuanfang & Pazgal, Amit, 2024. "Effects of information quantity and diversity on consumers under complex uncertainty," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).

    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:jmathe:v:11:y:2023:i:13:p:3006-:d:1187910. 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.