IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v64y2024i4d10.1007_s10614-023-10524-x.html
   My bibliography  Save this article

An Enterprise Multi-agent Model with Game Q-Learning Based on a Single Decision Factor

Author

Listed:
  • Siying Xu

    (Shanghai University of Finance and Economics
    Shanghai Lixin University of Accounting and Finance)

  • Gaoyu Zhang

    (Shanghai Lixin University of Accounting and Finance)

  • Xianzhi Yuan

    (Chengdu University)

Abstract

In recent years, the study of enterprise survival development and cooperation in the whole economic market has been rapidly developed. However, in most literature studies, the traditional enterprise multi-agent cannot effectively simulate the process of enterprise survival and development since the fundamental characteristics used to describe enterprises in social networks, such as the values of enterprise multi-agent attributes, cannot be changed in process of the simulation. To address this problem, an enterprise multi-agent model based on game Q- learning to simulate enterprise decision making which aims to maximize the benefits of enterprises and optimize the effect of inter-firm cooperation is proposed in this article. The Firm Q Learning algorithm is used to dynamically change the attribute values of the enterprise multi-agent to optimize the game results in the evolutionary game model and thus effectively simulate the dynamic cooperation among the enterprise agents. The simulation result shows that the evolution of the enterprise multi-agent model based on game Q-learning can more realistically reflect the process of real enterprise survival and development than the multi-agent simulation with fixed attribute values.

Suggested Citation

  • Siying Xu & Gaoyu Zhang & Xianzhi Yuan, 2024. "An Enterprise Multi-agent Model with Game Q-Learning Based on a Single Decision Factor," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2523-2562, October.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:4:d:10.1007_s10614-023-10524-x
    DOI: 10.1007/s10614-023-10524-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10524-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10524-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    2. He, Yi & Wang, Hang & Guo, Qiang & Xu, Qingyun, 2019. "Coordination through cooperative advertising in a two-period consumer electronics supply chain," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 179-188.
    3. Marino, Marianna & Parrotta, Pierpaolo & Pozzoli, Dario, 2016. "Educational diversity and knowledge transfers via inter-firm labor mobility," Journal of Economic Behavior & Organization, Elsevier, vol. 123(C), pages 168-183.
    4. Yang, Rui & Tang, Wansheng & Zhang, Jianxiong, 2021. "Technology improvement strategy for green products under competition: The role of government subsidy," European Journal of Operational Research, Elsevier, vol. 289(2), pages 553-568.
    5. Kaya, Orcun, 2022. "Determinants and consequences of SME insolvency risk during the pandemic," Economic Modelling, Elsevier, vol. 115(C).
    6. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    7. Friedman, Daniel & Sinervo, Barry, 2016. "Evolutionary Games in Natural, Social, and Virtual Worlds," OUP Catalogue, Oxford University Press, number 9780199981151.
    8. Coninx, Kristof & Deconinck, Geert & Holvoet, Tom, 2018. "Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics," Applied Energy, Elsevier, vol. 218(C), pages 104-113.
    9. Ding, Huiping & Guo, Baochun & Liu, Zhishuo, 2011. "Information sharing and profit allotment based on supply chain cooperation," International Journal of Production Economics, Elsevier, vol. 133(1), pages 70-79, September.
    10. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    11. Liu, Jia-Cai & Sheu, Jiuh-Biing & Li, Deng-Feng & Dai, Yong-Wu, 2021. "Collaborative profit allocation schemes for logistics enterprise coalitions with incomplete information," Omega, Elsevier, vol. 101(C).
    12. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    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. Sandro Sousa & Vincenzo Nicosia, 2022. "Quantifying ethnic segregation in cities through random walks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    3. Nicole Tabasso, 2014. "Diffusion of Multiple Information," School of Economics Discussion Papers 0914, School of Economics, University of Surrey.
    4. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    5. Ma, Peng, 2021. "Optimal generic and brand advertising efforts in a decentralized supply chain considering customer surplus," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    6. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    7. Simpson Zhang & Mihaela van der Schaar, 2018. "Reputational Dynamics in Financial Networks During a Crisis," Working Papers 18-03, Office of Financial Research, US Department of the Treasury.
    8. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    9. Timothy G. Conley & Nirav Mehta & Ralph Stinebrickner & Todd Stinebrickner, 2024. "Social Interactions, Mechanisms, and Equilibrium: Evidence from a Model of Study Time and Academic Achievement," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 824-866.
    10. Allouch, Nizar, 2017. "The cost of segregation in (social) networks," Games and Economic Behavior, Elsevier, vol. 106(C), pages 329-342.
    11. Wakiyama, Takako & Zusman, Eric, 2021. "The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    12. Bernd (B.) Heidergott & Jia-Ping Huang & Ines (I.) Lindner, 2018. "Naive Learning in Social Networks with Random Communication," Tinbergen Institute Discussion Papers 18-018/II, Tinbergen Institute.
    13. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    14. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    15. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    16. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    17. Xiaofeng Xu & Xiangyu Chen & Yi Xu & Tao Wang & Yifan Zhang, 2022. "Improving the Innovative Performance of Renewable Energy Enterprises in China: Effects of Subsidy Policy and Intellectual Property Legislation," Sustainability, MDPI, vol. 14(13), pages 1-24, July.
    18. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    19. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    20. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.

    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:kap:compec:v:64:y:2024:i:4:d:10.1007_s10614-023-10524-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.