IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v1y2013i1p22-37n2.html
   My bibliography  Save this article

Transformation of Characteristic Function in Dynamic Games

Author

Listed:
  • Gao Hongwei

    (College of Mathematics, Qingdao University, Qingdao, 266071, China)

  • Petrosyan Leon
  • Sedakov Artem

    (Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Saint Petersburg, 198504, Russia)

  • Qiao Han

    (Management School, University of Chinese Academy of Sciences, Beijing, 100190, China)

  • Xu Genjiu

    (Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, 710072, China)

Abstract

The problem of transformation of characteristic function, using a transformation matrix and connected with associated consistency of a solution concept, was investigated for the class of so-called “one-shot” games. The same problem also arises in dynamic games when players move step by step along the cooperative trajectory. In this case the evolution of characteristic function is, in general, unpredictable and may lead to time-inconsistency of a fixed cooperative solution concept. There are different approaches to overcome this problem based on transformation of characteristic function. In this paper, the step-wise transformation is used, which is a generalization of similar transformation of characteristic function for dynamic games with perfect information, and it leads to the time-consistent solution. The general form of such transformation is proposed.

Suggested Citation

  • Gao Hongwei & Petrosyan Leon & Sedakov Artem & Qiao Han & Xu Genjiu, 2013. "Transformation of Characteristic Function in Dynamic Games," Journal of Systems Science and Information, De Gruyter, vol. 1(1), pages 22-37, February.
    • Handle: RePEc:bpj:jossai:v:1:y:2013:i:1:p:22-37:n:2
    DOI: 10.1515/JSSI-2013-0022
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/JSSI-2013-0022
    Download Restriction: no
    File URL: https://libkey.io/10.1515/JSSI-2013-0022?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. Gérard Hamiache, 2001. "Associated consistency and Shapley value," International Journal of Game Theory, Springer;Game Theory Society, vol. 30(2), pages 279-289.
    2. Andrea Galeotti & Sanjeev Goyal, 2010. "The Law of the Few," American Economic Review, American Economic Association, vol. 100(4), pages 1468-1492, September.
    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. Arthur Schram & Boris Van Leeuwen & Theo Offerman, 2013. "Superstars Need Social Benefits: An Experiment on Network Formation," Working Papers 1306, Departament Empresa, Universitat Autònoma de Barcelona, revised Jul 2013.
    2. Eunae Yoo & Elliot Rabinovich & Bin Gu, 2020. "The Growth of Follower Networks on Social Media Platforms for Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2696-2715, December.
    3. Markus Kinateder & Luca Paolo Merlino, 2021. "The Evolution of Networks and Local Public Good Provision: A Potential Approach," Games, MDPI, vol. 12(3), pages 1-12, July.
    4. Luca Dall’Asta & Paolo Pin & Abolfazl Ramezanpour, 2011. "Optimal Equilibria of the Best Shot Game," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 13(6), pages 885-901, December.
    5. Allouch, Nizar, 2017. "The cost of segregation in (social) networks," Games and Economic Behavior, Elsevier, vol. 106(C), pages 329-342.
    6. Liu, Xiaodong & Patacchini, Eleonora & Zenou, Yves & Lee, Lung-Fei, 2011. "Criminal Networks: Who is the Key Player?," Research Papers in Economics 2011:7, Stockholm University, Department of Economics.
    7. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    8. Gaudeul, Alexia & Giannetti, Caterina, 2011. "The role of reciprocation in social network formation, with an application to blogging," MPRA Paper 34094, University Library of Munich, Germany.
    9. Nora, Vladyslav & Winter, Eyal, 2024. "Exploiting social influence in networks," Theoretical Economics, Econometric Society, vol. 19(1), January.
    10. Péter Bayer & Ani Guerdjikova, 2020. "Optimism leads to optimality: Ambiguity in network formation," Working Papers hal-03005107, HAL.
    11. in 't Veld, Daan & van der Leij, Marco & Hommes, Cars, 2020. "The formation of a core-periphery structure in heterogeneous financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    12. Kenan Huremović, 2021. "A noncooperative model of contest network formation," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(2), pages 275-317, April.
    13. Ying Chen & Tom Lane & Stuart McDonald, 2023. "Endogenous Network Formation in Local Public Goods: An Experimental Analysis," Discussion Papers 2023-02, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    14. Bolletta, Ugo, 2021. "A model of peer effects in school," Mathematical Social Sciences, Elsevier, vol. 114(C), pages 1-10.
    15. Pascal Billand & Christophe Bravard & Sudipta Sarangi & J. Kamphorst, 2011. "Confirming information flows in networks," Post-Print halshs-00672351, HAL.
    16. Bayer, Péter & Herings, P. Jean-Jacques & Peeters, Ronald, 2021. "Farsighted manipulation and exploitation in networks," Journal of Economic Theory, Elsevier, vol. 196(C).
    17. Choi, S. & Goyal, G. & Moisan, F., 2020. "Large Scale Experiments on Networks: A New Platform with Applications," Cambridge Working Papers in Economics 2063, Faculty of Economics, University of Cambridge.
    18. Rong, Rong & Houser, Daniel, 2015. "Growing stars: A laboratory analysis of network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 380-394.
    19. Liu, Jia & Sonntag, Axel & Zizzo, Daniel John, 2022. "Information defaults in repeated public good provision," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 356-369.
    20. Tolga Akcura & Kemal Altinkemer & Hailiang Chen, 0. "Noninfluentials and information dissemination in the microblogging community," Information Technology and Management, Springer, vol. 0, pages 1-18.

    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:bpj:jossai:v:1:y:2013:i:1:p:22-37:n:2. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.