IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v297y2017icp1-7.html
   My bibliography  Save this article

Selection intensity and risk-dominant strategy: A two-strategy stochastic evolutionary game dynamics in finite population

Author

Listed:
  • Yu, Jie-Ru
  • Liu, Xue-Lu
  • Zheng, Xiu-Deng
  • Tao, Yi

Abstract

Stochastic evolutionary game dynamics with weak selection in finite population has been studied and it has been used to explain the emergence of cooperation. In this paper, following the previous studies, the diffusion approximation of a two-strategy stochastic evolutionary game dynamics in finite population that includes a small mutation rate between two strategies is investigated, where we assume that these two strategies are both strict Nash equilibrium (NE). Our main goal is to partially reveal the effect of selection intensity on the stochastic evolutionary game dynamics. Through the analysis of potential function of the stationary distribution, our main result shows that for all possible situations with that the selection intensity is not zero (that includes the strong selection), if a strategy is a risk-dominant NE, then its expected fitness with respect to the stationary distribution must be larger than that of other strategy. This result not only extends the previous results but also provides some useful insights for understanding the significance of selection intensity in stochastic evolutionary game dynamics in finite population.

Suggested Citation

  • Yu, Jie-Ru & Liu, Xue-Lu & Zheng, Xiu-Deng & Tao, Yi, 2017. "Selection intensity and risk-dominant strategy: A two-strategy stochastic evolutionary game dynamics in finite population," Applied Mathematics and Computation, Elsevier, vol. 297(C), pages 1-7.
    • Handle: RePEc:eee:apmaco:v:297:y:2017:i:c:p:1-7
    DOI: 10.1016/j.amc.2016.10.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300316306464
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2016.10.039?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. Xiudeng Zheng & Ross Cressman & Yi Tao, 2011. "The Diffusion Approximation of Stochastic Evolutionary Game Dynamics: Mean Effective Fixation Time and the Significance of the One-Third Law," Dynamic Games and Applications, Springer, vol. 1(3), pages 462-477, September.
    2. Ken Binmore & Larry Samuelson & Petyon Young, 2003. "Equilibrium Selection in Bargaining Models," Levine's Bibliography 506439000000000466, UCLA Department of Economics.
    3. Martin A. Nowak & Akira Sasaki & Christine Taylor & Drew Fudenberg, 2004. "Emergence of cooperation and evolutionary stability in finite populations," Nature, Nature, vol. 428(6983), pages 646-650, April.
    4. Binmore, Ken & Samuelson, Larry & Young, Peyton, 2003. "Equilibrium selection in bargaining models," Games and Economic Behavior, Elsevier, vol. 45(2), pages 296-328, November.
    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. Jun Du & Jiejie Li & Jiaxin Li & Weiduo Li, 2023. "Competition–cooperation mechanism of online supply chain finance based on a stochastic evolutionary game," Operational Research, Springer, vol. 23(3), pages 1-28, September.
    2. Lin, XuXun & Yuan, PengCheng, 2018. "A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1096-1110.

    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. Xiudeng Zheng & Ross Cressman & Yi Tao, 2011. "The Diffusion Approximation of Stochastic Evolutionary Game Dynamics: Mean Effective Fixation Time and the Significance of the One-Third Law," Dynamic Games and Applications, Springer, vol. 1(3), pages 462-477, September.
    2. Dai, Darong & Shen, Kunrong, 2012. "A New Stationary Game Equilibrium Induced by Stochastic Group Evolution and Rational Individual Choice," MPRA Paper 40586, University Library of Munich, Germany, revised 09 Aug 2012.
    3. Nax, Heinrich H., 2015. "Equity dynamics in bargaining without information exchange," LSE Research Online Documents on Economics 65426, London School of Economics and Political Science, LSE Library.
    4. Heinrich Nax, 2015. "Equity dynamics in bargaining without information exchange," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 1011-1026, November.
    5. H Peyton Young, 2014. "The Evolution of Social Norms," Economics Series Working Papers 726, University of Oxford, Department of Economics.
    6. Bolle Friedel & Otto Philipp E., 2016. "Matching as a Stochastic Process," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 323-348, May.
    7. Hwang, Sung-Ha & Rey-Bellet, Luc, 2021. "Positive feedback in coordination games: Stochastic evolutionary dynamics and the logit choice rule," Games and Economic Behavior, Elsevier, vol. 126(C), pages 355-373.
    8. Hwang, Sung-Ha & Lim, Wooyoung & Neary, Philip & Newton, Jonathan, 2018. "Conventional contracts, intentional behavior and logit choice: Equality without symmetry," Games and Economic Behavior, Elsevier, vol. 110(C), pages 273-294.
    9. BOCHET, Olivier & KLAUS, Bettina & WALZL, Markus, 2007. "Dynamic recontracting processes with multiple indivisible goods," LIDAM Discussion Papers CORE 2007061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Daniel Wood, 2015. "Informal property rights as stable conventions in hawk-dove games with many players," Journal of Evolutionary Economics, Springer, vol. 25(4), pages 849-873, September.
    11. Yoshio Kamijo & Koji Yokote, 2022. "Behavioral bargaining theory: Equality bias, risk attitude, and reference-dependent utility," Working Papers 2208, Waseda University, Faculty of Political Science and Economics.
    12. Klaus, Bettina & Bochet, Olivier & Walzl, Markus, 2011. "A dynamic recontracting process for multiple-type housing markets," Journal of Mathematical Economics, Elsevier, vol. 47(1), pages 84-98, January.
    13. Kevin Hasker, 2014. "The Emergent Seed: A Representation Theorem for Models of Stochastic Evolution and two formulas for Waiting Time," Levine's Working Paper Archive 786969000000000954, David K. Levine.
    14. Julian J. Arevalo, 2005. "Gradual Nash Bargaining with Endogenous Agenda: A Path-Dependent Model," Game Theory and Information 0502004, University Library of Munich, Germany.
    15. Ross Cressman, 2009. "Continuously stable strategies, neighborhood superiority and two-player games with continuous strategy space," International Journal of Game Theory, Springer;Game Theory Society, vol. 38(2), pages 221-247, June.
    16. Newton, Jonathan, 2015. "Stochastic stability on general state spaces," Journal of Mathematical Economics, Elsevier, vol. 58(C), pages 46-60.
    17. Feng, Tian-Jiao & Fan, Song-Jia & Li, Cong & Tao, Yi & Zheng, Xiu-Deng, 2023. "Noise-induced sustainability of cooperation in Prisoner's Dilemma game," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    18. Sawa, Ryoji, 2021. "A prospect theory Nash bargaining solution and its stochastic stability," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 692-711.
    19. Andreozzi, Luciano, 2013. "Learning to be fair," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 181-195.
    20. Arnald J. Kanning, 2020. "Agreement by conduct as a coordination device," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 19(1), pages 77-90, June.

    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:eee:apmaco:v:297:y:2017:i:c:p:1-7. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.