IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i1p99-d474901.html
   My bibliography  Save this article

Characterizations of Pareto-Nash Equilibria for Multiobjective Potential Population Games

Author

Listed:
  • Guanghui Yang

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

  • Chanchan Li

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

  • Jinxiu Pi

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

  • Chun Wang

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

  • Wenjun Wu

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

  • Hui Yang

    (School of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou 550025, China)

Abstract

This paper studies the characterizations of (weakly) Pareto-Nash equilibria for multiobjective population games with a vector-valued potential function called multiobjective potential population games, where agents synchronously maximize multiobjective functions with finite strategies via a partial order on the criteria-function set. In such games, multiobjective payoff functions are equal to the transpose of the Jacobi matrix of its potential function. For multiobjective potential population games, based on Kuhn-Tucker conditions of multiobjective optimization, a strongly (weakly) Kuhn-Tucker state is introduced for its vector-valued potential function and it is proven that each strongly (weakly) Kuhn-Tucker state is one (weakly) Pareto-Nash equilibrium. The converse is obtained for multiobjective potential population games with two strategies by utilizing Tucker’s Theorem of the alternative and Motzkin’s one of linear systems. Precisely, each (weakly) Pareto-Nash equilibrium is equivalent to a strongly (weakly) Kuhn-Tucker state for multiobjective potential population games with two strategies. These characterizations by a vector-valued approach are more comprehensive than an additive weighted method. Multiobjective potential population games are the extension of population potential games from a single objective to multiobjective cases. These novel results provide a theoretical basis for further computing (weakly) Pareto-Nash equilibria of multiobjective potential population games and their practical applications.

Suggested Citation

  • Guanghui Yang & Chanchan Li & Jinxiu Pi & Chun Wang & Wenjun Wu & Hui Yang, 2021. "Characterizations of Pareto-Nash Equilibria for Multiobjective Potential Population Games," Mathematics, MDPI, vol. 9(1), pages 1-13, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:1:p:99-:d:474901
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/1/99/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/1/99/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kukushkin, Nikolai S., 2018. "Better response dynamics and Nash equilibrium in discontinuous games," Journal of Mathematical Economics, Elsevier, vol. 74(C), pages 68-78.
    2. Sandholm, William H., 2001. "Potential Games with Continuous Player Sets," Journal of Economic Theory, Elsevier, vol. 97(1), pages 81-108, March.
    3. Sandholm, William H., 2009. "Large population potential games," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1710-1725, July.
    4. Ratul Lahkar, 2017. "Large Population Aggregative Potential Games," Dynamic Games and Applications, Springer, vol. 7(3), pages 443-467, September.
    5. Christensen, Finn, 2017. "A necessary and sufficient condition for a unique maximum with an application to potential games," Economics Letters, Elsevier, vol. 161(C), pages 120-123.
    6. Cheung, Man-Wah & Lahkar, Ratul, 2018. "Nonatomic potential games: the continuous strategy case," Games and Economic Behavior, Elsevier, vol. 108(C), pages 341-362.
    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. Lahkar, Ratul, 2019. "Elimination of non-individualistic preferences in large population aggregative games," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 150-165.
    2. Ratul Lahkar & Vinay Ramani, 2022. "An Evolutionary Approach to Pollution Control in Competitive Markets," Dynamic Games and Applications, Springer, vol. 12(3), pages 872-896, September.
    3. Ratul Lahkar, 2020. "Convergence to Walrasian equilibrium with minimal information," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(3), pages 553-578, July.
    4. Sarvesh Bandhu & Ratul Lahkar, 2021. "Implementation in Large Population Games with Multiple Equilibria," Working Papers 62, Ashoka University, Department of Economics.
    5. Ratul Lahkar & Rezina Sultana, 2020. "Affirmative Action in Large Population Contests," Working Papers 40, Ashoka University, Department of Economics.
    6. Ratul Lahkar & Vinay Ramani, 2021. "An Evolutionary Approach to Pollution Control in Competitive Markets," Working Papers 68, Ashoka University, Department of Economics.
    7. Lahkar, Ratul & Mukherjee, Saptarshi, 2021. "Evolutionary implementation in aggregative games," Mathematical Social Sciences, Elsevier, vol. 109(C), pages 137-151.
    8. Lahkar, Ratul & Mukherjee, Saptarshi, 2019. "Evolutionary implementation in a public goods game," Journal of Economic Theory, Elsevier, vol. 181(C), pages 423-460.
    9. Sarvesh Bandhu & Ratul Lahkar, 2023. "Evolutionary robustness of dominant strategy implementation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(2), pages 685-721, August.
    10. Osawa, Minoru & Akamatsu, Takashi, 2020. "Equilibrium refinement for a model of non-monocentric internal structures of cities: A potential game approach," Journal of Economic Theory, Elsevier, vol. 187(C).
    11. Christian Ewerhart, 2020. "Ordinal potentials in smooth games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 70(4), pages 1069-1100, November.
    12. Lahkar, Ratul & Mukherjee, Sayan & Roy, Souvik, 2022. "Generalized perturbed best response dynamics with a continuum of strategies," Journal of Economic Theory, Elsevier, vol. 200(C).
    13. Hofbauer, Josef & Sandholm, William H., 2009. "Stable games and their dynamics," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1665-1693.4, July.
    14. William Sandholm, 2014. "Probabilistic Interpretations of Integrability for Game Dynamics," Dynamic Games and Applications, Springer, vol. 4(1), pages 95-106, March.
    15. Osawa, Minoru & Akamatsu, Takashi, 2019. "Emergence of Urban Landscapes: Equilibrium Selection in a Model of Internal Structure of the Cities," MPRA Paper 92395, University Library of Munich, Germany.
    16. Ratul Lahkar, 2017. "Large Population Aggregative Potential Games," Dynamic Games and Applications, Springer, vol. 7(3), pages 443-467, September.
    17. Oyama, Daisuke, 2009. "Agglomeration under forward-looking expectations: Potentials and global stability," Regional Science and Urban Economics, Elsevier, vol. 39(6), pages 696-713, November.
    18. Cheung, Man-Wah & Lahkar, Ratul, 2018. "Nonatomic potential games: the continuous strategy case," Games and Economic Behavior, Elsevier, vol. 108(C), pages 341-362.
    19. Minoru Osawa & Takashi Akamatsu & Yosuke Kogure, 2020. "Stochastic stability of agglomeration patterns in an urban retail model," Papers 2011.06778, arXiv.org.
    20. Patrick Maillé & Peter Reichl & Bruno Tuffin, 2011. "Interplay between security providers, consumers, and attackers: a weighted congestion game approach," Post-Print inria-00560807, HAL.

    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:9:y:2021:i:1:p:99-:d:474901. 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.