IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p694-d756527.html
   My bibliography  Save this article

Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism

Author

Listed:
  • Ahmed Boujnoui

    (Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
    Computer, Networks, Mobility and Modeling Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco)

  • Abdellah Zaaloul

    (Computer, Networks, Mobility and Modeling Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco
    Engineering, Mathematics and Informatics Laboratory (IMI), Faculty of Sciences, Ibn Zohr University of Agadir, Agadir 86150, Morocco)

  • Luis Orozco-Barbosa

    (Albacete Research Institute of Informatics, Universidad de Castilla-La Mancha, 02071 Albacete, Spain)

  • Abdelkrim Haqiq

    (Computer, Networks, Mobility and Modeling Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco)

Abstract

This paper introduces a general stochastic game analysis of a network scenario consisting of a mix of cooperative and non-cooperative players (i.e., users) under incomplete game information. Users access a shared channel using the Slotted ALOHA mechanism combined with ZigZag Decoding (SAZD). Cooperative players seek to optimize the global utility of the system (e.g., throughput, delay, loss rate) regardless of their individual interests, whereas non-cooperative players act selfishly and optimize their own benefits irrespective of the impact of this behavior on others and on the entire network system. The game equilibrium is characterized by the social optimum and the Nash equilibrium, where the former is adopted by cooperative players and the latter is the equilibrium strategy of non-cooperative players. We undertake a comparative study across two game scenarios with different levels of cooperation and selfishness. Our results generally show that the information possessed by a player can determine the outcome. Furthermore, our findings show that the network performance is strongly influenced by selfish behavior, which can lead to a significant disruption of the entire system. Finally, we show a possible scenario in which the network could greatly benefit from this selfish behavior thanks to the ZigZag scheme.

Suggested Citation

  • Ahmed Boujnoui & Abdellah Zaaloul & Luis Orozco-Barbosa & Abdelkrim Haqiq, 2022. "Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism," Mathematics, MDPI, vol. 10(5), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:694-:d:756527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/694/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/5/694/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Hilbe & Štěpán Šimsa & Krishnendu Chatterjee & Martin A. Nowak, 2018. "Evolution of cooperation in stochastic games," Nature, Nature, vol. 559(7713), pages 246-249, July.
    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. Liu, Fanglin & Wu, Bin, 2022. "Environmental quality and population welfare in Markovian eco-evolutionary dynamics," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    2. Ziyi Chen & Kaiyan Dai & Xing Jin & Liqin Hu & Yongheng Wang, 2023. "Aspiration-Based Learning in k -Hop Best-Shot Binary Networked Public Goods Games," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    3. Michael Foley & Rory Smead & Patrick Forber & Christoph Riedl, 2021. "Avoiding the bullies: The resilience of cooperation among unequals," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-18, April.
    4. Xu Chen & Xuan Di & Zechu Li, 2023. "Social Learning for Sequential Driving Dilemmas," Games, MDPI, vol. 14(3), pages 1-12, May.
    5. Huang, Changwei & Luo, Yijun & Han, Wenchen, 2023. "Cooperation and synchronization in evolutionary opinion changing rate games," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    6. Richard J Arend, 2020. "The expected prisoner’s dilemma – With rationally arising cooperation," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-6, September.
    7. Han, Ying & Song, Zhao & Sun, Jialong & Ma, Jiezhong & Guo, Yangming & Zhu, Peican, 2020. "Investing the effect of age and cooperation in spatial multigame," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Pengrui Wang & Chen Zeng & Yan Song & Long Guo & Wenping Liu & Wenting Zhang, 2021. "The Spatial Effect of Administrative Division on Land-Use Intensity," Land, MDPI, vol. 10(5), pages 1-18, May.
    9. Maria Kleshnina & Christian Hilbe & Štěpán Šimsa & Krishnendu Chatterjee & Martin A. Nowak, 2023. "The effect of environmental information on evolution of cooperation in stochastic games," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Li, Bin-Quan & Wu, Zhi-Xi & Guan, Jian-Yue, 2022. "Critical thresholds of benefit distribution in an extended snowdrift game model," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    11. Cheng, Jiangjiang & Mei, Wenjun & Su, Wei & Chen, Ge, 2023. "Evolutionary games on networks: Phase transition, quasi-equilibrium, and mathematical principles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    12. Lu, Shounan & Zhu, Ge & Zhang, Lianzhong, 2023. "Antisocial behavior-based environmental feedback in spatial prisoner's dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    13. Wang, Jianwei & Xu, Wenshu & Zhang, Xingjian & Zhao, Nianxuan & Yu, Fengyuan, 2023. "Redistribution based on willingness to cooperate promotes cooperation while intensifying equality in heterogeneous populations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    14. Li, Cong & Lessard, Sabin, 2020. "Randomized matrix games in a finite population: Effect of stochastic fluctuations in the payoffs on the evolution of cooperation," Theoretical Population Biology, Elsevier, vol. 134(C), pages 77-91.
    15. Guan, Junbiao & Wang, Kaihua, 2020. "Cooperative evolution in pedestrian room evacuation considering different individual behaviors," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    16. Han, Weiwei & Zhang, Zhipeng & Sun, Junqing & Xia, Chengyi, 2022. "Role of reputation constraints in the spatial public goods game with second-order reputation evaluation," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    17. Han, Xu & Zhao, Xiaowei & Xia, Haoxiang, 2022. "Hybrid learning promotes cooperation in the spatial prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    18. Bednarik, Peter & Linnerooth-Bayer, Joanne & Magnuszewski, Piotr & Dieckmann, Ulf, 2019. "A Game of Common-pool Resource Management: Effects of Communication, Risky Environment and Worldviews," Ecological Economics, Elsevier, vol. 156(C), pages 287-292.
    19. He, Jialu & Wang, Jianwei & Yu, Fengyuan & Chen, Wei & Xu, Wenshu, 2022. "The persistence and transition of multiple public goods games resolves the social dilemma," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    20. Chengyi Tu & Renfei Chen & Ying Fan & Yongliang Yang, 2024. "Coevolution of Resource and Strategies in Common-Pool Resource Dilemmas: A Coupled Human-Environmental System Model," Papers 2401.11269, arXiv.org.

    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:10:y:2022:i:5:p:694-:d:756527. 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.