Emergence and stability of social conventions in conflict situations

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    27 Citations (Scopus)

    Abstract

    We investigate the emergence and stability of social conventions for efficiently resolving conflicts through reinforcement learning. Facilitation of coordination and conflict resolution is an important issue in multi-agent systems. However, exhibiting coordinated and negotiation activities is computationally expensive. In this paper, we first describe a conflict situation using a Markov game which is iterated if the agents fail to resolve their conflicts, where the repeated failures result in an inefficient society. Using this game, we show that social conventions for resolving conflicts emerge, but their stability and social efficiency depend on the payoff matrices that characterize the agents. We also examine how unbalanced populations and small heterogeneous agents affect efficiency and stability of the resulting conventions. Our results show that (a) a type of indecisive agent that is generous for adverse results leads to unstable societies, and (b) selfish agents that have an explicit order of benefits make societies stable and efficient.

    Original languageEnglish
    Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
    Pages371-378
    Number of pages8
    DOIs
    Publication statusPublished - 2011
    Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia
    Duration: 2011 Jul 162011 Jul 22

    Other

    Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
    CityBarcelona, Catalonia
    Period11/7/1611/7/22

    Fingerprint

    Reinforcement learning
    Multi agent systems

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Sugawara, T. (2011). Emergence and stability of social conventions in conflict situations. In IJCAI International Joint Conference on Artificial Intelligence (pp. 371-378) https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-071

    Emergence and stability of social conventions in conflict situations. / Sugawara, Toshiharu.

    IJCAI International Joint Conference on Artificial Intelligence. 2011. p. 371-378.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Sugawara, T 2011, Emergence and stability of social conventions in conflict situations. in IJCAI International Joint Conference on Artificial Intelligence. pp. 371-378, 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, Barcelona, Catalonia, 11/7/16. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-071
    Sugawara T. Emergence and stability of social conventions in conflict situations. In IJCAI International Joint Conference on Artificial Intelligence. 2011. p. 371-378 https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-071
    Sugawara, Toshiharu. / Emergence and stability of social conventions in conflict situations. IJCAI International Joint Conference on Artificial Intelligence. 2011. pp. 371-378
    @inproceedings{73cdb4fc39384b6da5ba0dc2025d1452,
    title = "Emergence and stability of social conventions in conflict situations",
    abstract = "We investigate the emergence and stability of social conventions for efficiently resolving conflicts through reinforcement learning. Facilitation of coordination and conflict resolution is an important issue in multi-agent systems. However, exhibiting coordinated and negotiation activities is computationally expensive. In this paper, we first describe a conflict situation using a Markov game which is iterated if the agents fail to resolve their conflicts, where the repeated failures result in an inefficient society. Using this game, we show that social conventions for resolving conflicts emerge, but their stability and social efficiency depend on the payoff matrices that characterize the agents. We also examine how unbalanced populations and small heterogeneous agents affect efficiency and stability of the resulting conventions. Our results show that (a) a type of indecisive agent that is generous for adverse results leads to unstable societies, and (b) selfish agents that have an explicit order of benefits make societies stable and efficient.",
    author = "Toshiharu Sugawara",
    year = "2011",
    doi = "10.5591/978-1-57735-516-8/IJCAI11-071",
    language = "English",
    isbn = "9781577355120",
    pages = "371--378",
    booktitle = "IJCAI International Joint Conference on Artificial Intelligence",

    }

    TY - GEN

    T1 - Emergence and stability of social conventions in conflict situations

    AU - Sugawara, Toshiharu

    PY - 2011

    Y1 - 2011

    N2 - We investigate the emergence and stability of social conventions for efficiently resolving conflicts through reinforcement learning. Facilitation of coordination and conflict resolution is an important issue in multi-agent systems. However, exhibiting coordinated and negotiation activities is computationally expensive. In this paper, we first describe a conflict situation using a Markov game which is iterated if the agents fail to resolve their conflicts, where the repeated failures result in an inefficient society. Using this game, we show that social conventions for resolving conflicts emerge, but their stability and social efficiency depend on the payoff matrices that characterize the agents. We also examine how unbalanced populations and small heterogeneous agents affect efficiency and stability of the resulting conventions. Our results show that (a) a type of indecisive agent that is generous for adverse results leads to unstable societies, and (b) selfish agents that have an explicit order of benefits make societies stable and efficient.

    AB - We investigate the emergence and stability of social conventions for efficiently resolving conflicts through reinforcement learning. Facilitation of coordination and conflict resolution is an important issue in multi-agent systems. However, exhibiting coordinated and negotiation activities is computationally expensive. In this paper, we first describe a conflict situation using a Markov game which is iterated if the agents fail to resolve their conflicts, where the repeated failures result in an inefficient society. Using this game, we show that social conventions for resolving conflicts emerge, but their stability and social efficiency depend on the payoff matrices that characterize the agents. We also examine how unbalanced populations and small heterogeneous agents affect efficiency and stability of the resulting conventions. Our results show that (a) a type of indecisive agent that is generous for adverse results leads to unstable societies, and (b) selfish agents that have an explicit order of benefits make societies stable and efficient.

    UR - http://www.scopus.com/inward/record.url?scp=84881060548&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84881060548&partnerID=8YFLogxK

    U2 - 10.5591/978-1-57735-516-8/IJCAI11-071

    DO - 10.5591/978-1-57735-516-8/IJCAI11-071

    M3 - Conference contribution

    AN - SCOPUS:84881060548

    SN - 9781577355120

    SP - 371

    EP - 378

    BT - IJCAI International Joint Conference on Artificial Intelligence

    ER -