Emergence and stability of social conventions in conflict situations

Toshiharu Sugawara*

*この研究の対応する著者

研究成果

28 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
ページ371-378
ページ数8
DOI
出版ステータスPublished - 2011
イベント22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
継続期間: 2011 7 162011 7 22

出版物シリーズ

名前IJCAI International Joint Conference on Artificial Intelligence
ISSN(印刷版)1045-0823

Conference

Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
国/地域Spain
CityBarcelona, Catalonia
Period11/7/1611/7/22

ASJC Scopus subject areas

  • 人工知能

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