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Social structure emergence: A multi-agent reinforcement learning framework for relationship building

  • The University of Auckland
  • Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Social structures naturally arise from social networks, yet no model well interprets the emergence of structural properties in a unified dimension. Here, we unify explanations for the emergence of network structures by revealing the pivotal role of social capital, i.e., benefits that a society grants to individuals, in network formation. We propose a game-based framework social capital games that mathematically conceptualizes social capital. Through this framework, individuals are regarded as independent learning agents that aim to gain social capital via building interpersonal ties. We adopt multi-agent reinforcement learning (MARL) to train agents. By varying configurations of the game, we observe the emergence of classical structures of community, small-world, and core-periphery.

源语言英语
主期刊名Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
编辑Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar
出版商International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
页1807-1809
页数3
ISBN(电子版)9781450375184
出版状态已出版 - 2020
活动19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, 新西兰
期限: 19 5月 2020 → …

丛书

姓名Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
卷2020-May
ISSN(印刷版)1548-8403
ISSN(电子版)1558-2914

会议

会议19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
国家/地区新西兰
市Virtual, Auckland
时期19/05/20 → …

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