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

Yang Chen, Jiamou Liu, He Zhao, Hongyi Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1807-1809
Number of pages3
ISBN (Electronic)9781450375184
Publication statusPublished - 2020
Event19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand
Duration: 19 May 2020 → …

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period19/05/20 → …

Keywords

  • Multi-agent reinforcement learning
  • Network formation
  • Network structure
  • Relationship building

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