Norm emergence via influential weight propagation in complex networks

Ryosuke Shibusawa, Toshiharu Sugawara

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

10 Citations (Scopus)


We propose an influence-based aggregative learning framework that facilitates the emergence of social norms in complex networks and investigate how a norm converges by learning through iterated local interactions in a coordination game. In society, humans decide to coordinate their behavior not only by exchanging information but also on the basis of norms that are often individually derived from interactions without a centralized authority. Coordination using norms has received much attention in studies of multi-agent systems. In addition, because agents often work as delegates of humans, they should have 'mental' models about how to interact with others and incorporate differences of opinion. Because norms make sense only when all or most agents have the same one and they can expect that others will follow, it is important to investigate the mechanism of norm emergence through learning with local and individual interactions in agent society. Our method of norm learning borrows from the opinion aggregation process while taking into account the influence of local opinions in tightly coordinated human communities. We conducted experiments showing how our learning framework facilitates propagation of norms in a number of complex agent networks.

Original languageEnglish
Title of host publicationProceedings - 2014 European Network Intelligence Conference, ENIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479969142
Publication statusPublished - 2014 Dec 12
Event1st European Network Intelligence Conference, ENIC 2014 - Wroclaw, Poland
Duration: 2014 Sept 292014 Sept 30

Publication series

NameProceedings - 2014 European Network Intelligence Conference, ENIC 2014


Conference1st European Network Intelligence Conference, ENIC 2014


  • Complex Network
  • Influence
  • Multi Agent System
  • Norm
  • Reinforcement learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Information Systems


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