A model of norm emergence and innovation in language change

Samarth Swarup, Andrea Apolloni, Zsuzsanna Fagyal

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

Abstract

We analyze and extend a recently proposed model of linguistic diffusion in social networks, to analytically derive time to convergence, and to account for the innovation phase of lexical dynamics in networks. Our new model, the degree-biased voter model with innovation, shows that the probability of existence of a norm is inversely related to innovation probability. When the innovation rate in the population is low, variants that become norms are due to a peripheral member with high probability. As the innovation rate increases, the fraction of time that the norm is a peripheral-introduced variant and the total time for which a norm exists at all in the population decrease. These results align with his-torical observations of rapid increase and generalization of slang words, technical terms, and new common expressions at times of cultural change in some languages.

Original languageEnglish (US)
Title of host publication10th International Conference on Autonomous Agents and Multiagent Systems
Place of PublicationTaipei
Pages649-656
Number of pages8
Volume1
StatePublished - 2011
Event10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China
Duration: May 2 2011May 6 2011

Other

Other10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
CountryTaiwan, Province of China
CityTaipei
Period5/2/115/6/11

Keywords

  • Degree-biased voter model
  • Lexical innovation
  • Norms
  • Social simulation

ASJC Scopus subject areas

  • Artificial Intelligence

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