Identity-based learning and segregation in social networks under different institutional environments

Mooweon Rhee, Tohyun Kim

Research output: Contribution to journalArticle

Abstract

To study the evolution of segregation in social networks across systems embedded in different institutional environments, we develop an identity-based learning model where segregation is stochastically conditioned by the initial distribution of the actor’s attention to identity and the updating of this distribution over time. The updating process, which we call the process of mutual learning multiplier, is based on an actor’s success and failure experiences in tying with the same-subgroup and cross-subgroup actors. Results from a Monte Carlo simulation of the model show that the mutual learning multiplier produces disproportional relationships between the initial distribution of identity attention and the level of segregation in social networks. We also find that those relationships are affected by the actors’ attention to structural holes, rate of learning from experience, system size, and the identity heterogeneity of the system. Overall, the model provides insights into various dynamics of network structuration across time and space.

Original languageEnglish
Pages (from-to)339-368
Number of pages30
JournalComputational and Mathematical Organization Theory
Volume20
Issue number4
DOIs
Publication statusPublished - 2014 Dec 1

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All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)
  • Modelling and Simulation
  • Computational Mathematics
  • Applied Mathematics

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