Computational studies to understand the role of social learning in team familiarity and its effects on team performance

Vishal Singh*, Andy Dong, John S. Gero

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)


This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

Original languageEnglish
Pages (from-to)25-41
Number of pages17
Issue number1
Publication statusPublished - Mar 2012
MoE publication typeA1 Journal article-refereed


  • agent-based modelling
  • social learning
  • team communication
  • team familiarity
  • team mental models
  • team performance

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